Research Anthology on Cross-Industry Challenges of Industry 4.0 [1 ed.] 9781799885481, 9781799886075

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Research Anthology on Cross-Industry Challenges of Industry 4.0 Information Resources Management Association USA

Published in the United States of America by IGI Global Business Science Reference (an imprint of IGI Global) 701 E. Chocolate Avenue Hershey PA, USA 17033 Tel: 717-533-8845 Fax: 717-533-8661 E-mail: [email protected] Web site: http://www.igi-global.com Copyright © 2021 by IGI Global. All rights reserved. No part of this publication may be reproduced, stored or distributed in any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher. Product or company names used in this set are for identification purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark. Library of Congress Cataloging-in-Publication Data Names: Information Resources Management Association, editor. Title: Research anthology on cross-industry challenges of industry 4.0 / Information Resources Management Association, editor. Description: Hershey PA : Business Science Reference, [2021] | Includes bibliographical references and index. | Summary: “This book offers a variety of contributed chapters that explore the challenges that have risen as multidisciplinary industries adapt to the Fourth Industrial Revolution, offering a cross-industry view of these challenges, the impacts they have, potential solutions, and the technological advances that have brought about these new issues”-- Provided by publisher. Identifiers: LCCN 2021006758 (print) | LCCN 2021006759 (ebook) | ISBN 9781799885481 (hardcover) | ISBN 9781799886075 (ebook) Subjects: LCSH: Industry 4.0. | Business planning. | Industrial management. Classification: LCC T59.6 .R47 2021 (print) | LCC T59.6 (ebook) | DDC 658.4/038028563--dc23 LC record available at https://lccn.loc.gov/2021006758 LC ebook record available at https://lccn.loc.gov/2021006759 British Cataloguing in Publication Data A Cataloguing in Publication record for this book is available from the British Library. The views expressed in this book are those of the authors, but not necessarily of the publisher. For electronic access to this publication, please contact: [email protected].

Editor-in-Chief Mehdi Khosrow-Pour, DBA Information Resources Management Association, USA

Associate Editors Steve Clarke, University of Hull, UK Murray E. Jennex, San Diego State University, USA Ari-Veikko Anttiroiko, University of Tampere, Finland

Editorial Advisory Board Sherif Kamel, American University in Cairo, Egypt In Lee, Western Illinois University, USA Jerzy Kisielnicki, Warsaw University, Poland Amar Gupta, Arizona University, USA Craig van Slyke, University of Central Florida, USA John Wang, Montclair State University, USA Vishanth Weerakkody, Brunel University, UK



List of Contributors

Abbas, Yawar / University of Twente, The Netherlands.................................................................... 480 Abd Halim, Siti Noorhaslina / Universiti Teknologi MARA, Malaysia........................................... 1721 Abd Halim, Siti Noorjannah / Universiti Sains Malaysia, Malaysia.............................................. 1721 Abd Hamid, Mohd Syaiful Rizal / Universiti Teknikal Malaysia Melaka, Malaysia....................... 792 Abdulaziz Alhumaidan, Abdullah / Universiti Sains Malaysia, Malaysia.................................... 1077 Adkins, Joan / Colorado Technical University, USA....................................................................... 1529 Agrawal, Anand M. / GLA University, Mathura, India................................................................... 1312 Agrawal, Anirudh / Copenhagen Business School, Denmark........................................................... 245 Agrawal, Vivek / GLA University, Mathura, India.......................................................................... 1312 Ahmad, Noor Hazlina / Universiti Sains Malaysia, Malaysia..................................... 1057, 1077, 1959 Ak, Umut / Istanbul Technical University, Turkey............................................................................. 755 Akkaya, Bülent / Manisa Celal Bayar University, Turkey.............................................................. 1489 Aktan, Mehmet / Necmettin Erbakan University, Turkey................................................................... 21 Alarcón, Faustino / Universitat Politècnica de València, Spain..................................................... 1036 Almeida, Marisa / Technological Centre for Ceramics and Glass (CTCV), Portugal.................... 1353 Aquilani, Barbara / University of Tuscia, Italy................................................................................. 513 Arbelaiz, Ander / Vicomtech, Spain.................................................................................................. 586 Arıcıoğlu, Mustafa Atilla / Necmettin Erbakan University, Turkey................................................. 1548 Armellini, Fabiano / Polytechnique Montréal, Canada.................................................................... 895 Aşçı, Mehmet Saim / İstanbul Medipol University, Turkey.............................................................. 1178 Aslan, Emre / Tokat Gaziosmanpasa University, Turkey................................................................ 1015 Axel Nielsen, Peter / Aalborg University, Denmark.......................................................................... 955 Aydin, Mehmet N. / Kadir Has University, Turkey............................................................................ 303 Bagnoli, Carlo / Università Ca’ Foscari, Venice, Italy........................................................................ 37 Bahar, Mehmet / Cappadocia University, Turkey........................................................................... 1422 Baptista, Antonio J / Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Portugal....................................................................................................................... 411 Baptistella, Márcia Maria Teresa / University Center Toledo Araçatuba, Brazil.......................... 1711 Barata, João / Technological Centre for Ceramics and Glass (CTCV), Portugal & University of Coimbra, Portugal & Polytechnic Institute of Coimbra, Portugal & Miguel Torga Institute (SMT), Portugal........................................................................................................................... 1353 Bastos, Luiz Eduardo Marques / Braslift - Brasil Eletromecânica, Brazil...................................... 941 Beaudry, Catherine / Polytechnique Montréal, Canada................................................................... 895 Becker, Juan M. Jauregui / University of Twente, The Netherlands............................................... 1219 Bedi, Monica / Panjab University, Chandigarh, India...................................................................... 192  



Berus, Lucijano / University of Maribor, Slovenia............................................................................ 219 Bhandari, Krishna Raj / University of Vaasa, Finland..................................................................... 839 Bilbao, Andoni / Gaindu, Spain........................................................................................................ 586 Bolton, Anthony / University of South Africa, South Africa........................................................... 1936 Boon Cheong, Chew / Universiti Teknikal Malaysia Melaka, Malaysia........................................... 792 Boza, Andrés / Universitat Politècnica de València, Spain............................................................. 1036 Brun, Alessandro / Politecnico di Milano, Italy............................................................................... 895 Büchi, Giacomo / University of Turin, Italy......................................................................................... 97 Buchmeister, Borut / Faculty of Mechanical Engineering, University of Maribor, Slovenia........... 461 Budak, Aysenur / Gebze Technical University, Turkey..................................................................... 877 Burgess, Carl / University of North Texas at Dallas, USA.............................................................. 1529 Cadieux, Jean / Université de Sherbrooke (UdeS), Canada............................................................ 1915 Cafer, Eliz / Istanbul Technical University, Turkey............................................................................ 755 Canella, Irene Alonso / CTIC Technology Centre, Spain................................................................ 1877 Cardeal, Gonçalo / Instituto de Soldadura e Qualidade, Portugal................................................... 411 Carolina da Silva, Rafaela / Sao Paulo State University (UNESP), Brazil.................................... 1915 Castagnoli, Rebecca / University of Turin, Italy................................................................................. 97 Cebeci, Ufuk / Istanbul Technical University, Turkey........................................................................ 322 Chauhan, Chetna / Indian Institute of Management, Rohtak, India............................................... 1737 Contuzzi, Nicola / Dyrecta Lab srl, Italy........................................................................................... 634 Costa, Nélson / University of Minho, Portugal................................................................................ 1767 Cresnar, Rok / University of Maribor, Slovenia.............................................................................. 1632 Cuevas Zuñiga, Ingrid Yadibel / Instituto Politécnico Nacional, Mexico........................................ 503 Cugno, Monica / University of Turin, Italy.......................................................................................... 97 Dal Mas, Francesca / Università degli Studi di Roma La Sapienza, Rome, Italy................................ 37 Dašić, Predrag V. / High Technical Mechanical School, Trstenik, Serbia....................................... 1816 Dhamija, Pavitra / Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa.......................................................................... 192, 436 Di Martino, Beniamino / Università della Campania Luigi Vanvitelli, Italy................................. 1397 Dilan, Ebru / Kadir Has University, Turkey...................................................................................... 303 Duarte, Susana / Department of Mechanical and Industrial Engineering (UNIDEMI), NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica, Portugal............ 818 Echeverría Ezponda, Javier / Ikerbasque, Spain........................................................................... 1788 Emejom, Alexius A. / University of the People, USA....................................................................... 1529 Espinosa, Edgar Oliver Cardoso / Instituto Politécnico Nacional, Mexico.................................... 1686 Esposito, Antonio / Università della Campania Luigi Vanvitelli, Italy........................................... 1397 Estrela, Marco / Instituto de Soldadura e Qualidade, Portugal........................................................ 411 Etxegoien, Zelmar / Gaindu, Spain................................................................................................... 586 Eusébio, Celeste / University of Aveiro, Portugal............................................................................ 1444 Evans, Steve / University of Cambridge, UK..................................................................................... 411 Famurewa, Stephen Mayowa / Luleå University of Technology, Sweden........................................... 76 Fang, Teoh Ming / Universiti Sains Malaysia, Malaysia................................................................. 1252 Fernández, Lucía / CTIC Technology Centre, Spain....................................................................... 1877 Fero, Martin / Slovak University of Technology in Bratislava, Slovakia......................................... 1089 Ferrera, Enrico / Istituto Superiore Mario Boella, Italy................................................................... 411 Fialkowska-Filipek, Malgorzata / Lean Enterprise Institute Poland, Poland................................. 411



Ficko, Mirko / University of Maribor, Slovenia................................................................................. 219 Fidlerová, Helena / Slovak University of Technology, Slovakia...................................................... 1089 Fırat, Seniye Ümit Oktay / Marmara University, Turkey.................................................................. 171 Flores, María del Rocío Soto / Instituto Politécnico Nacional, Mexico............................................ 503 Franco, Javier / Vicomtech, Spain..................................................................................................... 586 Frunt, Lex / The Netherlands Railways, The Netherlands................................................................. 480 Funke, Thomas / TechQuartier, Germany......................................................................................... 245 Gajšek, Brigita / Faculty of Logistics, University of Maribor, Slovenia......................................... 1333 Galiano, Angelo / Dyrecta Lab srl, Italy........................................................................................... 634 Gallego, Iván / CTIC Technology Centre, Spain............................................................................. 1877 García, Ander / Vicomtech, Spain..................................................................................................... 586 García, J. Roberto Reyes / University of Twente, The Netherlands................................................. 1219 García, Rosa María Rivas / Instituto Politécnico Nacional, Mexico............................................... 1699 Gatti, Corrado / Sapienza University of Rome, Italy........................................................................ 513 Goksoy, Asli / American University in Bulgaria, Bulgaria................................................................ 615 Goldman, Geoff A. / University of Johannesburg, South Africa...................................................... 1592 Gómez-Gasquet, Pedro / Universitat Politècnica de València, Spain............................................ 1036 Goosen, Leilani / University of South Africa, South Africa............................................................. 1936 Gültekin Kutlu, Zelal / İnonu Universty, Turkey.............................................................................. 152 Gupta, M.L. / PEC University of Technology, Chandigarh, India.................................................... 192 Holgado, Maria / University of Cambridge, UK............................................................................... 411 Hopali, Egemen / Üsküdar University, Turkey................................................................................ 1113 Hovest, Gunnar Große / ATB - Institut für angewandte Systemtechnik Bremen GmbH, Germany........................................................................................................................................ 411 Husak, Ermin / University of Bihac, Bosnia and Herzegovina......................................................... 656 Ibaseta, Daniel / CTIC Technology Centre, Spain........................................................................... 1877 Iileka, Helvi / Namibia Energy Institute, Namibia........................................................................... 1570 Iqbal, Qaisar / Universiti Sains Malaysia, Malaysia................................................... 1057, 1959, 1968 Isa, Saifuddin / Universiti Teknikal Malaysia Melaka, Malaysia...................................................... 792 İşcan, Erhan / Cukurova University, Turkey.................................................................................... 1148 Janardhanan, Mukund Nilakantan / University of Leicester, UK................................................... 955 Janssens, Jurgen / asUgo Consulting, Belgium................................................................................ 853 Jinoop, Arackal N. / Raja Ramanna Centre for Advanced Technology, India & Homi Bhabha National Institute, Mumbai, India................................................................................................. 729 Kalitanyi, Vivence / University of Johannesburg, South Africa...................................................... 1592 Karabegović, Edina / University of Bihać, Bosnia and Herzegovina............................................... 656 Karabegović, Isak / Academy of Sciences and Arts of Bosnia and Herzegovina, Bosnia and Herzegovina & University of Bihac, Bosnia and Herzegovina..................................................... 656 Kargas, Antonios / National and Kapodistrian University of Athens, Greece................................ 1379 Klančnik, Simon / University of Maribor, Slovenia.......................................................................... 219 Kour, Ravdeep / Luleå University of Technology, Sweden.............................................................. 1836 Kramberger, Tomaž / Faculty of Logistics, University of Maribor, Slovenia................................. 1333 Kritzinger, Elmarie / University of South Africa, South Africa...................................................... 1936 Kukshal, Vikas / National Institute of Technology Uttarakhand, India............................................ 495 Kumar, Arun / Indian Institute of Technology, Delhi, India............................................................. 709 Kumar, Ravinder / Amity University, Noida, India.............................................................. 1244, 1807



Lezak, Emil / IZNAB Sp. z o.o., Poland............................................................................................. 411 Lourenço, Emanuel J. / Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Portugal....................................................................................................................... 411 Lovrec, Darko / Faculty of Mechanical Engineering, University of Maribor, Slovenia.................... 686 Loy, Jennifer / University of Technology Sydney, Australia............................................................ 1666 Mahmic, Mehmed / University of Bihac, Bosnia and Herzegovina.................................................. 656 Mangas, Andrés G. / CTIC Technology Centre, Spain.................................................................... 1877 Marnewick, Annlizé / University of Johannesburg, South Africa................................................... 1508 Marnewick, Carl / University of Johannesburg, South Africa........................................................ 1508 Martinetti, Alberto / University of Twente, The Netherlands................................................. 480, 1219 Martinez, Felipe / Independent Researcher, Czech Republic.............................................................. 53 Masluszczak, Zofia / Lean Enterprise Institute Poland, Poland....................................................... 411 Massaro, Alessandro / Dyrecta Lab srl, Italy................................................................................... 634 Massaro, Maurizio / Università Ca’ Foscari, Venice, Italy................................................................ 37 Matt, Dominik T. / Free University of Bolzano, Italy........................................................................ 275 Mazini, Sergio Ricardo / University Center Toledo Araçatuba, Brazil........................................... 1711 Mezgár, István / Institute of Computer Science and Control of the Hungarian Academy of Sciences, Hungary......................................................................................................................... 540 Mirarchi, Claudio / Politecnico di Milano, Italy............................................................................ 1397 Mohanty, R. P. / SOA University, Bhubaneswar, India.................................................................... 1312 Mosconi, Elaine / Université de Sherbrooke (UdeS), Canada......................................................... 1915 Mukomana, John / University of KwaZulu-Natal, South Africa..................................................... 1978 Müller, Julian M. / Salzburg University of Applied Sciences, Austria............................................... 131 Munsamy, Megashnee / Mangosuthu University of Technology, Umlazi, South Africa................... 436 Muqaddis, Muhammad Mustafa / PriceWaterhouseCoopers (PwC), Canada.............................. 1057 Musti, K. S. Sastry / Namibia University of Science and Technology, Namibia.............................. 1570 Muthuveloo, Rajendran / Universiti Sains Malaysia, Malaysia.................................................... 1252 N., Jayapandian / Faculty of Engineering, Christ University, Bangalore, India............................ 1853 Nasim, Adeel / PricewaterhouseCoopers (PwC), Canada.............................................................. 1057 Nayak, Saurav K. / Raja Ramanna Centre for Advanced Technology, India & Homi Bhabha National Institute, Mumbai, India................................................................................................. 729 Ng, Hee Song / KDU Penang University College, Malaysia.................................................................. 1 Novak, James I. / University of Technology Sydney, Australia........................................................ 1666 Novotná, Ivana / Slovak University of Technology in Bratislava, Slovakia..................................... 1089 Ofluoglu, Gokhan / Bulent Ecevit University, Turkey..................................................................... 1164 Ojsteršek, Robert / Faculty of Mechanical Engineering, University of Maribor, Slovenia.............. 461 Oner, Mahir / Istanbul Technical University, Turkey........................................................................ 568 Oner, Sultan Ceren / Istanbul Technical University, Turkey.............................................................. 568 Onyina, Paul Adjei / Pentecost University College, Ghana............................................................. 1613 Oregui, Xabier / Vicomtech, Spain.................................................................................................... 586 Ottonicar, Selma Leticia Capinzaiki / Sao Paulo State University (UNESP), Brazil..................... 1915 Ozturk, Hande Mutlu / Pamukkale University, Turkey................................................................... 1464 Ozturk, Mahmut Sami / Suleyman Demirel University, Turkey........................................................ 999 Padayachee, Indira / University of KwaZulu-Natal, South Africa.................................................. 1978 Palčič, Iztok / University of Maribor, Slovenia.......................................................................... 219, 461 Palupi, Majang / Universitas Islam Indonesia, Indonesia.............................................................. 1057



Pandey, Pulak Mohan / Indian Institute of Technology, Delhi, India................................................ 709 Patel, Dharmendra Trikamlal / Charotar University of Science and Technology, India............... 1647 Patnaik, Amar / Malaviya National Institute of Technology, Jaipur, India...................................... 495 Paul, Alini C. / Nitte Meenakshi Institute of Technology, India......................................................... 729 Paul, Christ P. / Raja Ramanna Centre for Advanced Technology, India & Homi Bhabha National Institute, Mumbai, India................................................................................................. 729 Pavan, Alberto / Politecnico di Milano, Italy.................................................................................. 1397 Pedone, Gianfranco / Institute of Computer Science and Control of the Hungarian Academy of Sciences, Hungary......................................................................................................................... 540 Pepper, Donna / Benedictine University, USA................................................................................. 1529 Perez, David / Universitat Politècnica de València, Spain.............................................................. 1036 Piccarozzi, Michela / University of Tuscia, Italy............................................................................... 513 Pinto da Costa, Susana / University of Minho, Portugal................................................................ 1767 Ponnambalam, S. G. / University Malaysia Pahang, Malaysia......................................................... 926 Popkhadze, Giorgi / Georgian Technical University (GTU), Georgia............................................ 1816 Porubčinová, Martina / Slovak Academy of Sciences, Slovakia..................................................... 1089 Pradeep J. / Rajalakshmi Engineering College, India........................................................................ 926 Pučko, Zoran / Faculty of Civil Engineering, Transportation Engineering, and Architecture, University of Maribor, Slovenia.................................................................................................. 1277 Radić, Ivana / Faculty of Logistics, University of Maribor, Slovenia.............................................. 1333 Rahman, Humyun Fuad / University of New South Wales, Australia............................................... 955 Rahul K. / Rajalakshmi Engineering College, India.......................................................................... 926 Rajabalinejad, Mohammad / University of Twente, The Netherlands............................................. 480 Ramìrez, Ericka Molina / Instituto Politécnico Nacional, Mexico................................................... 503 Rato, Ricardo / Instituto de Soldadura e Qualidade, Portugal......................................................... 411 Rauch, Erwin / Free University of Bolzano, Italy............................................................................. 275 Retamar, Ángel / CTIC Technology Centre, Spain.......................................................................... 1877 Riedl, Michael / Fraunhofer Italia Research, Italy............................................................................ 275 Rosário Cabrita, Maria do / Department of Mechanical and Industrial Engineering (UNIDEMI), NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica, Portugal........................................................................................................................ 818 Rossini, Rosaria / Istituto Superiore Mario Boella, Italy.................................................................. 411 Ruiz, Jésica Alhelí Cortés / Instituto Politécnico Nacional, Mexico............................................... 1699 Rupnik, Bojan / Faculty of Logistics, University of Maribor, Slovenia.......................................... 1333 Saiz-Alvarez, José Manuel / EGADE Business School, Tecnologico de Monterrey, Mexico............ 380 Sankaranarayanan, Bathrinath / Kalasalingam University, India.................................................. 926 Saravanasankar S. / Kalasalingam University, India........................................................................ 926 Sari, Irem Ucal / Istanbul Technical University, Turkey.................................................................... 755 Schaefer, Sebastian / TechQuartier, Germany.................................................................................. 245 Schneider, Alexander / Fraunhofer FIT, Germany........................................................................... 411 Scremin, Luca / Politecnico di Milano, Italy..................................................................................... 895 Sezen, Bülent / Gebze Technical University, Turkey.......................................................................... 113 Shi, Hu / Universiti Sains Malaysia, Malaysia................................................................................ 1968 Shidhika, Fenni / Namibia Energy Institute, Namibia.................................................................... 1570 Silva, Francisco / Technological Centre for Ceramics and Glass (CTCV), Portugal & University of Minho, Portugal...................................................................................................................... 1353



Silvestri, Cecilia / University of Tuscia, Italy..................................................................................... 513 Simões, Bruno / Vicomtech, Spain..................................................................................................... 586 Singh, Amol / Indian Institute of Management, Rohtak, India........................................................ 1737 Singh, Gurminder / SIMAP Lab, Université Grenoble Alpes, France.............................................. 709 Singh, Ravinder Pal / Indian Institute of Technology, Delhi, India................................................... 709 Singh, Sarbjeet / Luleå University of Technology, Sweden............................................. 398, 495, 1219 Šinko, Simona / Faculty of Logistics, University of Maribor, Slovenia........................................... 1333 Solar-Pelletier, Laurence / Polytechnique Montréal, Canada.......................................................... 895 Šuman, Nataša / Faculty of Civil Engineering, Transportation Engineering, and Architecture, University of Maribor, Slovenia.................................................................................................. 1277 Tabaklar, Tunca / Izmir University of Economics, Turkey................................................................ 336 Tabarés Gutiérrez, Raúl / Fundación Tecnalia Research and Innovation, Spain.......................... 1788 Tarifa-Fernández, Jorge / University of Almeria, Spain................................................................ 1129 Teixeira, Leonor / University of Aveiro, Portugal........................................................................... 1444 Teixeira, Pedro / University of Aveiro, Portugal.............................................................................. 1444 Telukdarie, Arnesh / Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa.................................................................................. 436 Thaduri, Adithya / Luleå University of Technology, Sweden.............................................................. 76 Thayananthan, Vijey / King Abdulaziz University, Saudi Arabia.................................................... 772 Tič, Vito / Faculty of Mechanical Engineering, University of Maribor, Slovenia............................. 686 Tjahjono, Heru Kurnianto / Universitas Muhammadiyah Yogyakarta, Indonesia......................... 1057 Topcu, Mustafa Kemal / ST Strategy and Technology Development LLC, Turkey............................ 359 Topsakal, Yunus / Adana Alparslan Türkes Science and Technology University, Turkey.............. 1422 Tretten, Phillip / Luleå University of Technology, Sweden............................................................... 398 Turmanidze, Raul / Georgian Technical University (GTU), Georgia............................................ 1816 Ulusoy, Berna / Hochschule Fresenius University of Applied Sciences, Germany............................ 260 Ulusoy, Tuba / Necmettin Erbakan University, Turkey........................................................................ 21 Uslu, Banu Çalış / Marmara University, Turkey................................................................................ 171 Ustundag, Alp / Industrial Engineering Department, Istanbul Technical University, Turkey.......... 877 van Dongen, Leo A. M. / University of Twente, The Netherlands............................................ 480, 1219 Vardarlier, Pelin / Istanbul Medipol University, Turkey................................................................. 1202 Varoutas, Dimitrios / National and Kapodistrian University of Athens, Greece............................ 1379 Vayvay, Özalp / Marmara University, Turkey................................................................................. 1113 Wei, Lee Heng / UOW Malaysia KDU Penang University, Malaysia.............................................. 1252 Yadav, Seemant Kumar / Institute of Business Management, GLA University, Mathura, India.... 1312 Yasar, Esra / KTO Karatay University, Turkey.................................................................................... 21 Yazdani, Javad / University of Central Lancashire, UK................................................................... 772 Yiğitol, Büşra / Konya Food and Agriculture University, Turkey.................................................... 1548 Yildirim, Cansu / Dokuz Eylul University, Turkey............................................................................ 336 Yıldırım, İsmail / Hittite University, Turkey...................................................................................... 983 Yildiz Çankaya, Sibel / Bolu Abant Izzet Baysal University, Turkey................................................ 113 Yüzbaşıoğlu, Nedim / Akdeniz University, Turkey.......................................................................... 1422 Zafer, Cem / Presidential Office of the Republic of Turkey, Turkey................................................ 1202

Table of Contents

Preface................................................................................................................................................. xxv

Volume I Section 1 Fundamental Concepts and Theories Chapter 1 Opportunities, Challenges, and Solutions for Industry 4.0...................................................................... 1 Hee Song Ng, KDU Penang University College, Malaysia Chapter 2 Impact of Industry 4.0 Revolution on Science, Technology, and Society (STS): Challenges and Opportunities in the Industry 4.0 Era.................................................................................................... 21 Tuba Ulusoy, Necmettin Erbakan University, Turkey Esra Yasar, KTO Karatay University, Turkey Mehmet Aktan, Necmettin Erbakan University, Turkey Chapter 3 The 4th Industrial Revolution: Business Models and Evidence From the Field................................... 37 Carlo Bagnoli, Università Ca’ Foscari, Venice, Italy Francesca Dal Mas, Università degli Studi di Roma La Sapienza, Rome, Italy Maurizio Massaro, Università Ca’ Foscari, Venice, Italy Chapter 4 Process Excellence and Industry 4.0...................................................................................................... 53 Felipe Martinez, Independent Researcher, Czech Republic Chapter 5 Evolution of Maintenance Processes in Industry 4.0............................................................................. 76 Adithya Thaduri, Luleå University of Technology, Sweden Stephen Mayowa Famurewa, Luleå University of Technology, Sweden

 



Chapter 6 How Industry 4.0 Changes the Value Co-Creation Process................................................................... 97 Rebecca Castagnoli, University of Turin, Italy Giacomo Büchi, University of Turin, Italy Monica Cugno, University of Turin, Italy Chapter 7 Industry 4.0 and Sustainability............................................................................................................ 113 Sibel Yildiz Çankaya, Bolu Abant Izzet Baysal University, Turkey Bülent Sezen, Gebze Technical University, Turkey Chapter 8 Industry 4.0 in the Context of the Triple Bottom Line of Sustainability: A Systematic Literature Review................................................................................................................................................. 131 Julian M. Müller, Salzburg University of Applied Sciences, Austria Chapter 9 Industry 4.0 and the Internet of Things (IoT)...................................................................................... 152 Zelal Gültekin Kutlu, İnonu Universty, Turkey Chapter 10 A Comprehensive Study on Internet of Things Based on Key Artificial Intelligence Technologies and Industry 4.0................................................................................................................................... 171 Banu Çalış Uslu, Marmara University, Turkey Seniye Ümit Oktay Fırat, Marmara University, Turkey Chapter 11 Industry 4.0 and Supply Chain Management: A Methodological Review.......................................... 192 Pavitra Dhamija, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa Monica Bedi, Panjab University, Chandigarh, India M.L. Gupta, PEC University of Technology, Chandigarh, India Section 2 Development and Design Methodologies Chapter 12 Design of Facility Layout for Industry 4.0........................................................................................... 219 Mirko Ficko, University of Maribor, Slovenia Lucijano Berus, University of Maribor, Slovenia Iztok Palčič, University of Maribor, Slovenia Simon Klančnik, University of Maribor, Slovenia



Chapter 13 Incorporating Industry 4.0 in Corporate Strategy................................................................................ 245 Anirudh Agrawal, Copenhagen Business School, Denmark Sebastian Schaefer, TechQuartier, Germany Thomas Funke, TechQuartier, Germany Chapter 14 Understanding Digital Congruence in Industry 4.0............................................................................. 260 Berna Ulusoy, Hochschule Fresenius University of Applied Sciences, Germany Chapter 15 Knowledge Transfer and Introduction of Industry 4.0 in SMEs: A Five-Step Methodology to Introduce Industry 4.0.......................................................................................................................... 275 Dominik T. Matt, Free University of Bolzano, Italy Erwin Rauch, Free University of Bolzano, Italy Michael Riedl, Fraunhofer Italia Research, Italy Chapter 16 Adoption of Design Thinking in Industry 4.0 Project Management................................................... 303 Ebru Dilan, Kadir Has University, Turkey Mehmet N. Aydin, Kadir Has University, Turkey Chapter 17 The Project Management of Industry 4.0 Strategy for Software Houses............................................ 322 Ufuk Cebeci, Istanbul Technical University, Turkey Chapter 18 The Development of Servitization Concept in the Era of Industry 4.0 Through SCM Perspective.... 336 Tunca Tabaklar, Izmir University of Economics, Turkey Cansu Yildirim, Dokuz Eylul University, Turkey Chapter 19 Competency Framework for the Fourth Industrial Revolution............................................................ 359 Mustafa Kemal Topcu, ST Strategy and Technology Development LLC, Turkey Chapter 20 Managing Social Innovation Through CSR 2.0 and the Quadruple Helix: A Socially Inclusive Business Strategy for the Industry 4.0................................................................................................. 380 José Manuel Saiz-Alvarez, EGADE Business School, Tecnologico de Monterrey, Mexico Chapter 21 Operator 4.0 Within the Framework of Industry 4.0........................................................................... 398 Sarbjeet Singh, Luleå University of Technology, Sweden Phillip Tretten, Luleå University of Technology, Sweden



Chapter 22 Towards Industry 4.0: Efficient and Sustainable Manufacturing Leveraging MTEF – MTEFMAESTRI Total Efficiency Framework.............................................................................................. 411 Emil Lezak, IZNAB Sp. z o.o., Poland Enrico Ferrera, Istituto Superiore Mario Boella, Italy Rosaria Rossini, Istituto Superiore Mario Boella, Italy Zofia Masluszczak, Lean Enterprise Institute Poland, Poland Malgorzata Fialkowska-Filipek, Lean Enterprise Institute Poland, Poland Gunnar Große Hovest, ATB - Institut für angewandte Systemtechnik Bremen GmbH, Germany Alexander Schneider, Fraunhofer FIT, Germany Emanuel J. Lourenço, Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Portugal Antonio J Baptista, Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Portugal Gonçalo Cardeal, Instituto de Soldadura e Qualidade, Portugal Marco Estrela, Instituto de Soldadura e Qualidade, Portugal Ricardo Rato, Instituto de Soldadura e Qualidade, Portugal Maria Holgado, University of Cambridge, UK Steve Evans, University of Cambridge, UK Chapter 23 Logistics 4.0 Energy Modelling........................................................................................................... 436 Megashnee Munsamy, Mangosuthu University of Technology, Umlazi, South Africa Arnesh Telukdarie, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa Pavitra Dhamija, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa Chapter 24 Applied Simulation Modelling and Evolutionary Computation Methods in Industry 4.0 CPS Architecture.......................................................................................................................................... 461 Robert Ojsteršek, Faculty of Mechanical Engineering, University of Maribor, Slovenia Iztok Palčič, Faculty of Mechanical Engineering, University of Maribor, Slovenia Borut Buchmeister, Faculty of Mechanical Engineering, University of Maribor, Slovenia Chapter 25 Tacit Knowledge Sharing for System Integration: A Case of Netherlands Railways in  Industry 4.0.......................................................................................................................................... 480 Yawar Abbas, University of Twente, The Netherlands Alberto Martinetti, University of Twente, The Netherlands Mohammad Rajabalinejad, University of Twente, The Netherlands Lex Frunt, The Netherlands Railways, The Netherlands Leo A. M. van Dongen, University of Twente, The Netherlands



Volume II Section 3 Tools and Technologies Chapter 26 Augmented Technology for Safety and Maintenance in Industry 4.0................................................. 495 Vikas Kukshal, National Institute of Technology Uttarakhand, India Amar Patnaik, Malaviya National Institute of Technology, Jaipur, India Sarbjeet Singh, Luleå University of Technology, Sweden Chapter 27 Technologies for Sustainability Within the Framework of the Fourth Industrial Revolution............. 503 Ingrid Yadibel Cuevas Zuñiga, Instituto Politécnico Nacional, Mexico María del Rocío Soto Flores, Instituto Politécnico Nacional, Mexico Ericka Molina Ramìrez, Instituto Politécnico Nacional, Mexico Chapter 28 Achieving Environmental Sustainability Through Industry 4.0 Tools: The Case of the “Symbiosis” Digital Platform.............................................................................................................. 513 Barbara Aquilani, University of Tuscia, Italy Michela Piccarozzi, University of Tuscia, Italy Cecilia Silvestri, University of Tuscia, Italy Corrado Gatti, Sapienza University of Rome, Italy Chapter 29 Cloud-Based Manufacturing (CBM) Interoperability in Industry 4.0................................................. 540 István Mezgár, Institute of Computer Science and Control of the Hungarian Academy of Sciences, Hungary Gianfranco Pedone, Institute of Computer Science and Control of the Hungarian Academy of Sciences, Hungary Chapter 30 Data Analytics in Industry 4.0: In the Perspective of Big Data........................................................... 568 Mahir Oner, Istanbul Technical University, Turkey Sultan Ceren Oner, Istanbul Technical University, Turkey Chapter 31 Technologies for Industry 4.0 Data Solutions...................................................................................... 586 Ander García, Vicomtech, Spain Ander Arbelaiz, Vicomtech, Spain Javier Franco, Vicomtech, Spain Xabier Oregui, Vicomtech, Spain Bruno Simões, Vicomtech, Spain Zelmar Etxegoien, Gaindu, Spain Andoni Bilbao, Gaindu, Spain



Chapter 32 Can Internal Social Media and Data Mining Be a Powerful Communication Vehicle in Reaching Employees in Change Management in Industry 4.0?........................................................................... 615 Asli Goksoy, American University in Bulgaria, Bulgaria Chapter 33 Intelligent Processes in Automated Production Involving Industry 4.0 Technologies and Artificial Intelligence........................................................................................................................................... 634 Alessandro Massaro, Dyrecta Lab srl, Italy Nicola Contuzzi, Dyrecta Lab srl, Italy Angelo Galiano, Dyrecta Lab srl, Italy Chapter 34 The Implementation of Industry 4.0 by Using Industrial and Service Robots in the Production Processes.............................................................................................................................................. 656 Isak Karabegović, Academy of Sciences and Arts of Bosnia and Herzegovina, Bosnia and Herzegovina & University of Bihac, Bosnia and Herzegovina Edina Karabegović, University of Bihać, Bosnia and Herzegovina Mehmed Mahmic, University of Bihac, Bosnia and Herzegovina Ermin Husak, University of Bihac, Bosnia and Herzegovina Chapter 35 An Embedded Online Hydraulic Fluid CM and RUL-System for Industry 4.0 Manufacturing Machines.............................................................................................................................................. 686 Vito Tič, Faculty of Mechanical Engineering, University of Maribor, Slovenia Darko Lovrec, Faculty of Mechanical Engineering, University of Maribor, Slovenia Chapter 36 Role of Additive Manufacturing in Industry 4.0 for Maintenance Engineering.................................. 709 Arun Kumar, Indian Institute of Technology, Delhi, India Gurminder Singh, SIMAP Lab, Université Grenoble Alpes, France Ravinder Pal Singh, Indian Institute of Technology, Delhi, India Pulak Mohan Pandey, Indian Institute of Technology, Delhi, India Chapter 37 Laser Additive Manufacturing in Industry 4.0: Overview, Applications, and Scenario in Developing Economies........................................................................................................................ 729 Christ P. Paul, Raja Ramanna Centre for Advanced Technology, India & Homi Bhabha National Institute, Mumbai, India Arackal N. Jinoop, Raja Ramanna Centre for Advanced Technology, India & Homi Bhabha National Institute, Mumbai, India Saurav K. Nayak, Raja Ramanna Centre for Advanced Technology, India & Homi Bhabha National Institute, Mumbai, India Alini C. Paul, Nitte Meenakshi Institute of Technology, India



Chapter 38 Feasibility Analysis of Industry 4.0 Projects and an Application in Automotive Maintenance Systems................................................................................................................................................ 755 Irem Ucal Sari, Istanbul Technical University, Turkey Eliz Cafer, Istanbul Technical University, Turkey Umut Ak, Istanbul Technical University, Turkey Chapter 39 Secure Cyber-Physical Systems for Improving Transportation Facilities in Smart Cities and Industry 4.0.......................................................................................................................................... 772 Vijey Thayananthan, King Abdulaziz University, Saudi Arabia Javad Yazdani, University of Central Lancashire, UK Chapter 40 A Study of Quality Tools and Techniques for Smart Manufacturing in Industry 4.0 in Malaysia: The Case of Northern Corridor Economic Region.............................................................................. 792 Mohd Syaiful Rizal Abd Hamid, Universiti Teknikal Malaysia Melaka, Malaysia Saifuddin Isa, Universiti Teknikal Malaysia Melaka, Malaysia Chew Boon Cheong, Universiti Teknikal Malaysia Melaka, Malaysia Section 4 Utilization and Applications Chapter 41 Addressing Sustainability and Industry 4.0 to the Business Model.................................................... 818 Maria do Rosário Cabrita, Department of Mechanical and Industrial Engineering (UNIDEMI), NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica, Portugal Susana Duarte, Department of Mechanical and Industrial Engineering (UNIDEMI), NOVA School of Science and Technology, Universidade NOVA de Lisboa, Caparica, Portugal Chapter 42 Balancing Exploration and Exploitation Through Customer Development Model: Leveraging Industry 4.0 for Sustainable Performance............................................................................................ 839 Krishna Raj Bhandari, University of Vaasa, Finland Chapter 43 Managing Customer Journeys in a Nimble Way for Industry 4.0........................................................ 853 Jurgen Janssens, asUgo Consulting, Belgium Chapter 44 Technology Project Portfolio Selection in Industry 4.0....................................................................... 877 Aysenur Budak, Gebze Technical University, Turkey Alp Ustundag, Industrial Engineering Department, Istanbul Technical University, Turkey



Chapter 45 Towards a Framework for Assessing the Maturity of Manufacturing Companies in Industry 4.0 Adoption.............................................................................................................................................. 895 Luca Scremin, Politecnico di Milano, Italy Fabiano Armellini, Polytechnique Montréal, Canada Alessandro Brun, Politecnico di Milano, Italy Laurence Solar-Pelletier, Polytechnique Montréal, Canada Catherine Beaudry, Polytechnique Montréal, Canada Chapter 46 Evaluation of Influence of Principles Involved in Industry 4.0 Over Coal Industries Using TISM.... 926 Bathrinath Sankaranarayanan, Kalasalingam University, India Rahul K., Rajalakshmi Engineering College, India Pradeep J., Rajalakshmi Engineering College, India S. G. Ponnambalam, University Malaysia Pahang, Malaysia Saravanasankar S., Kalasalingam University, India Chapter 47 Industry 4.0 in Pumping Applications: Achievements and Trends...................................................... 941 Luiz Eduardo Marques Bastos, Braslift - Brasil Eletromecânica, Brazil Chapter 48 Smart Make-to-Order Production in a Flow Shop Environment for Industry 4.0............................... 955 Humyun Fuad Rahman, University of New South Wales, Australia Mukund Nilakantan Janardhanan, University of Leicester, UK Peter Axel Nielsen, Aalborg University, Denmark Chapter 49 Industry 4.0 and Its Effects on the Insurance Sector............................................................................ 983 İsmail Yıldırım, Hittite University, Turkey

Volume III Chapter 50 Emerging Auditing Perspectives in the Age of the Fourth Industrial Revolution............................... 999 Mahmut Sami Ozturk, Suleyman Demirel University, Turkey Chapter 51 How Supply Chain Management Will Change in the Industry 4.0 Era?........................................... 1015 Emre Aslan, Tokat Gaziosmanpasa University, Turkey Chapter 52 Industry 4.0 From the Supply Chain Perspective: Case Study in the Food Sector............................ 1036 Andrés Boza, Universitat Politècnica de València, Spain Faustino Alarcón, Universitat Politècnica de València, Spain David Perez, Universitat Politècnica de València, Spain Pedro Gómez-Gasquet, Universitat Politècnica de València, Spain



Chapter 53 Enhancing Business Performance of Pakistani Manufacturing Firms via Strategic Agility in the Industry 4.0 Era: The Role of Entrepreneurial Bricolage as Moderator............................................ 1057 Qaisar Iqbal, Universiti Sains Malaysia, Malaysia Noor Hazlina Ahmad, Universiti Sains Malaysia, Malaysia Heru Kurnianto Tjahjono, Universitas Muhammadiyah Yogyakarta, Indonesia Adeel Nasim, PricewaterhouseCoopers (PwC), Canada Muhammad Mustafa Muqaddis, PriceWaterhouseCoopers (PwC), Canada Majang Palupi, Universitas Islam Indonesia, Indonesia Chapter 54 Sustainable Performance of Tunisian SMEs in Industry 4.0............................................................. 1077 Abdullah Abdulaziz Alhumaidan, Universiti Sains Malaysia, Malaysia Noor Hazlina Ahmad, Universiti Sains Malaysia, Malaysia Chapter 55 Identification of Challenges and Opportunities for Work 4.0 Competences Developing in  Slovakia.............................................................................................................................................. 1089 Helena Fidlerová, Slovak University of Technology, Slovakia Martina Porubčinová, Slovak Academy of Sciences, Slovakia Martin Fero, Slovak University of Technology in Bratislava, Slovakia Ivana Novotná, Slovak University of Technology in Bratislava, Slovakia Section 5 Organizational and Social Implications Chapter 56 Industry 4.0 as the Last Industrial Revolution and Its Opportunities for Developing Countries....... 1113 Egemen Hopali, Üsküdar University, Turkey Özalp Vayvay, Marmara University, Turkey Chapter 57 Sustainable Implications of Industry 4.0........................................................................................... 1129 Jorge Tarifa-Fernández, University of Almeria, Spain Chapter 58 Strategies of Sustainable Bioeconomy in the Industry 4.0 Framework for Inclusive and Social Prosperity........................................................................................................................................... 1148 Erhan İşcan, Cukurova University, Turkey Chapter 59 Industry 4.0 and Its Impact on Working Life..................................................................................... 1164 Gokhan Ofluoglu, Bulent Ecevit University, Turkey Chapter 60 The Effects of Industry 4.0 on Labor Force Attributes and New Challenges.................................... 1178 Mehmet Saim Aşçı, İstanbul Medipol University, Turkey



Chapter 61 The Impact of New Technology on Society and Workforce in Production in the Era of Industry 4.0...................................................................................................................................................... 1202 Cem Zafer, Presidential Office of the Republic of Turkey, Turkey Pelin Vardarlier, Istanbul Medipol University, Turkey Chapter 62 Towards an Industry 4.0-Based Maintenance Approach in the Manufacturing Processes................ 1219 J. Roberto Reyes García, University of Twente, The Netherlands Alberto Martinetti, University of Twente, The Netherlands Juan M. Jauregui Becker, University of Twente, The Netherlands Sarbjeet Singh, Luleå University of Technology, Sweden Leo A. M. van Dongen, University of Twente, The Netherlands Chapter 63 Espousal of Industry 4.0 in Indian Manufacturing Organizations: Analysis of Enablers.................. 1244 Ravinder Kumar, Amity University, Noida, India Chapter 64 Innovation Capability for SME Biomass Industry Performance: Perspectives of HRM, OC, KMC in Industry 4.0.................................................................................................................................... 1252 Teoh Ming Fang, Universiti Sains Malaysia, Malaysia Lee Heng Wei, UOW Malaysia KDU Penang University, Malaysia Rajendran Muthuveloo, Universiti Sains Malaysia, Malaysia Chapter 65 Integration of Industry 4.0 for Advanced Construction Project Management................................... 1277 Nataša Šuman, Faculty of Civil Engineering, Transportation Engineering, and Architecture, University of Maribor, Slovenia Zoran Pučko, Faculty of Civil Engineering, Transportation Engineering, and Architecture, University of Maribor, Slovenia Chapter 66 Prioritizing the Enablers of Construction Supply Chain in the Industry 4.0 Environment............... 1312 Vivek Agrawal, GLA University, Mathura, India Seemant Kumar Yadav, Institute of Business Management, GLA University, Mathura, India R. P. Mohanty, SOA University, Bhubaneswar, India Anand M. Agrawal, GLA University, Mathura, India Chapter 67 Redesign of the Workplace for Toolmakers Towards Industry 4.0.................................................... 1333 Ivana Radić, Faculty of Logistics, University of Maribor, Slovenia Bojan Rupnik, Faculty of Logistics, University of Maribor, Slovenia Simona Šinko, Faculty of Logistics, University of Maribor, Slovenia Tomaž Kramberger, Faculty of Logistics, University of Maribor, Slovenia Brigita Gajšek, Faculty of Logistics, University of Maribor, Slovenia



Chapter 68 Ceramic Industry 4.0: Paths of Revolution in Traditional Products.................................................. 1353 João Barata, Technological Centre for Ceramics and Glass (CTCV), Portugal & University of Coimbra, Portugal & Polytechnic Institute of Coimbra, Portugal & Miguel Torga Institute (SMT), Portugal Francisco Silva, Technological Centre for Ceramics and Glass (CTCV), Portugal & University of Minho, Portugal Marisa Almeida, Technological Centre for Ceramics and Glass (CTCV), Portugal Chapter 69 Industry 4.0 in Cultural Industry: A Review on Digital Visualization for VR and AR  Applications....................................................................................................................................... 1379 Antonios Kargas, National and Kapodistrian University of Athens, Greece Dimitrios Varoutas, National and Kapodistrian University of Athens, Greece Chapter 70 Impact of Industry 4.0 in Architecture and Cultural Heritage: Artificial Intelligence and Semantic Web Technologies to Empower Interoperability and Data Usage..................................................... 1397 Claudio Mirarchi, Politecnico di Milano, Italy Alberto Pavan, Politecnico di Milano, Italy Beniamino Di Martino, Università della Campania Luigi Vanvitelli, Italy Antonio Esposito, Università della Campania Luigi Vanvitelli, Italy Chapter 71 The Future of Tourism Guidance in the Scope of Industry 4.0 and Next-Generation  Technologies...................................................................................................................................... 1422 Yunus Topsakal, Adana Alparslan Türkes Science and Technology University, Turkey Mehmet Bahar, Cappadocia University, Turkey Nedim Yüzbaşıoğlu, Akdeniz University, Turkey Chapter 72 Accessible@tourism 4.0: An Exploratory Approach to the Role of Industry 4.0 in Accessible Tourism.............................................................................................................................................. 1444 Pedro Teixeira, University of Aveiro, Portugal Leonor Teixeira, University of Aveiro, Portugal Celeste Eusébio, University of Aveiro, Portugal Chapter 73 Technological Developments: Industry 4.0 and Its Effect on the Tourism Sector............................ 1464 Hande Mutlu Ozturk, Pamukkale University, Turkey



Volume IV Section 6 Managerial Impact Chapter 74 Leadership 5.0 in Industry 4.0: Leadership in Perspective of Organizational Agility....................... 1489 Bülent Akkaya, Manisa Celal Bayar University, Turkey Chapter 75 Insights Into Managing Project Teams for Industry 4.0.................................................................... 1508 Carl Marnewick, University of Johannesburg, South Africa Annlizé Marnewick, University of Johannesburg, South Africa Chapter 76 Agile Approaches for Successfully Managing and Executing Projects in the Fourth Industrial Revolution.......................................................................................................................................... 1529 Alexius A. Emejom, University of the People, USA Carl Burgess, University of North Texas at Dallas, USA Donna Pepper, Benedictine University, USA Joan Adkins, Colorado Technical University, USA Chapter 77 Strategic Management in SMEs in Industry 4.0................................................................................ 1548 Mustafa Atilla Arıcıoğlu, Necmettin Erbakan University, Turkey Büşra Yiğitol, Konya Food and Agriculture University, Turkey Chapter 78 Industry 4.0-Based Enterprise Information System for Demand-Side Management and Energy Efficiency........................................................................................................................................... 1570 K. S. Sastry Musti, Namibia University of Science and Technology, Namibia Helvi Iileka, Namibia Energy Institute, Namibia Fenni Shidhika, Namibia Energy Institute, Namibia Chapter 79 Human Capital Management in the Fourth Industrial Revolution..................................................... 1592 Vivence Kalitanyi, University of Johannesburg, South Africa Geoff A. Goldman, University of Johannesburg, South Africa Chapter 80 Human Capital Formation for the Fourth Industrial Revolution: The Role of Women..................... 1613 Paul Adjei Onyina, Pentecost University College, Ghana Chapter 81 New Generation of Productive Workers: How Millennials’ Personal Values Impact Employee Productivity in Industry 4.0............................................................................................................... 1632 Rok Cresnar, University of Maribor, Slovenia



Chapter 82 Education in the Era of Industry 4.0: Qualifications, Challenges, and Opportunities....................... 1647 Dharmendra Trikamlal Patel, Charotar University of Science and Technology, India Chapter 83 The Future of Product Design Education Industry 4.0...................................................................... 1666 Jennifer Loy, University of Technology Sydney, Australia James I. Novak, University of Technology Sydney, Australia Chapter 84 The Development of the Management Competences at the Postgraduate Level in the Context of the Fourth Industrial Revolution........................................................................................................ 1686 Edgar Oliver Cardoso Espinosa, Instituto Politécnico Nacional, Mexico Chapter 85 Professional Training in Tourism for the Fourth Industrial Revolution............................................ 1699 Rosa María Rivas García, Instituto Politécnico Nacional, Mexico Jésica Alhelí Cortés Ruiz, Instituto Politécnico Nacional, Mexico Chapter 86 The New Challenges in the Training of the Engineer for the Industry 4.0: A Case Study of a Brazilian University Center............................................................................................................... 1711 Sergio Ricardo Mazini, University Center Toledo Araçatuba, Brazil Márcia Maria Teresa Baptistella, University Center Toledo Araçatuba, Brazil Chapter 87 Employer’s Role Performance Towards Employees’ Satisfaction: A Study of SME Industry 4.0 in Malaysia............................................................................................................................................. 1721 Siti Noorjannah Abd Halim, Universiti Sains Malaysia, Malaysia Siti Noorhaslina Abd Halim, Universiti Teknologi MARA, Malaysia Section 7 Critical Issues and Challenges Chapter 88 Analysis of Challenges Responsible for the Slow Pace of Industry 4.0 Diffusion............................ 1737 Chetna Chauhan, Indian Institute of Management, Rohtak, India Amol Singh, Indian Institute of Management, Rohtak, India Chapter 89 Industrial Occupational Safety: Industry 4.0 Upcoming Challenges................................................. 1767 Susana Pinto da Costa, University of Minho, Portugal Nélson Costa, University of Minho, Portugal



Chapter 90 Technodata and the Need of a Responsible Industry 4.0................................................................... 1788 Raúl Tabarés Gutiérrez, Fundación Tecnalia Research and Innovation, Spain Javier Echeverría Ezponda, Ikerbasque, Spain Chapter 91 Sustainable Manufacturing in the Era of Industry 4.0: A DEMATEL Analysis of Challenges........ 1807 Ravinder Kumar, Amity University, Noida, India Chapter 92 Investigation of Operational Characteristics of Mechatronic Systems in Industry 4.0...................... 1816 Raul Turmanidze, Georgian Technical University (GTU), Georgia Predrag V. Dašić, High Technical Mechanical School, Trstenik, Serbia Giorgi Popkhadze, Georgian Technical University (GTU), Georgia Chapter 93 Cybersecurity Issues and Challenges in Industry 4.0........................................................................ 1836 Ravdeep Kour, Luleå University of Technology, Sweden Chapter 94 Industry 4.0 Privacy and Security Protocol Issues in Internet of Things.......................................... 1853 Jayapandian N., Faculty of Engineering, Christ University, Bangalore, India Chapter 95 An Introduction to IWoT: How the Web of Things Helps Solve Industry 4.0 Challenges................ 1877 Ángel Retamar, CTIC Technology Centre, Spain Daniel Ibaseta, CTIC Technology Centre, Spain Andrés G. Mangas, CTIC Technology Centre, Spain Iván Gallego, CTIC Technology Centre, Spain Irene Alonso Canella, CTIC Technology Centre, Spain Lucía Fernández, CTIC Technology Centre, Spain Chapter 96 Information Literacy and the Circular Economy in Industry 4.0....................................................... 1915 Selma Leticia Capinzaiki Ottonicar, Sao Paulo State University (UNESP), Brazil Jean Cadieux, Université de Sherbrooke (UdeS), Canada Elaine Mosconi, Université de Sherbrooke (UdeS), Canada Rafaela Carolina da Silva, Sao Paulo State University (UNESP), Brazil Chapter 97 The Impact of Unified Communication and Collaboration Technologies on Productivity and Innovation: Promotion for the Fourth Industrial Revolution............................................................. 1936 Anthony Bolton, University of South Africa, South Africa Leilani Goosen, University of South Africa, South Africa Elmarie Kritzinger, University of South Africa, South Africa



Chapter 98 Challenges for Pakistani SMEs in Industry 4.0: Applications of Disruptive Technologies.............. 1959 Qaisar Iqbal, Universiti Sains Malaysia, Malaysia Noor Hazlina Ahmad, Universiti Sains Malaysia, Malaysia Chapter 99 Chinese SMEs in Industry 4.0: Analysis and Future Trends............................................................. 1968 Hu Shi, Universiti Sains Malaysia, Malaysia Qaisar Iqbal, Universiti Sains Malaysia, Malaysia Chapter 100 Factors Influencing Port Terminal Automation in the Fourth Industrial Revolution: A Case Study of Durban........................................................................................................................................... 1978 Indira Padayachee, University of KwaZulu-Natal, South Africa John Mukomana, University of KwaZulu-Natal, South Africa Index.................................................................................................................................................xxviii

xxv

Preface

Traditional manufacturing and production methods are being utilized less as Industry 4.0 brings on a new transformation that is fundamentally changing the processes of various types of industries. In order to remain competitive and stay updated with current trends, businesses and industries must embrace this modern technology to keep up with the times. The inclusion of smart factories, automation, the internet of things (IoT), artificial intelligence, robotics, and more is creating new benefits and new challenges. New threats in security, reliability, and regulations in the technologies, malfunctioning devices, and operational disruptions are a few of the major concerns. Examining current research, case studies, and applications can lead to a greater understanding of the impacts, issues, and technologies in Industry 4.0. Staying informed of the most up-to-date research trends and findings is of the utmost importance. That is why IGI Global is pleased to offer this four-volume reference collection of reprinted IGI Global book chapters and journal articles that have been handpicked by senior editorial staff. This collection will shed light on critical issues related to the trends, techniques, and uses of various applications by providing both broad and detailed perspectives on cutting-edge theories and developments. This collection is designed to act as a single reference source on conceptual, methodological, technical, and managerial issues, as well as to provide insight into emerging trends and future opportunities within the field. The Research Anthology on Cross-Industry Challenges of Industry 4.0 is organized into seven distinct sections that provide comprehensive coverage of important topics. The sections are: 1. 2. 3. 4. 5. 6. 7.

Fundamental Concepts and Theories; Development and Design Methodologies; Tools and Technologies; Utilization and Applications; Organizational and Social Implications; Managerial Impact; Critical Issues and Challenges.

The following paragraphs provide a summary of what to expect from this invaluable reference tool. Section 1, “Fundamental Concepts and Theories,” serves as a foundation for this extensive reference tool by addressing crucial theories essential to the understanding of the technologies and advancements being brought on by Industry 4.0. This reference book opens with the chapter “Opportunities, Challenges, and Solutions for Industry 4.0” by Prof. Hee Song Ng of KDU Penang University College, Malaysia, which makes an in-depth analysis on the issues and controversies of I4.0, recent technological 

Preface

advancement, management, and organizational concerns and offers suggestions for solutions and recommendations in the future. This opening section ends with “Industry 4.0 and Supply Chain Management: A Methodological Review” by Prof. Pavitra Dhamija of the University of Johannesburg, South Africa; Prof. Monica Bedi of Panjab University, India; and Prof. M.L. Gupta of the University of Technology, Chandigarh, India, which focuses specifically on Industry 4.0 and its association with supply chain management and presents newfound areas that require managerial attention. Section 2, “Development and Design Methodologies,” presents in-depth coverage of the conceptual designs and developments for systems utilizing Industry 4.0. The first chapter in this section, “Design of Facility Layout for Industry 4.0,” by Profs. Mirko Ficko, Lucijano Berus, Iztok Palčič, and Simon Klančnik of the University of Maribor, Slovenia, presents an efficient layout that ensures an efficient transport system and takes in account the large number of participants. The last chapter in this section, “Tacit Knowledge Sharing for System Integration: A Case of Netherlands Railways in Industry 4.0,” by Profs. Alberto Martinetti, Leo A. M. van Dongen, Yawar Abbas, and Mohammad Rajabalinejad of the University of Twente, The Netherlands and Prof. Lex Frunt of the Netherlands Railways, The Netherlands, presents an example from the Netherlands Railways to emphasize the key role technological solutions of Industry 4.0 can play in facilitating tacit knowledge sharing. Section 3, “Tools and Technologies,” explores the various tools and technologies being used in Industry 4.0 to improve processes, efficiency, and safety. This section beings with the chapter “Augmented Technology for Safety and Maintenance in Industry 4.0” by Prof. Sarbjeet Singh of Luleå University of Technology, Sweden; Prof. Vikas Kukshal of National Institute of Technology Uttarakhand, India; and Prof. Amar Patnaik of Malaviya National Institute of Technology, Jaipur, India, which discusses the challenges arising from digitalized manufacturing system and how innovative technologies can provide solutions for this. This section ends with “A Study of Quality Tools and Techniques for Smart Manufacturing in Industry 4.0 in Malaysia: The Case of Northern Corridor Economic Region” by Profs. Saifuddin Isa, Mohd Syaiful Rizal Abd Hamid, and Chew Boon Cheong of the Universiti Teknikal Malaysia Melaka, Malaysia, which explores the key factors for selecting quality tools and techniques in industrial revolution 4.0, particularly in the smart manufacturing context. Section 4, “Utilization and Applications,” describes how Industry 4.0 is being implemented and used within different types of models and business applications, as well as across numerous industries. Opening this section is “Addressing Sustainability and Industry 4.0 to the Business Model” by Profs. Maria do Rosário Cabrita and Susana Duarte of the Universidade NOVA de Lisboa, Caparica, Portugal, which provides insightful information on the potentials of exploring business models in the age of Industry 4.0 and the opportunities to promote business sustainability. Closing this section is the chapter “Identification of Challenges and Opportunities for Work 4.0 Competences Developing in Slovakia” by Profs. Helena Fidlerová, Martina Porubčinová, Martin Fero, and Ivana Novotná of Slovak University of Technology in Bratislava, Slovakia, which presents opportunities and threats in competence development regarding the concept of intelligent industry and discusses sustainable solutions in the context of National Action Plan of Intelligent Industry of Slovak Republic. Section 5, “Organizational and Social Implications,” includes chapters discussing the organizational and social impact that Industry 4.0 has caused various industries, as well as its impact on sustainability. The first chapter in this section, “Industry 4.0 as the Last Industrial Revolution and Its Opportunities for Developing Countries,” by Prof. Özalp Vayvay of Marmara University and Prof. Egemen Hopali of Üsküdar University, Turkey investigates the role of different technologies and business partners on success of Industry 4.0 and presents actions to be taken from the point of the emerging economies to xxvi

Preface

sustain and increase competitive advantage by implementing Industry 4.0. The last chapter in this section, “Technological Developments: Industry 4.0 and Its Effect on the Tourism Sector,” by Prof. Hande Mutlu Ozturk of Pamukkale University, Turkey, examines the impact of Industry 4.0 on the tourism sector specifically. Section 6, “Managerial Impact,” focuses on how Industry 4.0 is impacting leadership and management roles as well as developing new training methods for employees. This section begins with “Leadership 5.0 in Industry 4.0: Leadership in Perspective of Organizational Agility” by Prof. Bülent Akkaya of Manisa Celal Bayar University, Turkey, which discusses how the fast process of technology and the digital world are taking place in all organizational areas and in all kind of sectors as the business world is transformed by the postmodern revolution-Fourth Industrial Revolution. This section ends with the chapter “Employer’s Role Performance Towards Employees’ Satisfaction: A Study of SME Industry 4.0 in Malaysia” by Prof. Siti Noorjannah Abd Halim of the Universiti Sains Malaysia, Malaysia and Prof. Siti Noorhaslina Abd Halim of the Universiti Teknologi MARA, Malaysia, which establishes the relationship between employees’ satisfaction toward their employer’s role performance, specifically if the company jumps into the IR4.0.. Section 7, “Critical Issues and Challenges,” presents coverage of academic and research perspectives on challenges within Industry 4.0 and the various industries it impacts. This closing section starts with “Analysis of Challenges Responsible for the Slow Pace of Industry 4.0 Diffusion” by Profs. Chetna Chauhan and Amol Singh of the Indian Institute of Management, Rohtak, India, which offers insights into the reasons for the slow diffusion of smart manufacturing systems and provides an understanding of these challenges. The final chapter in this reference book, “Factors Influencing Port Terminal Automation in the Fourth Industrial Revolution: A Case Study of Durban,” by Profs. Indira Padayachee and John Mukomana of the University of KwaZulu-Natal, South Africa, reports on a case study aimed at determining the challenges and limitations experienced with the current information and communication technology used in port terminals in Durban and examines how technological, organizational, and environmental factors influence port automation. Although the primary organization of the contents in this multi-volume work is based on its seven sections, offering a progression of coverage of the important concepts, methodologies, technologies, applications, social issues, and emerging trends, the reader can also identify specific contents by utilizing the extensive indexing system listed at the end of each volume. As a comprehensive collection of research on the latest findings related to the fourth industrial revolution, the Research Anthology on Cross-Industry Challenges of Industry 4.0 provides IT specialists and consultants, engineers, managers, business science teachers, manufacturing companies, machinists, tourism professionals, researchers, administrators, academicians, technology professionals, and students with a complete understanding of the developments in Industry 4.0. Given the vast number of issues concerning the new technological revolution of Industry 4.0, the Research Anthology on Cross-Industry Challenges of Industry 4.0 encompasses the most pertinent research in the technologies, trends, and challenges in Industry 4.0 with cross-industry research.

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Section 1

Fundamental Concepts and Theories

1

Chapter 1

Opportunities, Challenges, and Solutions for Industry 4.0 Hee Song Ng https://orcid.org/0000-0002-2001-2477 KDU Penang University College, Malaysia

ABSTRACT Industry 4.0 (I4.0) is the fourth industrial revolution sweeping through the world of manufacturing. This revolution integrates the current trend of intelligent automation with internet of things (IoT), big data, and artificial intelligence to bring about extraordinary technological innovation, economic growth, and tremendous progress to organizations of all shapes and sizes, on a magnitude beyond the current imagination. The disruptive technologies introduced by I4.0 represent a leap forward from more traditional automation to next generation industrial production based on fully web-based cyber-physical systems (CPS)s. To full understand the I4.0 concept and implementation, this chapter makes an in-depth analysis on the issues and controversies of I4.0, recent technological advancement, management and organizational concerns in terms of opportunities and threats, capital investment and skillsets, cybersecurity threat, ethics consideration, current challenges facing organizations and industry in terms of geopolitical domination, economic and social disenfranchisement, job destruction and job creation, the roles of multinational corporations, lack of technologies capabilities, lack of skillset, and skill mismatches. This chapter also makes suggestions for solutions and recommendations in terms of the role of government and incentives and grants; assessment tools; collaboration; the development of local companies and small and medium-sized enterprises (SMEs); upskilling, reskilling, and lifelong learning; education; universities and students; skilled graduates; and future research and directions.

INTRODUCTION Industry 4.0 (I4.0) is the common term referring to the use of cyber-physical systems (CPS) which comprise numerous major innovations in digital technology such as artificial intelligence (AI), Internet of Things (IoT), machine-to-machine link, data capture and data analytics, cloud computing, advanced robotics and smart production facilities. Such systems are capable of independently exchanging informaDOI: 10.4018/978-1-7998-8548-1.ch001

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 Opportunities, Challenges, and Solutions for Industry 4.0

tion, triggering actions, controlling each other independently and making autonomous decisions. In fact, CPSs are driven by cutting-edge software, computational power, programmable logic controllers, and sophisticated sensors and cameras to process vast amount of data using advanced predictive algorithms, monitor real-time transactions at every monitor real-time transactions at every stage of a process in the value chain right from customer ordering, marketing, suppliers, procurement, design and development (R&D), manufacturing, logistics and customer services, finally make large-scale centralised decisions without human intervention (Brettel, Friederichsen, Keller, & Rosenberg, 2014). In another example, such systems can use Internet Protocol (IP) addresses, Quick Response (QR) codes and Radio Frequency Identification (RFID) tracking tags to keep track of the manufacturing of every product and process using by online computer and smart-phones apps with the ease of a button and on-the-go. The development of I4.0 technologies is fast changing the landscape of global supply chain at a breakneck pace. In this regard, digitalisation of the supply chain is underway to achieve operational efficiency and cost competitiveness. MNCs can leverage on digital global economy which are driven by information, ideas and innovation. Most leading companies are deploying such I4.0 technologies to share market intelligence and organizing their orders by divisionalsing their product lines according to the cost-base structures, economies of scales and tariff-free, and full market access with a view to realising high-flexible, individualised and resource-friendly mass production. In other word, I4.0 technologies will make such smart factories work easier, safer, leaner and more productive through I4.0 digital sphere. In today’s competitive environment, most MNCs certainly cannot afford to look nervously over their shoulders at rivals taking command of the technology revolution, instead quickly jump on the bandwagon of adopting I4.0 cutting-edge technologies to stay competitive, connect more closely with customers and finally propel organisations towards for more sustainable growth (Ghobakhloo, 2018; Pandiyan, 2017). Taking a quick look back in time the at these first three industrial revolutions, it is worth mentioning that Industry 1.0 refers to use of the mechanical production harnessing the power of water mills and coal-fired steam turbines in Britain in the late 1765, Industry 2.0 refers to the introduction of division of labour and electrically powered assembly lines especially pioneered by Henry Ford and Frederick Taylor in the manufacture of cars in 1870, and finally Industry 3.0 refers to the use of electronics and telecommunications and computers that further automate production in 1969. Over the course of history, mankind has perfected its industry by creating and striking innovations all throughout their revolutions. Indeed, previous three industrial revolutions have liberated humankind from animal power, made mass production possible and brought digital capabilities to billions of people (Sentryo, 2017).

COUNTRIES AND INDUSTRIES BACKGROUND The concept of I4.0, also known as Industrie 4.0, was originally coined by the Germany government in 2012 to drive its High-Tech Strategy under the 2020 initiatives. As I4.0 holds promises of scientific and technology development, industrial optimalisation and upgrading, and finally major productivity improvement, major developing and developed countries have scrambled to join the bandwagon with different variants to play catch-ups with I4.0 but maintain their competitive edge beyond traditional industrialisation pathways for wealth creation and prosperity. For examples, Made in China 2025 (by China); Smart Manufacturing (by USA); Revitalisation Robotics Strategy (by Japan), Manufacturing Innovation 3.0 (by South Korea), Smart Nation Programme (by Singapore), Future of Manufacturing 2050 (by UK), and Productivity 4.0 (by Taiwan) are some of the variants. At the organisational level, 2

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world leading companies are at the forefront of the I4.0 blueprints. For example, Google’s Waymo selfdriving cars, IBM’s Watson AI Systems, Google’s AlphaGo, Siemens’s Mindsphere, Bosch’s Software Innovation, Amazon’s Go autonomous stores and Chinese e-commerce giant Alibaba’s Ant Financial. Other tech giants include Facebook Apple, Tencent and Baidu and IFLYTEK (voice recognition tech firm). These companies are redefining high-tech innovation and product in the most trailblazing ways (Matthew, 2018). Their groundbreaking technologies and innovation in automation, computerisation, AI logarithms, 3D modelling and 3D printing have reshaped and transformed the business world in the most unpredictable ways. For design and development, new technologies significantly reduce the risk of creating innovative designs and producing smart products without the burden of a heavy capital investment in machines and materials because it is possible to build prototypes in computer software before undertaking mass production. Indeed, the idea of smart factory is becoming increasingly ubiquitous as more industries are adopting such technologies which allows it to work longer, 24/7 and with near zero defects that humans are incapable of. However, most countries are aware of the high-tech conundrum, namely, on one hand, they must embrace Industry 4.0 to avoid being left behind, but on the other, they may push back I4.0 in order to keep their cheap labour and low-skilled workers fully employed to maintain economic and social stability.

OBJECTIVES OF THIS CHAPTER The main objectives of this chapter are to examine the I4.0 concepts, issues, practices and ramifications. It covers opportunities, challenges and solutions of I4.0. Indeed, I4.0 offers great opportunities for organisations to achieve a very significant gain in productivity and technical capability where everything will be automated. However, I4.0 poses challenges to industries leaders and practitioners as the advancements have degenerated into myriad of issues like inequality, skill shortages, cybercrime and technological unemployment. Therefore, practical and bold solutions must be established to ensure smooth adoption of I4.0. In this regard, multi-stakeholders especially policymakers, industry players, universities and academia and MNCs can equally play a crucial role in facilitating, working out blueprints and innovative solutions and fine-tuning the execution plan and thereafter embarking their journey towards I4.0.

ISSUES AND CONTROVERSIES OF INDUSTRY 4.0 There are a lot of economics opportunities surrounding I4.0. According to Boston Consulting Group (2015), the widespread adoption of I4.0 could boost labour productivity by as much as 30% by 2024, stressing the economic and technological benefits of I4.0 technologies far outweigh costs of investments and maintenance. Brynjolfsson and McAfee (2014) argued that in the era of the ‘Second Machine Age’, a digital economy yields unanticipated paths for making record profits and boosting productivity. As humanoid robots on different platforms can learn, communicate control and “share knowledge” with each other without human intervention, significant improvements in quality and reduction in cost and time can be made (PwC, 2016). There are many advancements made for I4.0 technologies. According to BCG (2019), there are nine disruptive technologies that forms the building block of Industry 4.0 that have spurred tremendous progress in developing the next generation of smart manufacturing technolo3

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gies and reinvention. They are big data analytics, autonomous robots, simulation, horizontal and vertical system integration, the industrial internet of things, cybersecurity (plant security, network security, system integrity and risk), the cloud, additive manufacturing (3D printing) and augment reality. Of which, AI, an idea of decoding the human brain and mimicking human-like functions which was created by John McCarthy in 1956, has exploded in 2010 thanks to very fast state-of-the-art computer processors that allow the analysis of huge amount of data, learn from experience, adjust to new inputs and perform human-like tasks. AI has become the most important driving force for leading a new round of scientific and technological revolution and industrial transformation with leaps and bounds. For instance, AI, together its subset machine learning and deep learning, is at the forefront of the tech boom in the fields of virtual assistants, translations, vision for driverless delivery trucks, drones and autonomous cars, chatbots and service bots, image colorisation, facial recognition, medicine and pharmaceuticals, and personalized shopping and entertainment around the globe (Lee, 2018; Marr, 2018). In addition, Schmidt, Möhring, Härting, Reichstein, Neumaier and Jozinović (2015) stressed that the most important technologies are mobile computing, cloud-computing, and Big Data because these technologies can provide services that can be accessed nearly real-time, globally via the Internet and at the same time, support services can be easily developed, integrated and deployed. In this context, organisations can explore rich data visualisation and advanced reporting for smart decision making. Smart intelligence systems will create a superior capability that has never before been possible to catapult the industry into next industrial revolution with new means of efficiency, accuracy and reliability. Kuhn (2108) stressed that world powers have relentlessly sought breakthroughs in big data intelligence, deep machine learning, brain-like computing, multimedia computing, human-machine hybrid intelligence, swarm intelligence, expert systems, even quantum intelligent computing for a race toward the global tech supremacy. Such cutting-edge, novel and unique “futuristic tech” should empower people especially workers rather than replace them, and also serve the society rather than disrupt it. Experts believe that robot is not something to fear but to embrace as it will take a long time before AI can literally take over the world, causing uncertain, even dire, consequences for humanity. I4.0 also provides huge opportunities for creating innovation, restructuring industrial systems and uplifting the wellbeing of society, organisations and skilled workers. Harvard Business School (2018) reported that that since its inception, I4.0 remains an epicentre for innovation and economic growth and transforming people’s lifestyles with an array of applications in in the field of manufacturing, but also urban management, public services, transportation, logistics, health and security. Rapid deployment of smart manufacturing technologies in robotics, AI, IoT, data analytics, quantum computing and virtual reality has spurred tremendous progress in industrial innovation. This has generated ample opportunities to companies of all sizes to leverage on I4.0 technologies to achieve quantum leap in performance across industries. The growth of smart factories, advancements in supply chain management, and transformations of traditional manufacturing relationships are another strong testament that I4.0 technologies have farreaching, profound implications in the digital age. It is envisioned that I4.0 technologies will far exceed the three previous three revolutions in terms of smart adaptability, autonomy, efficiency, functionality, reliability, safety, and usability because the programmability, memory storage capacity and sensor-based capabilities are far more advanced and enhanced. Indeed, many scholars believe that I4.0 is an industry game-changer that seems to spring right out of science fiction movies with ‘Terminator’, ‘Norman’, ‘Sophia’ robots with a humanistic face, mobile internet, and mobile supercomputing. However, there are many naysayers who predict various doom, rather than gloom scenarios for countries, societies, organisations and workers. According to Deloitte Insights (2018), while digital trans4

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formation is taking shape in nearly every organisation, I4.0 paradoxes can be observed around strategy, supply chain transformation, talent readiness, and drivers for investment because most organisations need to overcome the disconnects between companies’ plans and actions, and tradeoffs between their current operations with the opportunities for innovation and business model transformation. In addition, there is another negative phenomenon, coined as ‘productivity paradox’ which basically suggests that dramatic advances in computer power and increasing investment in IT does not necessarily cause an increase in productivity in national economy (Brynjolfsson, 1993). Researchers even suspect that this highly-debated I4.0 concept may be a hot air and hype as they found out that only four of ten companies made good progress—with the strongest results in Germany and among technology suppliers (Breunig, Kelly, Mathis & Wee, 2016, Ghobakhloo, 2018). Critics also believe that I4.0 may bring major disadvantages to workers. There are countless dystopian viewpoints painting a foreboding scenario of the future of work, where millions of jobs will disappear in the coming decade due to increasing automation and AI. It is a fact that when new automation with AI capability is introduced, there is a concern for loss of high-paying jobs in the market (Mashelkar, 2018). Experts warn that society is confronted with ‘win-lose competition’ where high-skilled workers are rewarded with high pay and the rest of workers are left out in the cold. Another imbalance is the ownership and wealth concentration which are in the hand of chosen few. These development gives rise to a number of issues like unemployment, inequality, market consumption and tax revenues. With regard to the cost of capital investments, I4.0 requires substantial upgrading of high-tech hardware and software to support large-scale CPS systems in automated process control applications. The installation of both hardware - intelligent equipment, advanced camera and smart sensors, and software - coding, programming and operating control systems, necessitates commitment of huge capital investment. Studies of manufacturing show that sophisticated, flexible, expensive equipment often needs sophisticated, flexible, expensive people to operate and maintain it. This means highly-trained engineers, data scientists, software engineer, cloud computing programmers, object craftsmen, and robot operators with good knowledge and skills in data analytics capabilities are required to tackle any emerging technical issues and make final data-driven decision in a large scale, complex problems. There is an issue of cybercrimes in the era of I4.0. Most industry players are concerned with threat to data security and privacy besides system reliability and stability, and technical production outages. It is because existing IT infrastructure is incapable of managing information security, and inter-company data management, thus undermining digital trust in a 21st century digital environment (PwC, 2016; Schröder, 2017). In addition, recent advances in hacking technologies have made organisations more nervously and vulnerable to cyberattacks. Under this situation, risk-adverse organisations are reluctant to adopt the cloud-empowered I4.0 technologies and allow inter-companies link out of fear of losing control over data privacy and trade secrets. For example, the 1.5 million SingHealth patients’ non-medical personal data are stolen by well-planned hackers. To prevent future attacks and security issue, this organisation decided to pull the plug and go offline from the public domain and operate manually their operations. With regard to ethics in I4.0 technology, the development of future technologies and AI also creates serious concern to civil rights. A case in point is the recent backlash of a tech giant’s Face Recognition technology which has caused controversies due to its questionable accuracy of the software and perceived violation of civil rights. There is also a high chance of potential misuses and abuses of I4.0 technologies by rogue states, criminals and lone-wolf attackers. Researchers warn that rapid advances in AI are raising gargantuan risk that malicious users will soon exploit the technology to mount automated hacking attack, causing driverless car crashes or turn commercial drones into targeted weapons. Scientists, busi5

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ness leaders and ethicists are concerned with the ethics of tech giants participating in the development of military of autonomous war machines and killer robots. This not only poses imminent threat to the safety and security of a country but also have disastrous consequences on mankind (Horowitz, 2016). In fact, Bossmann (2016) asserted that there are top nine ethical issues when developing AI technologies. They are a) Unemployment - what happens after the end of jobs? b) Inequality - how do we distribute the wealth created by machines? c) Humanity - how do machines affect our behaviour and interaction? d) Artificial stupidity - how can we guard against mistakes? e) Racist robots - how do we eliminate AI bias? f) Security - how do we keep AI safe from adversaries? g) Evil genies - how do we protect against unintended consequences? h) Singularity - how do we stay in control of a complex intelligent system? and j) Robot rights - how do we define the humane treatment of AI? In this case, I4.0 technology inventors and innovators should have strong moral standard to guide their research and development to ensure to ensure they respect ethical boundaries rather than cross them for profitability. To uphold ethical values, they should have the willingness to foster public R&D, conduct open discourses on the purpose of technologies, deployment and ensure full adherence to code of ethics for technology to minimise harm to the public. In this regard, Smith (2018) suggested that five core principles to keep AI ethical and avoid controversy and conflict. They are namely a) AI must be a force for good and diversity, b) Intelligibility and fairness, c) Data protection, d) Flourishing alongside AI, and e) Confronting the power to destroy. In brief, I4.0 technology key decision makers should have strong moral compass to fulfill a social purpose and eventually serve the long term benefits of mankind. It is absolute crucial to inject human culture and values into machines and ensure human should remain “in” or “on” the decision making loop so that AI will act without accidentally harming human, especially preventing an artificial stupidity like allowing AI to conclude that the best solution was to eradicate human beings.

CURRENT CHALLENGES FACING ORGANISATION AND INDUSTRY As I4.0 is coming at a much faster pace due to the advancement and diffusion of AI technology, it is a challenge for all key stakeholder to anticipate the way corporations organise their operations and plan their goals and protect, the way workers protect their jobs and rights, and finally the way human interact with machine harmoniously for better efficiency and productivity. What is certain is that I4.0 has become the juggernaut and behemoth that define the future world of manufacturing and services with their far-reaching influence on future business, societies, jobs and world politics. To overcome the massive impact of the I4.0 technologies, it is important for all key stakeholder especially the world leaders, industry leaders, policymakers and academics to work together to thrash out the issues, mitigate challenges and work out innovative solutions to make I4.0 works for the benefits of all (Oesterreich & Teuteberg, 2016; Star Online, 2018).

GLOBALISATION 4.0 In the context of current world trades, there is a new trend of globalization pushback, dubbed de-globbalisation which poses serious challenges to global world order and economic growth. Schwab (2018) argued that the world, which is highly-interconnected, is surprisingly vastly under-prepared for a new type of globalization, coined as ‘Globalisation 4.0’ which calls for a more inclusive and sustainable 6

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global architecture and governance in terms of balancing paradoxically the climate change and economic growth, patriotism and accepting immigration, future of work for man or machine, future technology for life better or worse, a fairer economy for rich and poor, and working together or competing in knowledge, information, and technology. These challenges and contradictions are being addressed and redefined by the deep political, technological, society and economics forces. It is optimistic and hopeful that under the new era of Globalisation 4.0, the current world trade issue is not a zero-sum game, but a sweet spot for building harmonious bridges of trust and technical collaboration between the world powers for the future mankind.

ECONOMIC AND SOCIAL DISENFRANCHISEMENT Disruptive technologies impact almost every facet of economic activities from products and services to business models. According to PwC (2016), close to half of industrial companies in Asia-Pacific rate their level of digitalisation as “high” which lead to the annual digital revenue increases of 2.9% on average and cost reductions of 3.6% per annum. In another report, the 4.0 Research (2018) also forecasted that from 2016 to 2023, Asia-Pacific Industry 4.0 market is the world’s fastest, growing at a CAGR of 23.7%. That being said, Brynjolfsson and McAfee (2014) argued rapid automations could bring immense economic disenfranchisement. Recent indicators reflect that there are fewer people are working, and wages are falling. In the market economies, reduction of high-paying jobs will lead to reduction of worker incomes. Such declining incomes will significantly reduce consumption, thus disturbing the supply and demand of consumer economy, widening income gap (Ford, 2015). This situation is likely to cause social instability if no action is taken address the issues of mutation of traditional industries and jobs, arising from adopting I4.0. Disruptive technologies are reshaping the future of the global workforce, giving rise to the so-called gig economy. In a gig economy, temporary, flexible jobs are commonplace. ‘Shamrock’ organisations with a core of essential executives and key workers tend to hire independent contractors and freelancers in order to reduce the overhead cost. This new wave of economy undermines traditional economy of supply and demand, ownership and traditional workers who prefer full-time job for lifetime career advancement. For examples, Uber, Grab and Airbnb are often described as products of the “sharing economy”. It produces wealth for the benefits a tiny handful of the superrich rather than the workers. It is therefore necessary and make sense to counter the rise of ‘uberisation’ of economy and business in the long term.

JOB DESTRUCTION AND JOB CREATION In the changing era of job landscape, there will be a transition of jobs gained, job changed, and jobs lost in time of automation but these disruptions on the horizon may vary country by country, with the largest disruptions expected in advanced economies (Alsen et al., 2017). It is inevitable that traditional technologies and jobs are being replaced by high-value industries like intelligent driving, big data and data services. Such replacement and compensatory effects are in line with the Schumpeter’s theory of creative destruction where existing, traditional businesses and jobs are destroyed and replaced by new industries and new high-skilled jobs through new technologies and collaborative-networked innovation. Most experts predict that I4.0 technologies especially AI, deep learning and IoT will have a devastating 7

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impact on traditional industries and workers as it will obliterate half of all types of jobs. In an automated future, many jobs that will disappear in the next 20 years is due to automation and AI. According to Frey and Osborne (2013) from Oxford University, 47% of US jobs can be automated within the next two decades. It is predicted there will be significantly drop in demand for professions of all kinds, ranging from commodity salespeople, report writers, journalists, news authors and announcers, accountants and bookkeepers, lawyers, doctors, call-centre workers and telemarketers and car/bus/truck drivers (Brynjolfsson & McAfee, 2014). It is because it is possible to automate repetitive mundane and potentially 3D (dangerous, dirty and difficult) jobs with artificially-intelligent robots of all shapes and sizes in the manufacturing sector. In addition, the development of machine and deep learning, algorithm-driven decision frameworks, multi-language simultaneous translation, and rapidly improving speech-, voice face-recognition, VR panoramic navigation system, Amazon’s Alexa and Echo smart speaker, Siri-like intelligent personal assistant programme technologies has accelerated the robotic process automation (RPA) in the service sector (Brynjolfsson & McAfee, 2014; Economist, 2018). If such full-scale adoptions of automation go unchecked, then both blue-collar jobs and white-collar professions alike will evaporate, eradicate their wages and incomes, thus squeezing working- and middle-class families ever further. It is the biggest fear of jobless future, causing tectonic shift in the meaning of work and society as there will be unprecedented inequality between the haves and the have-nots in a two-tier society. In flexible and reconfigurable smart factories, there will be lot of highly-automated machines, operating in a nonsweatshop environment and supported by less skilled workers slaving over machines. Those displaced workers will need to overcome the inertia of traditional operations and quickly need to be reskilled and retooled. They need to be a jack of all trades, becoming multi-skilled and multi-tasked workers. Those who cannot unlearn and relearn new skills, run the risk of being made redundant and retrenched. However, Burke, Mussomeli, Laaper, Hartigan and Sniderman (2017) asserted that a smart factory does not necessarily translate into a “dark” factory. People are expected to be key to operations as technology is historically is the net job creator. I4.0 needs smart personnel to work in tandem with smart machines which they can significantly improve or co-create through integrating smart intelligent systems. New industries and new job like 3D designers, developers, engineers, programmers and scientists are highly sought after in the I4.0 technologies. New technologies will certainly create new products and new services at lower costs, thus creating new opportunity for job creation. Old technologies and old jobs will be available and not be quickly replaced because new technologies normally encounter teething issues like time and hedging risk delays to their implications, expected slow demand and technical limitations. Under this circumstance, organisations have to address of the issue of “hidden factory” that results from a mix of old and new technologies that impedes the smooth running of connected operations. There are also safe jobs that robots cannot do very well like managing people, imagination and creativity, and judgment and decision making.

THE ROLES OF MULTI-NATIONAL CORPORATIONS The rise of smart automation and globally connected IT systems heralds profound transformations of entire systems of production, management, and governance in established MNCs. It is clear that I4.0 has dawned on MNCs and local big companies as witnessed by the extent of the proliferation of I4.0 technologies in the industries. According to Ruban (2017), more than 5,000 MNCs have already started implementing some of these new technologies in their factories and plants. Although there will be a 8

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significant reduction in human dependency especially from the low-skilled and medium-skilled workers, I4.0 demands new highly-skilled workers. It is true that flawless manufacturing execution of I4.0 is only possible with the right integration of highly-skilled workers, intelligent machines and technologies. It is important to highlight that most MNCs have strong inclination to employ more machines rather than humans because machines can precisely produce parts with high quality products yet without human errors, thus increasing efficient and accuracy. It is logical to automate and robotise their operations when there is an acute shortage of well-trained workers. Small-scale industrial, lightweight robots or “cobots” are deployed in repetitive processes, which tend to be voluminous and prone to error, along with human employees to help keep assembly lines moving. Human employees have to change their work culture in order to interface and work alongside with cobots. In future, it is not inconceivable that humans have to work with highly intelligent robots that talk to each other, learn on their own through AI.

LACK OF TECHNOLOGICAL CAPABILITIES Although there are much discourses and talks about the I4.0, many business leaders only had a vague notion of what it is. Local companies lack of I4.0 technologies and are still ill-fitted for a future that demands digital prowess. MITI (2018) identified that major obstacles impending local companies from moving towards and exploiting the full potential of I4.0. They need a lot of catching up to benefit from the IR4.0 because they are lack of awareness on the concept of I4.0; no clear comprehensive policy and coordination on I4.0; infrastructure gaps particularly the digital infrastructure as well as ecosystem gaps; lack of targeted incentives to incentivise more companies to move to I4.0; mismatched skillsets and lack of right human capital; and lack of interoperability and standards resulting in difficulty of integrating different systems and reliability issue. In the 2016 FMM survey done by Monash University, 40% of small and medium sized enterprises (SMEs) are of the view that they do not need the internet to run their business. According to Ong (2017), SMEs face barriers to adopt I4.0 due to low awareness towards I4.0, lack of budget or funding constraints, lack of technical knowledge and practical skills to go into automation and beyond, and lack of skilled workers and training. In another survey, Ganapathy (2018) found that the main obstacles to SMEs in adopting I4.0 are systemic lack of funding, talent and knowledge concerning implementation. These barriers prevent them from fully adopting I4.0. It is a fact majority of local companies and SMEs remain primarily family-owned business with a simple structures and technologies, and therefore they prefer to serve more domestic market rather than global market (Musa & Chinniah, 2016). It is also widely recognised that resource-constraint SMEs have resisted the temptation to follow footsteps of big corporations. Many SMEs prefer to keep their foreign workers, rather than to invest heavily in new technologies as high-cost robots remain an unpopular choice. Without easy access to financing, SME owner-managers put high priority to routine jobs of meeting daily deliveries with target quality, cost and delivery to survive and continue to prosper, rather than incurring capital investment. SMEs are risk-adverse and more cost cautious when investing in high-tech machine, firm capabilities and hire talents to future-proof their business to stay in the game. To overcome the status quo, a new breed of dynamic local companies and SME owner-managers is required to build up new technologies to face new emerging technologies disrupting the market. It is important to note that some high-tech, high-growth, ‘Gazelle’ who manufacture sub-assembly high-tech products for MNCs may stand a chance to leapfrog their development and growth into large enterprises and eventually become large corporations (Sims & 9

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O’Regan, 2006). In fact, local companies have to tackle their technical and operational issue by recruiting high-skilled and highly-trained workforce. In terms of management and leadership, owner-managers need essential skills in transformational leadership, technical expertise, entrepreneurship to innovatively navigate their operations and tenaciously evolve into bigger enterprises (Ng & Kee, 2017).

SOLUTIONS AND RECOMMENDATIONS After evaluating the issues and challenges related to I4.0 implementation, it is necessary to provide solutions to asset and technology, people skills in order to survive and thrive in the future in today’s rapidly-changing global and vibrant digital economy.

THE ROLE OF GOVERNMENT Policymakers should establish I4.0 masterplan for industries through regulating policies and initiatives to attract strategic investments in I4.0 technologies and anchor the country’s competitive position in the global value chains. Such roadmap provides a clear direction for key industry players and streamline fasttrack programmes related to I4.0 technologies. It is high time to engage with all stakeholders and create awareness of I4.0 concepts and toolboxes for an automated future. Revisiting the issues of worker rights and livelihoods, there is a need to maintain a delicate balancing of creating new jobs and losing job for workers threatened up by new emerging technologies. The government has to find alternative industries by offering employment. It may be inevitable for regulators, working in tandem with private sectors and trade unions, to enact legislations to deaccelerate the adoption of I4.0 technologies to prevent the peril of technology where there is mass unemployment in society and technology take control of human lives. It is inconceivable to see humans become slaves to technology rather than technology serving humans in a new social contract. With regard to incentives and grants, the Government should provide incentive and grants to deserving MNCs and local companies, besides building strong infrastructures, ecosystems and strong legal framework for I4.0 (MITI, 2018). Alternatively, fintech companies with mobile e-payments should be allowed to legally offer financial assistance to local companies through equity crowdfunding (ECF) peer-to-peer (P2P) lending platforms. This will certainly help defray the huge capital outlays and the cost of hiring highly skilled, well-paid talented professionals and expatriates. This will boost up the bench strength of workforce to maximize the economic returns of I4.0 investment. They should make use of the government financial assistance to upgrade and install new technologies. Upgrading and R&D efforts, virtual manufacturing lab, a digitised lean assembly line and intelligent control room could help organisations design and produce world-class products and services. In order to ensure only deserving and well-prepared organisations obtain financial assistance, it is necessary to assess the overall preparedness of of organisation to embark on the I4.0 journey. It is important to evaluate the existing levels and standards of skilled workforce, smart machines and advanced technologies in operations. To determine its readiness and suitability in the I4.0 adoption, a diagnostic tool called the ‘Smart Industry Readiness Index’ run by Singapore Development Board in partnership with TÜV SÜD, could be adopted (EDB, 2017). It consists of 3 fundamental building blocks, namely: Process, Technology and Organisation with 8 pillars of focus and 16 dimensions of assessment. Alternatively, McKinsey and The University of Warwick also offers the Industry 4 readiness assessment 10

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tools to assess whether companies have the right capabilities and skills and are capable of housing I4.0 technologies and go digital (Daub & Wiesinger, 2015; WMG, 2017). Subsequently, organisations should focus on feasibility and cost-benefit analysis to make business decisions of becoming early adopters of technology and innovation or just laggards in technological progress. In term of technical collaboration, the government has to strengthen the industry-academia partnership for transfer of I4.0 technology, technical know-how and experiences to address the formidable challenges in the adoption of I4.0 (Switzerland Global Enterprise, 2017). MNCs and local companies can have a world-class platform to connect, exchange deeply, build consensus, promote the development together and test-bed solutions before scaling to the world market. Such ongoing collaboration can drive significant transformation in this digital age and rise to the challenge of I4.0. They have to invest in R&D to encourage technological innovation and to boost manufacturing capacity up the value chain. More joint R&D activities should be carried out to minimise industry segregation and improve networking. As the external task and general environments are volatile, it is imperative that MNCs and local companies they should have long term policies and strategies to guide their execution and to grow with the current challenges. The government can play a facilitating role between universities and private sectors. With partnership with leading industries, universities have to engage in high-impact R&D in order to be at the forefront pioneering new knowledge and technical knowledge. Universities should develop extensive academic link and industry network to offer industrial attachments opportunities. Established corporations should roll up apprenticeships and traineeships programmes to train unskilled workers to become competent in the competitive labour market. It is essential to equip technically-minded graduates with 21st century multidisciplinary skills and tools to increase their chances of employability.

EDUCATION I4.0 has given a new impetus to educational transformation of its content and delivery across disciplines through technological innovations. With the upcoming changes in I4.0 taking place, it is necessary to closely re-examine the curriculum and delivery through a mix of online and offline learning. Curriculum should be revamped to include technical aspects of autonomous robotics, computerisation, IoT, machine learning, AI, distributed ledger technologies, data analytics, cybersecurity, data science, and cloud computing. The technical contents should be industry-driven based on the existing tech giants. Revised course outlines in advanced application software such as Microsoft Visual Basic, JAVA, HTML, Javascript, Microsoft Access, MySQL, XAMPP and Notepad should possess industry certification for relevancy and employability. To tackle the challenges faced by students, industry and academics, there should be more digital and information literacy introduced to test, pioneer and learn new ways of solving problems of enormous complexity in configurations and calculations. Educators in the realm of I4.0 need to keep abreast of I4.0 technologies and discuss the frontier trend of smart technology in AI, Big Data, data science and data analytics capabilities. In this context, both science, technology, engineering and mathematics (STEM) and technical and vocational education training (TVET) education systems should be emphasised to ensure sustainable and balanced supply of both academic-based and skill-based qualifications. To push for digital agenda, the education system need advanced teaching techniques to cultivate the next-generation of university students and startups.

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UNIVERSITIES AND STUDENTS Universities are a centrepiece in nurturing the skills and talents required for I4.0. Hence, universities should adopt suitable learning management system and use the modern student-led learning and teaching approaches through a combination of classroom lessons, project based studies, case and scenario analysis, and internship with companies having I4.0 systems. Science-focused, AI related courses should be offered to meet growing skill demands in data analytics, AI design, IoT engineering, environmental science and precision farming. Computer science, data engineering, electronics engineering, and computational thinking in science and mathematics courses should be infused into not only computing and robotic engineering tracks but to business, finance, and actuarial science disciplines to ensure students are exposed to multidisciplinary technical fields. Universities should groom students to become professionals like data analysts and scientists, AI specialists, and software and applications developers and analysts for future recruitment possibilities. Universities should have an industry-driven, fluid and organic academic programmes, flexible modular classroom and modern pedagogy such as heutagogy (self-based learning), paragogy (peer-oriented learning) and cybergogy (virtual-based learning) to stay relevant to the industry demands. As smartphones, intelligent wearable devices and desktop computers are ubiquitous in today’s world, faculties should deploy state-of-art technologies and facilities that allows students with 24/7, mobile access on the multimedia learning materials. This gives students the opportunity to learn and playback anytime, anywhere. Students can virtually interact with faculty members through social media like Facebook, WhatsApp, WeChat, Skype, Telegram, and Live Chat for better and effective learning experience. Before leaving their intellectual ivory towers, graduates should boost their 10 future skillset, namely, complex problem solving, critical thinking, creativity, people management, coordinating with others, emotional intelligence, judgement and decision making, service orientation, negotiation and cognitive flexibility. As such skillsets are among the things that humans do better than machines, thus ensuring their relevance now and in the future (Gray, 2016). Graduates should be well prepared for the future. They should get set for new jobs that will either not exist, or such jobs that will look very differently by the time they graduate. There will be new types of jobs requiring new skills in various industries which are setup in various part of the world. Hence, they must be cross-culturally, digitally and ethically literate to handle future of works. Indeed, as leaders of tomorrow, graduates with STEM and TVET qualifications should possess not only emotional quotient, cultural quotient, intelligence quotient but also digital quotient in order to be groomed to become business leaders, innovators and technopreneurs (Dhesi, 2018).

THE ROLES OF DEVELOPMENT OF LOCAL COMPANIES AND SMES Local companies have to be well prepared for optimal processes, technologies and solutions of I4.0 in order to ride the wave of I4.0 technologies in terms of industry convergence, breakthrough global best practices and changing customer dynamics. As I4.0 brings a lot of uncertainties and transformation, it is essential to anticipate future products and services to serve customer needs. With states supporting on technology-based education and training, industries should recruit the best and the brightest with high-tech digital skills to build strong future workforce across all levels and categories to support digital enterprises (PwC, 2016). Such graduates and non-graduates have basic technical skills to pick up new

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industry-specific skills. They have to learn, lead and build up firm capabilities. Besides, it is necessary to invest heavily in agile, high-tech IT infrastructure needed to inject advanced technologies. While it is important to instill frugality, hard work of 996 (9am to 9pm six days), self-reliance, ethics and values, it is equally important to nurture a work culture that encourages innovation and fosters a sense of challenge. Such work culture is embedded in the organisational culture that welcomes good ideas from anybody, no matter an employee’s rank. There should be a paradigm shift in thinking outside of human narrow creative window in a highly conformist society. Employees are encouraged to take measured risks, and try new things, without fearing making mistakes and facing punishments. It is necessary to change to new mind-sets as workers and staff who have been doing things the same way for many years might not see the need for change.

UPSKILLING, RESKILLING AND LIFELONG LEARNING It is a fact that most local businesses do not want to spend much time and money on training to improve new employees’ shortfalls in skills. But they have to change their mind-set in today era of digital, globalised world. It is important to ensure new hires can quickly use digital tools in every aspect of their daily activities without getting too long on-the-job training. Skilled workforce should be constantly upskilled to learn I4.0 technologies to add values to companies and customer experience. Old workers, unskilled or semi-skilled workers who cannot adapt to new role will inevitably be replaced. It is crucial to ensure that affected workers must have the ability to learn new knowledge and skills. To fast-track the learning process, it is necessary to hire talents and expatriates to manage I4.0. Digital know-how and affinity should be part of the company’s DNA. With the I4.0 fast-eclipsing existing digital revolution, there is a growing demand for digital talent for MNCs and local companies. Therefore, it makes sense to attract global talent pipeline from abroad (TalentCorp, 2017). Meanwhile, all employees have to achieve delicate work-life balance and follow an ethos of lifelong learning to enhance hybrid skills mastery. As the future is uncertain, it is undoubtedly difficult to predict future skillset for any soaring career. Besides, they have to ensure good overall equipment effectiveness and future-proof operations. Future workforce have to “learn how to learn” in order to hone their digital skillsets. It is possible to experiment new methods like learning analytics, blended learning and virtual learning for effective learning. Future workers should accept agility, adaptability and flexibility with open-minded thinking to work for a sustainable future.

FUTURE RESEARCH AND DIRECTIONS As I4.0 is an integrative, self-organised system for creating values for organisations, it is important to identify relevant research avenues for future research. The future research area is to determine which industries will be winners and losers in today’s digital transformation as nobody can accurately predict the applications and feasibility, and acceptance of I4.0 technologies. The second research area is the new relationship between humans and machines in terms of work culture and ethics. Although the holy grail for smart factories is to have a “lights-out factory” which seamlessly operates without human intervention, the future role of humans should be more important than those of machines. According to Schwab (2016), new technologies are first and foremost tools ethically made by people and for people, and by 13

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no machines. Having said that, there is a potential issue of singularity where machines with artificial superintelligence can outsmart human capabilities. Other research areas can cover cybercrime, cybersecurity, data sharing and privacy, and interoperability standards between firms and industries (Roblek, Meško, & Krapež, 2016).

CONCLUSION In spite of the pessimistic predictions about 4IR concern on massive disruptions almost in every industry in a non-linear way at a speed and scale unparalleled in human history, the mainstream view is optimistic that I4.0 will bring more promise of techno-utopia society where I4.0 technology will help countries make economic progress, bring comfort and happiness to the society, liberate humanity where people can be freed up for creativity. At organisational level, MNCs have made a paradigm shift in managing innovation and adopting new technologies in order to gain first-mover advantage, outpace their competitors and finally thrive in the digital economy. To achieve this, MNCs should constantly upgrade their technologies and employ highly skilled workforce through upskilling and reskilling, and reinvent their impregnable business models to ensure I4.0 technologies generate economic benefits and better customer experience. With regard to local companies who are relatively under-resourced, they have no choice but to embrace I4.0 technologies. They need to play catch-ups to remain engaged in the global supply chain dominated by MNCs. They have to maintain high competitiveness in quality, cost and delivery in order to achieve profitability and therefore make investment in I4.0 technologies. Local companies have to work harder and smarter in order to punch far above their weight to succeed in the global competition by leveraging on I4.0 technologies. They have to be transformational, competent, entrepreneurial and innovative in order to be at the forefront of the smart manufacturing systems. For workers in the era of I4.0, it is evitable to face technological unemployment where low-skilled workers will be displaced, later lose jobs and marginalized but high skilled workers will gain new jobs for better pay and experience. In this case, displaced workers need reskilling for closing skill gap and overcoming skill mismatches in order to improve their chance of returning to the workforce or face the risk of retrenched. As robots and AI technology will be taking over routine works, unskilled and retrenched workers have to have the prospect of low-paid jobs or joblessness in the future. On the other, high-skilled workers will be in high demand for smart factory and smart office. But, they should be upskilled with relevant IT and technical knowledge and skills to stay in the jobs which offer handsome pay and job satisfaction. To push organisations to the next level, talented workforce should be sought and deployed by farsighted employers across different industries to fast-track development and transfer of technology and technical skills while reducing learning curve. It is envisaged that the advent of major the advancement and diffusion of AI, deep learning and automation technologies requires strong support from the management commitment and talented people to bring I4.0 to fruition. In the long run, all workers need to future-proof themselves against the job apocalypses and successfully ride the tidal wave of I4.0 Working closely with MNCs, industries players and high-tech organisations, government have to adopt the concept of e-government, establish I4.0 policies and practices address issues of high-tech infrastructures and industrial upgrading and re-structuring. Policymakers need to work out initiatives to support the capital investment by high-tech MNCs and creation of high-tech industries through incentives and grants. It is true that most countries adopt this I4.0 technologies in order to inject new energy to drive the next wave of high-tech growth and technology progress which have significant spillover effects on 14

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economic growth and social development. To ensure I4.0 can be promoted, there be an ecosystem that allows creativity and innovation from risk-taking and leading experts of I4.0. The government can create public and private sector collaboration platform so that cross-industry industry can share technical knowhow and R&D technology to promote transfer of technology and skills. In this way, they can learn and stay above the curve and also navigate triumphantly in this fast-changing technological landscape. Policymakers need to address the address the issues of consumption, social inequality, skill shortages, risk of cybercrime and technological unemployment. In this aspect, the policymakers need to think beyond the Gini coefficients and include measures like social welfare subsidies and universal basic income distribution to address the absolute income gap and social issues. It is absolutely necessary for governments work with the universities to address the issues of skill shortages and mismatches by offer relevant development programmes. Both public and private universities have to take cognizance of skill requirements to narrow down the huge disparity between what is learnt and the skills needed by employers. In other words, universities have to ensure that the supply of supply of graduates meet the industry demand and there are job opportunities available in the market. All universities have to promote deep integration of AI and education so that a more open and flexible education will be equal and suitable for everyone. Graduates will be more marketable in their technical profession if they have relevant I4.0 skills. In the era of I4.0, it is obsoletely necessary to produce techsavvy graduates who are keen and able to adapt and learn new technologies like computer programming, object coding, app development, 3D printing, robotics, and data analytics. Besides, universities should churn out graduates who are transformational, competent, entrepreneurial and innovative in tacking complex problem and working out automated solutions. Policymakers play a critical in the development between technologies and human development. Without proper human capital development, it is difficult to support the advances in technologies and innovation. In fact, the success of any country in meeting the challenges I4.0 lies at the heart of how human capital of future generations. Going forward, all stakeholders like public institutions, private sectors, captains of industries, entrepreneurs, civil society and academia from different countries need to collaboratively and ethically work together to ensure not only I4.0 deliver the economic potential and long-term benefits of society and workers but also remain human-centred, giving humans more time and freedom for a better, safer and happy life in the future. It is envisioned that there will be a closely intertwined human-robot interaction and easy-to-use synergistic automation that pairs brute force algorithms with human ingenuity. Under such situation, the development of I4.0 technologies of today and tomorrow should bring benefits to the mankind. This means, people (human) should be in decision making loop where humans can exercise judgement and definitely have a final say on all important decisions in the CPS system. Hence, it makes sense to intensify efforts to overcome major disruptions and break down the barriers of implementation like cost of investment, cost of running I4.0 technologies and technical standards and challenges, besides handling massive resistance to change from relevant stakeholders. Although there are no one-size-fits-all solutions, steps and initiatives should be taken to ensure the development of I4.0 technologies of today and tomorrow can bring greater benefits for all stakeholders especially countries, societies, organisations and workers.

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Li, Y. (2018, July 4). Why Made in China 2025 Will Succeed, Despite Trump? The New York Times Company. Retrieved from https://www.nytimes.com/2018/07/04/technology/made-in-china-2025dongguan.html Marr, B. (2018). What Is Deep Learning AI? A Simple Guide With 8 Practical Examples. Retrieved May 21, 2019, from https://bernardmarr.com/default.asp?contentID=1572 Mashelkar, R. A. (2018). Exponential Technology, Industry 4.0 and Future of Jobs in India. Review of Market Integration. doi:10.1177/0974929218774408 Matthew, K. (2018). Five smart factories – and what you can learn from them. Retrieved August 18, 2018, from https://internetofbusiness.com/success-stories-five-companies-smart-factories-can-learn/ MITI. (2018). FAQs on Industry 4.0. Retrieved August 15, 2018, from http://www.miti.gov.my/index. php/pages/view/industry4.0?mid=559 Musa, H., & Chinniah, M. (2016). Malaysian SMEs Development: Future and Challenges on Going Green. Procedia - Social and Behavioral Sciences, 224, 254–262. doi:10.1016/j.sbspro.2016.05.457 Ng, H. S., & Kee, D. M. H. (2017). The core competence of successful owner-managed SMEs. Management Decision, 56(1), 252–272. doi:10.1108/MD-12-2016-0877 Oesterreich, T. D., & Teuteberg, F. (2016). Understanding the implications of digitisation and automation in the context of Industry 4.0: A triangulation approach and elements of a research agenda for the construction industry. Computers in Industry, 83, 121–139. doi:10.1016/j.compind.2016.09.006 Ong, C. T. (2017). The Challenges of Industry 4.0 for Small and Medium-sized Enterprises (SMEs). Retrieved August 13, 2018, from http://www.miti.gov.my/miti/resources/Industry4Point0/SMEAM_ The_Challenges_of_Industry_4.0_for_SMEs_.pdf Online, S. (2018, August 14). Designing automated minds. The Star Media Group Bhd. Retrieved from https://www.pressreader.com/malaysia/the-star-malaysia-star2/20180814/281960313586319 Pandiyan, M. V. (2017). Industry 4.0: The future is here. The Star Online. Retrieved from https://www. thestar.com.my/opinion/columnists/along-the-watchtower/2017/09/06/industry-40-the-future-is-heremalaysia-cannot-afford-to-lag-in-a-world-facing-swift-exponential-cha/ PwC. (2016). Industry 4.0: Building the digital enterprise. PwC. doi:10.1080/01969722.2015.1007734 Research. (2018). Industry 4.0 Market & Technologies - 2018-2023. Retrieved August 14, 2018, from https://industry40marketresearch.com/reports/industry-4-0-market-technologies/ Roblek, V., Meško, M., & Krapež, A. (2016). A Complex View of Industry 4.0. SAGE Open, 6(2). doi:10.1177/2158244016653987 Ruban, A. (2017). Minister: More than 5,000 MNCs adopted Industry 4.0. Retrieved August 14, 2018, from https://www.malaymail.com/s/1399771/minister-more-than-5000-mncs-adopted-industry-4.0 Schmidt, R., Möhring, M., Härting, R. C., Reichstein, C., Neumaier, P., & Jozinović, P. (2015, June). Industry 4.0-potentials for creating smart products: empirical research results. In International Conference on Business Information Systems (pp. 16-27). Springer. 10.1007/978-3-319-19027-3_2

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Schröder, C. (2017). The Challenges of Industry 4.0 for Small and Medium-sized Enterprises. FriedrichEbert-Stiftung. Retrieved from http://library.fes.de/pdf-files/wiso/12683.pdf Schwab, K. (2016). The Fourth Industrial Revolution: what it means and how to respond. World Economic Forum. 10.1038/nnano.2015.286 Schwab, K. (2018). Globalization 4.0 – what does it mean? World Economic Forum. 10.1038/nnano.2015.286 Sentryo. (2017). The 4 industrial revolutions. Retrieved from https://www.sentryo.net/the-4-industrialrevolutions/ Sheng, A. (2018, August 18). Competition and conflict in knowledge economies. The Star Media Group Bhd. Retrieved from https://www.thestar.com.my/business/business-news/2018/08/18/competition-andconflict-in-knowledge-economies/ Sims, M. A., & O’Regan, N. (2006). In search of gazelles using a research DNA model. Technovation, 26(8), 943–954. doi:10.1016/j.technovation.2005.07.001 Smith, R. (2018). 5 core principles to keep AI ethical. World Economic Forum. Retrieved from May 16, 2019, from https://www.weforum.org/agenda/2018/04/keep-calm-and-make-ai-ethical/ Switzerland Global Enterprise. (2017). Rising Digitalisation, Industry 4.0, Smart Cities and the Opportunities on the Life Sciences Market in Turkey. Retrieved from https://www.s-ge.com/sites/default/ files/cserver/article/downloads/market_study_rising_digitalisation_industry_4_smart_cities_2017.pdf TalentCorp. (2017). Visioning Malaysia’s Future of Work: A Framework for Action. Retrieved from www.telentcorp.com.my WEF. (2017). ASEAN 4.0: What does the fourth industrial revolution mean for regional economic integration. Retrieved from https://www.businessoffashion.com/community/voices/discussions/whatdoes-the-fourth-industrial-revolution-mean-for-fashion WMG. (2017). An Industry 4 readiness assessment tool. The University of Warwick, Crimson & Co.

KEY TERMS AND DEFINITIONS Artificial Intelligence (AI): The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. Automation: The use or introduction of automatic machine, robotics equipment in a manufacturing to improve efficiency and flexibility. Industry 4.0: A current trend of web-based automation using cyber-physical systems, Internet of Things, artificial intelligence and cloud competing that enables self-monitoring and self-optimization on the manufacturing network. Job Creation: A provision of new opportunities for paid employment, especially for those who are unemployed.

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Job Displacement: An involuntary job loss due to economic factors such as economic downturns or structural change in organizations. Reskilling: An improvement effort especially displaced employees an unemployed person to learn new knowledge and skills to assume new jobs. Skillset: A individual’s range of knowledge, skills, and abilities. STEM: An education system that focuses on science, technology, engineering, and mathematics disciplines for tertiary studies. TVET: A technical and vocational education training (TVET) that equip non-academic youth with knowledge and skills for employment. Upskilling: An improvement effort of employees to learn additional skills in their profession.

This research was previously published in Business Management and Communication Perspectives in Industry 4.0; pages 3251, copyright year 2020 by Business Science Reference (an imprint of IGI Global).

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Chapter 2

Impact of Industry 4.0 Revolution on Science, Technology, and Society (STS): Challenges and Opportunities in the Industry 4.0 Era Tuba Ulusoy https://orcid.org/0000-0002-7484-6100 Necmettin Erbakan University, Turkey Esra Yasar KTO Karatay University, Turkey Mehmet Aktan Necmettin Erbakan University, Turkey

ABSTRACT The Industry 4.0 concept, which leads the Fourth Industrial Revolution, was introduced by Germany in 2011 at the Hannover Messe trade fair and attracted the attention of the world. Since that time, its effects have been seen in different fields, such as science, technology, and society. In this chapter, in order to investigate the effects of Industry 4.0 revolution, answers to the following questions will be presented: Are there any concerns about technological unemployment as a result of Industry 4.0. revolution? Which professions have emerged? How has Industry 4.0 affected society directly or indirectly? What are the technologies of this concept? How do these technologies affect manufacturing and service systems? What are the challenges of implementing the technologies of Industry 4.0? What are the benefits of digitalized manufacturing? Which studies are conducted to accelerate the shift of Industry 4.0 from science to reality? and Which studies have been conducted so far about this concept?

DOI: 10.4018/978-1-7998-8548-1.ch002

Copyright © 2021, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

 Impact of Industry 4.0 Revolution on Science, Technology, and Society (STS)

BACKGROUND Numerous studies, which examine Industry 4.0 from various perspectives, can be found in the literature. While some of them focus on the technologies related to Industry 4.0, in some studies, the topic is discussed as based on countries. However, to the best knowledge of the authors of this chapter, there is no study that presents the effects of Industry 4.0 on science, technology, and society together. This chapter deals with Industry 4.0, which means digital transformation in manufacturing by using high-technology. Since its effects lead to some changes in science, technology, and society, it is important to take attraction to this issue. Many resources indicate that this transformation has both negative and positive impacts. These impacts of Industry 4.0 is presented to shed light on different aspects of the concept of Industry 4.0 in this chapter. In this regard, this chapter can be a guide for the decision makers, who engage with education, industry, and politics.

Introduction Since Germany, which is one of the European countries, has faced some problems related to product quality and product cost, German government recognized that transformation in the industry is required in order to compete with Eastern countries, like China, which has advantages in terms of low production cost. The concepts of this transformation which lead to the fourth industrial revolution were introduced by Germany in 2011. The transformation is stimulated by the Internet and Cyber-Physical Systems (CPS) which enable digitalized manufacturing and smart factories. The fourth industrial revolution, namely Industry 4.0, has affected not only the manufacturing industry but also the social life. Although it is expected that the technologies related with Industry 4.0 will bring benefits on different aspects, such as economic and social, it brings some concerns related to employment. The global effects of Industry 4.0 Revolution in science, technology, and society are presented in the remainder of this chapter.

Impact of Industry 4.0 Revolution on Science In this section, information about scientific studies related to the concept of Industry 4.0 is presented to provide a literature review, which deals with the studies focusing on different aspects of the issue. German government published an article in November 2011 in which the “Industry 4.0” concept was introduced as a high-tech strategy for 2020 (Zhou, Liu & Zhou, 2015). After this date, papers related to the concept of Industry 4.0 started to be seen in the literature. When the term “Industry 4.0” is searched in the Web of Science (WoS) database, 1684 papers are found from its core collection. The number of papers for the years from 2012 to 2018 is given in Figure 1. According to the web of science database, the number of papers has increased year by year. It shows that interest in this issue has continued to be increased since its introduction. Numbers for types of papers can be seen in Figure 2. As seen in Figure 2, the largest number of papers are proceedings papers. There is also a significant number of articles in the journals which are indexed by WoS.

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Figure 1. Number of papers published between 2012 and 2018 (WoS, 2018)

Figure 2. Numbers for types of papers published between 2012 and 2018 (WoS, 2018)

As seen in Figure 3, the vast majority of papers are in the categories of Engineering Electrical and Engineering Industrial. While most of the articles published so far are related to the technological aspect of Industry 4.0, the authors of this chapter expect that scientific articles, which investigate the social effects of this concept, will be more common in the near future. It is recognized that the German term “Industrie 4.0” is used instead of Industry 4.0 in some studies in English. So, it is possible to see “Industrie 4.0” term in this study. Different definitions of this term can be found in literature. Some of these definitions from various resources are given as follows: Brettel et al. (2014) emphasized a confusing definition of the term of Industry 4.0 in their study is that “The imminent changes of the industry landscape, particularly in the production and manufacturing industry of the developed world”. Kolberg and Zühlke (2015) describe the terms as that it as a vision of future production.

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Figure 3. Number of papers according to web of science categories (WoS, 2018)

Gilchrist (2016) pointed out that Industry 4.0 refers to the fourth industrial revolution. Industry 4.0 is described by Lee et al. (2015) as a trend that transforms manufacturing industry to the next generation. Additionally, the authors of this chapter define the term of “Industry 4.0” as a vision that may lead to a considerably efficient transformation by using high technology, especially in manufacturing. Selected papers which are about Industry 4.0 are classified in 3 research categories given as follows: Studies on a specific country, applications of Industry 4.0 technologies, and literature reviews. Some papers focus on the studies related to Industry 4.0 in a country and show observed changes in the 4th industrial revolution so far. Moreover, “Industry 4.0: Building the digital enterprise (Geissbauer, 2015)”, and “Time to accelerate in the race toward Industry 4.0 (Lorenz et al., 2016)” reports are presented by Boston Consulting Group (BCG) and PricewaterhouseCoopers (PwC), respectively, which are global consulting management firms. BCG surveyed more than 600 German and American companies, while PwC conducted a survey of more than 2,000 participants from nine major industries in 26 countries, including the United States, Canada, United Kingdom, Germany, France, Brazil, Spain, China, and Japan. The reports, which present the results of these surveys, contain valuable information that enables to see in which stage the countries are in the Industry 4.0 race and the challenges they face when implementing the Industry 4.0. Some of the studies on a specific country are given as follows: Sung (2018) advices that companies should consider Industry 4.0 very seriously and suggests policy implications to transition toward Industry 4.0 in Korea. Li (2018) gives details of “Made-in-China 2025” strategic plan and compares the plan and Germany’s “Industry 4.0” vision. Hereinbefore, Germany is the country that introduced the Industry 4.0 concept to the world. Heng (2014) focuses on Industry 4.0 and Germany and states that Industry 4.0 has potentials that can enhance Germany’s industrial capabilities of the firms which already create one-third of the EU’s total industrial value added. The studies that examine Turkey and the concept of Industry 4.0 can be found in the literature. One of them was conducted by Özkan et al. (2018). In the study, effects of the fourth industrial revolution in

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Turkey are evaluated and the authors state that Turkey is a country, which aims to have one of the ten most powerful economies in the world, so, adoption to the fourth industrial revolution is necessary to realize this aim. Besides, threats and opportunities of Industry 4.0 for Turkey are discussed by Koca (2018). When technology related studies in the literature are examined, it is thought that these studies can guide the firms on how Industry 4.0 technologies will be implemented. Also, these studies present implementation of these technologies and expected benefits of implementations. The details of some selected studies are given at the remainder of this section. In the scope of the study of Lee, Kao, & Yang (2014), a systematic prognostics-monitoring system approach is proposed for self-aware and self-maintained machines and implementation of this approach for a heavy-duty equipment vehicle used in mining and construction is presented. It is noted that customers’ perception on product innovation, quality, variety, and speed of delivery are affected by information technology and social media networks under Industry 4.0 concept. Self-awareness, self-prediction, selfcomparison, self-reconfiguration, and self-maintenance are the capabilities which a factory should have in order to meet the needs of its customers. Shrouf, Ordieres, & Miragliotta (2014) propose an Internet of Things (IoT) based approach for a smart factory which can help managing energy efficiently. This study is carried out in a manufacturing company in Spain by collecting energy consumption data in real time with several smart meters which are installed at the machine level. It is expected that providing this data for decision makers after analyzing can reduce the wastes. Schuh, Gartzen, Rodenhauser, & Marks (2015) point out that increasing the integration of working and learning in order to support processes and instruction of new employees are promising approaches. In order to learn the new tasks, Cyber-Physical Systems (CPS) in Industry 4.0 provide work environments with new opportunities. A model including characteristics of Industry 4.0 that support work-based learning is proposed. Application is carried out at the Demonstration Factory of the RWTH Aachen Campus. Faller & Feldmüller (2015) indicate that Small and Medium-sized Enterprises (SMEs) should be supported to survive in the globalized environment since they face some problems related to Industry 4.0. In order to provide further training in modern technologies enabling Industry 4.0, together with the Schlüsselregion e.V., Bochum University of Applied Sciences established a Campus directly in the Velbert/ Heiligenhaus region in which there are SMEs of the lock & key industries. In this campus, mechatronics and information technology are taught, also, students have opportunity work in one of these SMEs. Literature reviews which examine papers related to the Industry 4.0 according to different aspects can be found in the literature. One of the literature reviews is conducted by Stock & Seliger (2016). In this study, different opportunities for sustainable manufacturing in Industry 4.0 are covered. Environmental contributions and realizing sustainable industrial value creation on all three sustainability dimensions: economic, social and environmental can be shown as examples of opportunities of this concept. Hermann, Pentek, & Otto (2016) present a literature review which indicates that Industrie 4.0 does not have a generally accepted definition. A definition of Industry 4.0 is given as “Industrie 4.0 is a collective term for technologies and concepts of value chain organization. Within the modular structured Smart Factories of Industrie 4.0, CPS monitor physical processes, create a virtual copy of the physical world and make decentralized decisions. Over the IoT, CPS communicate and cooperate with each other and humans in real time. Via the IoS, both internal and cross organizational services are offered and utilized by participants of the value chain.” based on the literature review. Interoperability, virtualization,

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decentralization, real-time capability, service orientation, and modularity are explained as six design principles of Industrie 4.0 in this study. Rojko(2017), Brettel et al.(2014), Liao et al. (2017), and Lu (2017) mention detailed background and overview of Industry 4.0 from different aspects. Furthermore, research proposals and open research topics can be found in the studies that conducted by Liao et al. (2017) and Lu (2017).

Impact of Industry 4.0 Revolution on Technology When the historical development of industry is reviewed, it is possible to see that new technologies and methods that come into use in production are the starting points of industrial revolutions. At the end of the 18th century, the first industrial revolution started with the introduction of mechanical production in which water and steam power were used. Division-based labor mass production with the help of electricity and production lines is assumed as the development which led to the second industrial revolution. The 3rd industrial revolution started with applications of IT and electronics which enabled the automation in production at the 1970s (Lukač, 2015). Presently, the era of the fourth industrial revolution, namely Industry 4.0, is continuing (Lu, 2017). Industry 4.0 which is the subject of this study is associated with the use of Cyber-Physical Systems (CPS) in production systems. CPS are new generation systems which transform industry. Physical factory floor and the cyber computational space are integrated in these systems that can both monitor and synchronize the information (Lee, Bagheri, & Kao, 2015). With the help of feedback loops, physical processes are affected by computations and physical processes affect computations in CPS (Lee, 2008). Air- and ground-traffic, discrete and continuous production systems, logistics, medical science, energy production, infrastructure surrounding us, entertainment are application fields of CPS that can affect human life. Also, the fields in which CPS are used may provide better quality of life (Monostori, 2014). Boston Consulting Group which is a global consulting management firm indicates that there are nine technological advances which transform industrial production in the report titled ”Industry 4.0: The Future of Productivity and Growth in Manufacturing Industries” by using case studies from Germany (Rüßmann et al.,2015). These technologies are shown in Figure 4 and the details of them are given as follows: The Industrial Internet of Things (IIoT): Zhou, Liu & Zhou (2015) described Internet of Things (IoT) as a vision that things “talk” to each other in a network, blending the virtual world with the physical world. Also, it can be described as a system in which electronics (RFID, tags, sensors, etc.) are embedded in physical items in order to connect Internet (Shrouf, Ordieres, & Miragliotta, 2014). The Industrial Internet of Things (IIoT) refers to IoT related applications which are used in industry. Objects with embedded sensors build the information networks which can provide improved business processes that may result in low cost and risks (Chui, Löffler, & Roberts, 2010). According to Sun (2012), one of the benefits of IoT applications in supply chain management operations is visibility that can improve supply chain transparency. Also, these applications can provide a real-time management, high agility, response to the varied market quickly, and complete integration. Although IIoT applications offer some advantages to the companies, security and data protection are the concerns associated with both IoT and IIoT. Cybersecurity: It is expected that as the number of interconnected companies via IoT increases, the number of cyber-attacks will increase as well (Ervural & Ervural, 2018). This situation brings the Cybersecurity on the agenda.

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Figure 4. Nine technologies transforming industrial production (Rüßmann et al., 2015)

The Cloud: Information technologies, like IoT, required the usage of Cloud computing which enables to provide computing resources, information integration and a data repository for the connected devices (things). Also, Cloud Computing may help to implement new architectures for automation (Givenchi & Jasperneite, 2013). Additive Manufacturing: This means that a certain material is produced by superimposing. Additive manufacturing (AM) is used in 3D technologies and recently, this issue has attracted much attention because of Industry 4.0. By AM technology disrupting the traditional production, it makes production more specialized and producing small volume and customer specific products efficiently. (Calignano et al., 2017) Augmented Reality: Augmented reality (AR) is a kind of mixed reality. AR is a technology that enhances the real word environment thanks to computer generated objects. (Krevelen & Poelman, 2010). In the near future, most companies will use AR technology extensively to improve themselves. Especially this technology will be much used in the areas like industrial design and marketing. This technology ensures essentially to produce unlimited new innovative products and ideas. Big Data and Analytics: Large and complex data is generated by the elements of Industry 4.0, including the equipment, machines, production, applications, products and services. Analytics are necessary in order to extract value from the huge data, namely Big Data. Big Data and analytics can enable to optimize processes, reduce costs, and improve operational efficiencies (Zhou, Liu & Zhou, 2015). Simulation: Simulation is the process of creating the environment by transferring the actual living data to a computer system. It provides advantages in terms of time, cost and risk management since it can make the development of the processes traceable (Celen, 2017).

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Horizontal and Vertical System Integration: While the integration of a resource and an information network within the value chain is called “Horizontal integration”, “Vertical integration” refers to networked manufacturing systems within the intelligent factories of the future and personalized custom manufacturing. Man to man, man to machine, machine to machine or service to service are the interconnections within CPS that may help integration in order to maximize customization (Zhou, Liu & Zhou, 2015). According to Rüßmann et al. (2015), most of today’s IT systems are not fully integrated, but Industry 4.0 will make companies, departments, functions, and capabilities integrated. Autonomous Robots: Robots have been preferred in manufacturing systems for a long time, but, they are not as simple as before. New robots are self-sufficient, autonomous, interactive, flexible, and cooperative (Gilchrist, 2016; Rüßmann et al. 2015). Artificial Intelligence (AI) also plays important role in today’s manufacturing. Robots with donated AI can perform the tasks which can be unsafe and unsuitable for a human. This may help to increase occupational safety. To ensure safety and security in the transportation of hazardous materials, to increase the quality of packaging used in food transport with smart containers, to prevent damage with sensors on machine in predictive maintenance, to provide easy and affordable repair with remote control in elevator maintenance, to update devices with remote control, to manage traffic flows, water flows, air quality, air security in smart cities can be opportunities provided by Industry 4.0 technologies.

Impact of Industry 4.0 Revolution on Society One of the biggest impacts of the Industry 4.0 will be on society since with the development of technology, lifestyles of people also change. The change in the way of life is reflected as a radical change in the society. (Ortega,2018) So far, it can be observed that society has been affected by developments of science and technology, since it must integrate its life to these changes which have both positive and negative impacts. As technology brings speed with it, fast communication and transportation were realized. While this situation has stepped up community communication, it is falsifying and eternal verities are not improving at the same time. New technology may have problems, such as loss of personality and laziness of people. These can be considered as negative effects of Industry 4.0 on society. On the other hand, Industry 4.0 also has positive impacts. It has increased cultural development because it provides easier access to areas of interest. Since development of technology helps people to save time, they can spend their time on self-development. Convenience has increased in many areas of society. For example; social assistance organizations are popularized and the forms of assistance to these organizations have been transformed into a message. Today, the effect of Industry 4.0 is observed in every country and adaptation processes are studied on the basis of countries. However Japan has not acted radically in this regard. It has been a partner of CeBIT, one of the world’s most comprehensive technology fairs in Germany and in this fair Society 5.0 philosophy was introduced by Japan. (Pîrvu & Zamfirescu, 2017) Thus, a report prepared by the Federation of Japanese Economic Organizations Keidanren tried to introduce this concept to the world (Keidanren, 2016). Within the scope of this report; from the moment when the society started to be established up to now, 5 divisions were presented as shown in Figure 5. 28

 Impact of Industry 4.0 Revolution on Science, Technology, and Society (STS)

Figure 5. The representation of the historical development of society (Keidanren, 2016)

These sections are hunting society, agrarian society, industrial society, information society and super smart society. Society initially originated as a hunter society (Fujii et al,2018). They used only animals as food supplies and had to hunt. After that, the agriculture period started and the agrarian society was formed by passing to resident life. With the invention of steam technology, industrial society transfer was provided. With the development of production and technology, the period of information society has begun. Finally, with the emerge and development of the Industry 4.0, super smart society concept was formed. The reason why Japan introduces this concept is that Industry 4.0 integrates the society and uses the technology that Industry 4.0 has brought. (Shiroishi et al,2018) The aim of this philosophy is to produce solutions against the aging world population, to find solutions for environmental pollution and natural disasters, to use the internet in order to collect the objects effectively and to integrate the virtual world with the real world. There was often job concern within the society since a new cycle began in every age, especially in terms of production systems. But this has always been the opposite. For example, the introduction of steam power made the people move into new business lines. For the new period, the same concepts of technological unemployment become the main topics of conversation and job concern among the society (Pfeiffer, 2017). Constitutively; unemployment is a level of employment created by people who want to work and who cannot find a job despite the desire and ability to work. Unemployment is mainly divided into open unemployment and closed unemployment. Closed unemployment occurs when there is no significant reduction in production in a situation where a part of the workforce is removed from production (İçerli, 2007). Open unemployment is a condition in which a person cannot find a job despite being willing. Among open unemployment, the most commonly found types of unemployment in the literature are given as follows:

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 Impact of Industry 4.0 Revolution on Science, Technology, and Society (STS)

Temporary unemployment: Temporary employment, studies on projects as well as fixed-time and subcontract work, are unrestricted work relationships. Temporary unemployment is a type of unemployment that occurs in this context. (Virtanen et al., 2005) Seasonal unemployment: It happens to decrease in production due to slowing of economic activity due to natural conditions or social events in the types of jobs where regular shifts do not exist in sectors like agriculture, construction and tourism (Mourdoukoutas, 1988; Unay, 2001) Structural unemployment: It is the type of unemployment that is caused by the economic structure of a country (Jackman,Roper, 1987) and experienced in such cases some sectors are making progress, while some sectors are declining; therefore, labour demand is experienced in developing sectors while labour loss is observed in declining sectors. (Oktay, 2002) Cyclical unemployment: The economy does not stay at the same level, there are developments such as recession and stagnation. In these periods there is a surplus of demand and this leads to a decrease in production. Thus, temporary or long term unemployment occurs. This type of unemployment is called cyclical unemployment. (Lilien,1982; Yıldırım, Karaman,2001) Technological unemployment: As technology developing, labour can be replaced by machines or new technology, this situation may cause the employees to become unemployed or working in new business lines. The type of unemployment in this period is called technological unemployment. Of the types of unemployment described here; technological unemployment is the type of unemployment that is concerned about living with Industry 4.0 and Society 5.0. In the literature, there are estimating studies about the current and near future on this subject. In some studies, it is predicted that technological unemployment will take place for a temporary period of 3 years; some studies have made estimates over the professions (Feldmann, 2013; Walsh, 2017; Frey & Osborne, 2017). With the integration of emerging technology into the way of life as Industry 4.0 and Society 5.0 inductivities, everything people use is renewing or changing in general terms. New occupations are also emerging to fully adapt to these innovations or changes. While some occupations lose their significance or disappear, some occupations come into prominence or are born. This transformation of the industrial era is in fact reflected in all areas of society. For example, in the academic sense, Industry 4.0 courses have been added by some schools at undergraduate level. Some of the pre-licensing periods have been added to the curriculum subject and is foreseen to be necessary in the new age. In the case of the example, even this situation reveals the need for a specialist teacher. Occupations to be newly formed are stated in some sources as follows (Lueth, 2015): • • • • •

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Industrial Data Scientist: This profession is based on Big Data management. This analyses the data and works to make the results of the analysis useful to the companies. Robot Coordinator: They undertake the task of supervising the production robots and ensuring their regular checks. Cloud Computing Expertise: It ensures that cloud computing works unproblematic so that all data from any point can be accessed with persistent internet servers. Wearable Technology Design: They work with the advancement of technology. Wearable products are technologically advanced, and at the same time can be designed by the costumer as desired (Eğer, n.d.) 3-D Printer Engineering: They undertake the task of developing new printers (Eğer, n.d.).

 Impact of Industry 4.0 Revolution on Science, Technology, and Society (STS)

Expectations in future job opportunities are in the direction of the reduction of jobs based on physical strength and the increase in employment of specialist and skilled workers. According to the World Economic Forum, with Industry 4.0, life satisfaction of each employee will increase, people will have more individual time and more hobbies. As a result of the study conducted by BCG, the potential impact of job growth by occupational and industry groups by 2025 is shown in Figure 6 (Lueth, 2015). According to the results, while the increase in professions related to R&D and IT can be seen, a decline in the manufacturing labor force is expected. Figure 6. The potential impact of job growth by occupational and industry groups by 2025 (Lueth, 2015)

Considering that one of the most important society related issues is education, the impact of Industry 4.0 on education should be mentioned in this section. Penprase (2018) emphasized that some changes are required in the science and technology curriculum of higher education in order to make the students more skilled in the rapidly emerging areas of genomics, data science, AI, robotics, and nanomaterials. Besides analyzing and breaking a technical or scientific problem into its constituent parts, the interconnections between each scientific problem across global scales and interrelations between physical, chemical, biological and economic dimensions of a problem must be taken into account in the higher education.

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DISCUSSION The developments in the Industry 4.0 revolution have raised numerous discussions. Some of the major questions are how this technology can be used effectively by society and about technological unemployment. The first main subject is how technology of Industry 4.0 can be used effectively by society. In fact, the Society 5.0 put forward by Japan has been raised to answer this question. As technology develops; at the same time, society must also adapt to this technology. In the other words, technology producers should turn technology into the condition that can be used by society. The second main subject is technological unemployment. As mentioned above, there are three different opinions about the technological unemployment. The first is that the technological unemployment will be occur, the second is that it will be occur temporarily, the third is that it will not be occur. But; the general expectation is that it will be occur temporarily. Because in previous industrial revolutions, it was only temporary; after a certain period, people have turned to new professions. Development leads to improvement, and this become a transformation. Adaptation to new professions will not happen suddenly, people will need a certain time to learn about these professions and to get used to it. In addition to this discussion; Schwab, mentioned that the technological developments in the field of biology are of concern in his book he wrote. (Schwab, 2016) He worries about that we will use this developing technology only to repair our wounds, or we will use it to make ourselves a better person. In second case, personality confusion may occur, parental education can be destroyed. Therefore, technology should be managed in such a way that people do not change their personalities. We have expressed concerns on the issues of technology. But it should not be forgotten that this development of the technology in this industry revaluation is very faster, wider and deeper than the other industrial ages. It is possible that this development will be more effective than other ages. So, the new technology change will be effective in every aspect of life. Technology is development that makes the life of people easier if people use it. People can spend more time with them as they adapt to the technology. Thus, the hobbies of people are increasing, and people can specialize in a specific area. This situation makes life easier and happier. However; there are also opinions that suggests otherwise. For example; Professor Stephen Hawking, a scientist, is concerned that artificial intelligence can bring an end to humanity. Because Hawking argued that artificial intelligence could continue to improve itself and even reshape itself. Therefore he thinks that humanity cannot compete this such force. (Cellan,John, 2014). Also, Kolberg and Zühlke (2015) claim that some people are skeptical or even hostile towards the Industry 4.0. There are a lot of people who are afraid that new technology would bring the end of humanity. Abusing of technology is one of the most common and valid reasons that make people think of that way.

CONCLUSION Companies and countries which cannot adapt to Industry 4.0 revolution concept can be left behind by their rivals in both national and international markets. Failure in the competitiveness may affect adversely not only themselves but also the society they are in. Because the development of societies depends on the strategies of the county; to adapt to the technology, to follow the developments should become government policy. Thereby, governments can develop a roadmap that can help them toward the fourth industrial revolution. At the same time, companies should develop both products and production process. 32

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It is clear that using Industry 4.0 technologies bring a range of benefits to companies. However, these technologies require large amounts of investments. So, while making investment decisions, companies should analyze their current status, sector, and expectations carefully. In the process of development, both countries and companies should be in interaction with each other. In addition, it is important for states to develop their own technologies and not to buy them from another country in terms of maintaining development. Although Industry 4.0 revaluation is associated with the technological changes in the manufacturing industry, it has crucial effects on society. Although there are worries that the technologies used in production will bring about problems that will negatively affect the society, such as unemployment, it is also important to consider that new business models will emerge, innovations have to be applied in the field of education in order to equip the workforce with the skills required by this concept.

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Shiroishi, Y., Uchiyama, K., & Suzuki, N. (2018). Society 5.0: For Human Security and Well-Being. Computer, 51(7), 91–95. doi:10.1109/MC.2018.3011041 Shrouf, F., Ordieres, J., & Miragliotta, G. (2014, December). Smart factories in Industry 4.0: A review of the concept and of energy management approached in production based on the Internet of Things paradigm. In Industrial Engineering and Engineering Management (IEEM), 2014 IEEE International Conference on (pp. 697-701). IEEE. Stock, T., & Seliger, G. (2016). Opportunities of sustainable manufacturing in industry 4.0. Procedia Cirp, 40, 536–541. doi:10.1016/j.procir.2016.01.129 Sun, C. (2012). Application of RFID technology for logistics on internet of things. AASRI Procedia, 1, 106–111. doi:10.1016/j.aasri.2012.06.019 Sung, T. K. (2018). Industry 4.0: A Korea perspective. Technological Forecasting and Social Change, 132, 40–45. doi:10.1016/j.techfore.2017.11.005 Unay, C. (2001). Makro Ekonomi. Bursa: Vipaş. Van Krevelen, D. W. F., & Poelman, R. (2010). A survey of augmented reality technologies, applications and limitations. International Journal of Virtual Reality, 9(2), 1. Virtanen, M., Kivimäki, M., Joensuu, M., Virtanen, P., Elovainio, M., & Vahtera, J. (2005). Temporary employment and health: A review. International Journal of Epidemiology, 34(3), 610–622. doi:10.1093/ ije/dyi024 PMID:15737968 Walsh, T. (2017). Expert and Non-Expert Opinion about Technological Unemployment. International Journal of Automation and Computing, 1-6. Web of Science. (n.d.). Retrieved from www.webofknowledge.com Zhou, K., Liu, T., & Zhou, L. (2015, August). Industry 4.0: Towards future industrial opportunities and challenges. In Fuzzy Systems and Knowledge Discovery (FSKD), 2015 12th International Conference on (pp. 2147-2152). IEEE.

This research was previously published in Critical Issues Impacting Science, Technology, Society (STS), and Our Future; pages 1-20, copyright year 2019 by Information Science Reference (an imprint of IGI Global).

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The 4th Industrial Revolution: Business Models and Evidence From the Field Carlo Bagnoli Università Ca’ Foscari, Venice, Italy Francesca Dal Mas https://orcid.org/0000-0001-6477-4177 Università degli Studi di Roma La Sapienza, Rome, Italy Maurizio Massaro Università Ca’ Foscari, Venice, Italy

ABSTRACT The objective of this article is to analyze the impact of Industry 4.0 on business models considering technological change as a driver of strategic innovation. The research aims to provide the key to interpreting a process of innovation that, starting from the technological transformation, translates it into a broader change of business models. A structured literature review has been developed analyzing 144 sources divided into scientific papers, reports from consultancy firms and institutional reports. This method identified the importance given by the literature to the technologies and their impact on the building blocks of the business model. The research has led to the identification of 12 business models that can represent a framework to interpret the Industry 4.0 phenomenon strategically. A questionnaire analysis of a sample of 111 companies based in Italy allowed us to compare the results of theoretical research with the perceptions of Italian entrepreneurs.

1. INTRODUCTION The concepts of “Innovation” and “Strategy” have become the fundamental themes of two rich areas of study in the nineties (Schlegelmilch et al., 2003). The literature focused on strategy defines the way to compete within a specific market sector and outlines the field of action of the organization through the DOI: 10.4018/978-1-7998-8548-1.ch003

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choices needed to achieve a long-term or overall aim. On the other hand, the literature on innovation has focused its attention on the level of product and process innovation (Schlegelmilch et al., 2003). Therefore, while the literature on strategy focuses on the overall aims of the organization, the concept of innovation, until the mid-nineties, was never used at the enterprise level. The different focus of innovation and strategy are stimulating, especially considering the intrinsic nature of the term innovation. Indeed, innovation represents the ability to think and to practice new or better ways of doing things, and thus represents an exceptional mechanism, capable of unleashing the creative spirit. Innovation can be the trigger for opening the mind to possibilities that were previously unknown, leading to progress in areas essential for human development. Therefore, innovation leads to very demanding challenges but also extraordinary opportunities for companies pushing traditional approaches focused on product and process under a great pressure to expand their horizons (Porter, 1996). Because of the need to create a more comprehensive approach on innovation, from the nineties, the concepts of innovation and strategy have started to become more linked, thanks to the introduction of the concept of strategic innovation (Schlegelmilch et al., 2003). Strategic innovation consists in the development of a new concept (and therefore a model) of business, namely: new products or services, presented or combined in a new way, to create a radically new experience for clients, involving them also at an emotional level. Strategic innovation can also arise from the reconfiguration of the sector’s value chain to change the rules of the game - exploiting, for example, the possibilities offered by new technologies to reach the final customer directly to enhance the distinctive competencies of the company (Buaron, 1981). From that point on the literature agreed that strategic innovation goes beyond the simple adjustment of the current business strategy. Indeed, strategic innovation requires extensive changes both at the level of the structure and at the level of business processes. Therefore, it becomes necessary especially for companies anchored to “traditional” business models (BM) that are resistant to strategic change (Spender, 1989). The phenomenon of the new industrial revolution (Industry 4.0) has contributed to enhancing the complexity of the topic. Given the new technological challenges and the impact on innovation provided by Industry 4.0, this study employs a structured literature review (SLR) approach according to Massaro et al. (2018) to understand how Industry 4.0 is changing companies’ BM providing new opportunities and challenges. The analysis refers to 144 selected documents such as journal papers, books, reports, to understand the impact of Industry 4.0’s technologies on BM. A questionnaire analysis of a sample of 111 companies based in Italy has been carried on to double check the results of theoretical research with the perceptions of Italian entrepreneurs. The paper is novel since it starts from a literature review of recent and various sources to verify the impact of new technologies on BM. The results allow identifying which BM seem to be the more successful, and which technologies seem to have the power to influence the current BM. The results of the survey allow verifying what appears to be the state of the art for Italian entrepreneurs. The paper is structured as follow. The first paragraph explains the literature review, and it is followed by the research methodology. Results and conclusions end the paper.

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2. LITERATURE REVIEW 2.1. Strategic Innovation and the BM Canvas BMs represent the underlying logic of how the company is doing business, creates value for stakeholders and captures a share of value for itself (Biloslavo et al., 2018; Bagnoli et al., 2018 (a); Bagnoli et al., 2018 (b); Nielsen et al., 2018; Nielsen et al., 2019). The business model “Canvas” is a strategic tool that uses visual language to create and develop innovative BM. It represents the way in which a company generates, distributes, and captures value. The value offered is the reason for customers to choose a company rather than a different one by solving a customer issue or meeting their needs. Each value consists of a selected set of products or services that fits the requirements of a specific customer segment. Some value propositions can be innovative and represent a new or disruptive offer, others may be similar to the ones existing on the market, but with additional features and attributes. The framework adopted in our study is the one by Biloslavo et al. (2018), which consists of a reworking of the famous model of Osterwalder and Pigneur. The model starts from a triangular figure that can be “open,” thus configuring a direct and straightforward visual reference scheme, suitable for a lean but complete representation of all the eight elements of the business model: suppliers, resources, internal processes, external processes, products, customers, society, and value proposition. Figure 1. Our framework (Source: Biloslavo et al., 2018)

2.2. Strategic Innovation and Industry 4.0 The growing attention on the role BM canvas follows the development of the literature on innovation. Scholars distinguish three different sources of strategic innovation: Technology Push, Market Pull, and design-driven (Verganti, 2011). These three different types of change start from different assumptions and therefore lead to equally mixed results.

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The technology push innovation derives from the exploration of new technological possibilities by the organization. Typically, they lead to radical innovations in technical and technological terms and changes in consumer needs. According to Schumpeter, the company imposes “change,” and the consumer is instrumental to the fact that the activity of the producer is successful (Schumpeter, 1971). The company, therefore, moves independently, and the introduction of innovation does not take place according to the needs of consumers. In this way, the firm “educates” its consumer, to push him/her to let the new products be included among his/her preferences. Strategic innovation, however, cannot derive only from a technology push innovation. Change cannot focus only on the increase in technical or technological characteristics, without considering the needs of the client (Verganti, 2008). Market pull innovation, on the other hand, originates from an understanding of the needs of customers or end users, or direct requests from the market. It usually starts with the analysis of the clients/users’ needs, and with the following search for technologies that can satisfy them in a better way. These innovations are purely incremental because the market and the customers are rarely able to express needs that go beyond their usual consumption experience (Verganti, 2008). Design-driven innovation is not a technological innovation, nor it does derive from the needs expressed by the market, but it is an innovation of meaning. This type of change arises from the exploration and understanding of existing and future trends in socio-cultural models and offers new visions, new concepts and radically new senses to existing products or services and therefore acts on potential needs or emotional and symbolic aspects. These are, therefore, innovations pushed by the vision of the company regarding the possible changes in meanings and languages that could emerge in the future (Verganti, 2008) and not from current customer needs. Design-driven innovations can be both radical and incremental. They can bring about a change in language, which in turn determines the message transmitted to the client and therefore the meaning of the products or services offered, only partially or different from that existing in the current socio-cultural models. According to Biloslavo et al. (2018), design-driven innovations are the only way for companies already present in the market to renew their position of success. At the same time, they represent a way for new entrants to overcome the significant disadvantages compared to companies already present in the market. Interestingly, the development of the innovation called “Industry 4.0” is providing new sources of innovations that draw on all these characteristics, presenting new challenges for changing companies’ BM. Indeed, Industry 4.0 leads to a digital transformation, which takes the form of interconnected systems able to interact with each other and to collect and analyze data to adapt to changes. It disrupts the value chain, and for this reason, companies must not limit themselves to a technological analysis of transformation but are forced to rethink their BM, their way of working to create value for their customers.

3. RESEARCH METHODOLOGY This paper employs an SLR (Massaro, Dumay, et al., 2016). Conducting an SLR “can help experienced scholars develop new and interesting research paths by accessing and analyzing a considerable volume of scholarly work” (Massaro, Dumay, et al., 2016). Additionally, Massaro et al. (2016) state that an SLR can “contribute to developing research paths and questions by providing a foundation” for future investigation. Interestingly, SLRs seem to provide an alternative to more ‘traditional’ literature reviews, to reach more

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“defensible” and “replicable” results. This approach has already been used to investigate interdisciplinary fields of accounting, auditing and accountability (Guthrie and Parker, 2011), Knowledge Management in the Public Sector (Massaro et al., 2015), Knowledge Management in Small and Medium Enterprises (Massaro, Handley, et al., 2016), organizational knowledge protection (Manhart and Thalmann, 2015), human capital accounting (Guthrie et al., 2012; Guthrie and Murthy, 2009), the use of content analysis (Dumay and Cai, 2014), Integrated Reporting (Bernardi et al., 2014; Dumay et al., 2016) and Intellectual Capital (IC) (Dumay, 2014). Figure 1 depicts the model described by Massaro et al. (2013). Figure 2. SLR methodology (Source: Massaro et al., 2016)

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Following Massaro et al. (2016) approach, first, we developed a Research Protocol describing the steps shaping an SLR (Figure 2). A total number of 140 documents divided into journal articles, consulting reports, institutional reports and other sources have been searched using keyword searches in databases such as Scopus, Ebsco, Google Scholar and professional or institutional websites focused on the topic of Industry 4.0 (e.g. a specific section of the Economic Ministry of Italy). More than 18,770 references have been coded in 162 nodes using an open coding approach (Miles et al., 2013). The inquiry was developed around two main research questions: RQ1: How does Industry 4.0 affect existing BM? RQ2: How could Industry 4.0 lead to the development of new BMs? Considering that we employed an open coding approach, they could not apply validity measures such as the Krippendorff’s alpha. To ensure validity, results were discussed by whole the research team to ensure consistency of the coding approach. Additionally, word searches were employed to ensure that relevant nodes were not missed or underestimated. The following sections describe the findings of the research.

4. RESULTS 4.1. Impact of Industry 4.0 to Existing BMs This section depicts the results of the SLR focusing on the first research question: how does Industry 4.0 affect existing BM? To answer the first research question we analyzed current definitions provided by the authors. According to (Schumacher et al., 2016, p. 162) “Industry 4.0 refers to recent technological advances where the internet and supporting technologies (e.g., embedded systems) serve as a backbone to integrate physical objects, human actors, intelligent machines, production lines and processes across organizational boundaries to form a new kind of intelligent, networked and agile value chain.” Main technologies considered within the concept of Industry 4.0 are depicted in Figure 3. To better focus the research, we analyzed how these technologies are described regarding their impact on the company’s BM. Results of this analysis are depicted in the following Figure 4. The figure shows the synthesis of the nine heatmaps referring to each enabling technology. The intensity of the color of each building block in the heatmaps is associated with the importance recognized by the literature to the change due to the development of the technology. The literature focuses its attention on the internal part of the company and, in particular, on internal processes and resources, interpreting Industry 4.0 as a tool to improve productivity and efficiency of the methods. However, we could find a keen interest in the literature towards customers and their increasingly central and collaborative role in the value chain and also on products, which are now more innovative and full of smart functionality. There is an openness towards elements of the business model addressed to the outside, such as customers, products, external processes of the company and society. On the other hand, literature seems to neglect the impact of technologies on the value proposition, adopting a more operative perspective. The following tables (Table 1a and Table 1b) provide more details describing how each technology can affect each building block shaping the company’s BM.

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Figure 3. Industry 4.0 technologies (Source: Bagnoli et al., 2018a)

Figure 4. Industry 4.0 technologies heatmap according to the literature (Source: Our elaboration)

4.2. Impact of Industry 4.0 to the Development of New BMs Findings of the SLR show that Industry 4.0 allows the development of new BM starting a strategic innovation that creates new market spaces through a unique value proposition. Findings allowed us to identify twelve new BM characterized by innovative value propositions, thanks to the new technological opportunities provided by the Industry 4.0. Finally, we were able to group the 12 new business models into four categories, namely: Mass customization BM, data & analytics BM, as a service BM and platform BM. Each group is described in the following subsections.

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4.2.1. Mass Customization BMs In the category “Mass Customization” we can identify specific BMs that work on the value discipline of Operational Excellence (Treacy and Wiersema, 1993). Technology provided by the Industry 4.0 allows transforming traditional paradoxes such as “man vs. machine,” “profit vs. sustainability,” “craft production vs. industrial production” and “knowledge exploration vs. knowledge exploitation.” Value production plays a central role for Industry 4.0, as it involves integrating the product with all the actors in the value chain, as it acts as an interconnection tool. This connection lays the foundation for cyber-physical systems, intelligent networks of machines, ICT systems, products, and people. In this context, the production of value is automated and dematerialized and makes it possible to combine large-scale production with customization, thus moving to a more dynamic and on-demand approach, increasing at the same time efficiency and productivity. Therefore, BM is set up for the production of high-tech goods that use increasingly sophisticated materials and can adapt dynamically to changing market conditions. This adaptation is also favored by the proximity to the customers, which allows to fully understand the clients’ needs by establishing a proximity relationship both in a physical and virtual sense. The products are so unique and customized, being designed directly to the requests of the final consumer. The following table depicts main impacts of Industry 4.0 on the BM of Mass Customization. The color in Table 2 describes the magnitude of the effect.

4.2.2. Servitization BM Services are taking an increasingly central position, allowing the creation of valuable proposals based on the combination of services and products, integrated through technologies. The digital transformation is offering the possibility of creating BMs based on the provision of services, and increasingly customeroriented. The services allow to combine the virtual world with the physical one, and to set up new profit models such as performance-based contracts, product-as-a-service, pay-per-use, subscription-based and machine-as-a contracts -service. The new BM of the “servitization” category try to identify the “why” behind the need to purchase and to respond to this by transforming products into services. In fact, the logic of these models lies in the fact that the value is not represented only by the product itself, but by what is made possible through its use. The service thus becomes the very foundation of the exchange and provide the base of the value discipline of product/service leadership (Treacy and Wiersema, 1993). The following Table 3 depicts main impacts of Industry 4.0 on the BM of Servitization. The color in the table describes the magnitude of the effect.

4.2.3. Data-Driven BM The exploitation of the value generated by the data obtained and the recognition of the centrality of the client lead to the development of new BM based on big data & analytics technologies. The data represent the core driver of innovation and competition, as they are necessary to achieve leadership positions in the creation of value.

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Table 1. Results of the literature review on the impact of industry 4.0 on company’s BM

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Table 2. Mass customization BM and the impacts of industry 4.0 on the BM building blocks

Table 3. Servitization BM and the impacts of industry 4.0 on the BM building blocks

These BM allow the development of innovative methods for the collection and use of data, and to benefit from the value embedded in information. The data make it possible to increase product functionality but can also be exploited as a product to obtain incremental revenues. The information collected is used by companies for production and commercial purposes, and this requires attention to the legal profiles of their use in the private sphere, but also about the price policies. Therefore, Data-driven BM allow the development of a new perspective on the customer intimacy value discipline is reducing barriers to reach customers and customize products (Treacy and Wiersema, 1993). The following Table 4 depicts main impacts of Industry 4.0 on the Data-driven BM. The color in the table describes the magnitude of the effect. 46

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Table 4. Data-driven BM and the impacts of industry 4.0 on the BM building blocks

4.2.4. Platform BM The distribution of value is undergoing significant changes thanks to technologies that reduce the distance between the company and the customer through a deeper understanding of the latter’s needs. The proximity to the customer is a crucial feature of the distribution BM. It requires the development of platforms, which support the interoperability between various actors of the value chain, based on the shared representation and updated in real time, configured on the specific needs of the user. These platforms make it possible to co-create value among networks, to share experiences and meanings, because they facilitate the exchange of data and services among the actors of the ecosystem. The following Table 5 depicts main impacts of Industry 4.0 on the Data-driven BM. The color in the table describes the magnitude of the effect. Table 5. Platform BM and the impacts of industry 4.0 on the BM building blocks

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4.2.5. Evidence From the Field To better understand the entrepreneurs’ vision of the opportunities, risks and practical feasibility of Industry 4.0, an exploratory survey was carried out in collaboration with KPMG. The questionnaire was managed by KPMG in May 2017, and its results were presented at the event “Biennale Innovazione” organized by the Ca ‘Foscari University of Venice in June 2017. The representatives of 111 companies belonging to different sectors were involved. The sample involved companies from all over Italy. However, the most significant part is represented by organizations from the North of the country, in particular from Veneto, Friuli-Venezia Giulia, Trentino-Alto Adige, Lombardy, and Emilia-Romagna. The sectors involved included iron and steel, engineering, pharmaceuticals, textiles, automation, food, and beverages. Both companies operating in the B2B industry, as well as B2C, were considered. The survey was also extended to technology providers with the aim of studying a different point of view. The questions were oriented towards understanding the opportunities and benefits that the technologies could offer. The questionnaire consisted of 24 multiple-choice questions which themes can be traced to two different main sections. The first section included issues related to a broader context, that allowed to understand the more general view of companies on the impacts of Industry 4.0 both regarding opportunities, as well as the obstacles that can block the implementation. The section was further divided into two parts: the country system and the industrial system that allowed to draw a complete picture of the impact of Industry 4.0 in the external context, as perceived by the companies. In particular, the first section, entitled “The external context: the country system and the industrial sector”, recorded the opportunities and benefits that companies expected from investments in Industry 4.0 about the dynamics of their sectoral and government policies. Besides, it sought to understand the main obstacles to the development of industry 4.0 by companies and their perception of the political and competitive aspects of their sector. The second section specified the vision on the impact in the internal context, the organizational one, that an investment in Industry 4.0 can have on the strategic choices of the companies. Specifically, in the second section, “Internal context: technological feasibility and strategic opportunities,” the questions were about the changes that companies are observing on their business model and what human and intellectual resources and technologies are necessary to implement innovative strategies and actions. In this sense, we aim to compare the vision on the phenomenon of Industry 4.0 and Italian business, evaluating the Italian manufacturing point of view compared to the international one given by the literature. The results of the questionnaire showed that the Italian companies seem not to be yet fully aware of the phenomenon and are primarily behind the application of the technologies and skills necessary to use them. The business models developed in our research can be used by companies. An example can be the attention to the personalization of products as an emerging trend, as shown by the study. The use of technologies allows improving the quality of production processes and outputs without a spending much money, allowing companies to invest in the personalization of products and, therefore, in a new experience for the consumer. This is made possible by additive manufacturing technologies and by the use of robots that allow refining the product and, therefore, guarantee the highest quality to the mass market (mass customization) at reduced costs, thus conquering new markets. The challenge of the Industry 4.0 can be grasped by the Italian companies trying to find an original synthesis between the humanistic culture that is at the base of the success of the “Made in Italy” and the technical culture that the new technologies in some ways impose. Paradoxically, the fourth industrial revolution will bring back to life craftsmanship, linked to the ability to experiment with innovative solutions without losing sight of their cultural significance. Main results are depicted in the following Table 6. 48

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The internal context: technological feasibility and strategic opportunities

The external context: the country system and the industrial sector

Table 6. Industry 4.0 challenges as seen by entrepreneurs Industry 4.0 as an opportunity to change

The majority of firms thinks that Industry 4.0. represent an opportunity to change and enhance the Italian manufacturing industry

Factors that foster the possibility to invest in Industry 4.0

The majority of firms thinks that human resources are the key enablers of Industry 4.0

Obstacles or threats that limit the possibility to invest in Industry 4.0

The majority of firms thinks that organizational culture is the major obstacle

Ability of the “Made in Italy” industry to fully embrace Industry 4.0 technologies

The majority of firms thinks that Italy will face several difficulties in embracing Industry 4.0 technologies

Most effective industrial policies to enhance Industry 4.0

The majority of firms thinks that infrastructural investments are more effective than fiscal benefits

Ability of Industry 4.0 to renew the current production models

The majority of firms agrees that Industry 4.0 can help to renew the current production models

Competitive benefits from Industry 4.0

The majority of firms thinks that data from Industry 4.0 technologies can help fostering competitive advantage

Occupational repercussions from Industry 4.0

The majority of firms thinks that Industry 4.0 will not decrease the number of employees, however, several of them will change their role

Ability of Italian competitors to activate Industry 4.0 investments

The majority of firms thinks that their Italian competitors have not yet invested in Industry 4.0 technologies

Ability of European competitors to activate Industry 4.0 investments

The majority of firms thinks that their European competitors have not yet invested in Industry 4.0 technologies

Ability of Extra-European competitors to activate Industry 4.0 investments

The majority of firms thinks that their extra European competitors have not yet invested in Industry 4.0 technologies

Ability of the firm compared to its competitors

The majority of firms are not yet able to define their positioning compared to their competitors

Path to integrate Industry 4.0 with existing technologies/operations

The majority of firms still needs to allocate a budget for this purpose

Internal agents to enhance Industry 4.0

The majority of firms will rely on their CEOs

External agents to enhance Industry 4.0

The majority of firms will rely on consultancy firms more than technology providers and universities

Impact on BMs

The majority of firms thinks that resources and products will the most affected building blocks

Expected general benefits from Industry 4.0

The majority of firms thinks that Industry 4.0 will allow them to better react to changes

Presence of enough internal skills to use Industry 4.0 tools properly

The majority of firms thinks that internal skills should be improved to use Industry 4.0 tools properly

Skills to be enhanced

The majority of firms thinks that organizational skills are the ones to be improved

Ways of reducing skills gaps

The majority of firms is likely to use external sources, however internal classes/training are considered as well

Areas of investments from Industry 4.0

The majority of firms is likely to invest more in Data Analytics

Expected internal benefits from Industry 4.0

The majority of firms expect innovation in managing their industrial processes

Presence of a specific budget devoted on Industry 4.0

The majority of firms has not defined a budget yet

Future investments on Industry 4.0

The majority of firms will invest between 5 and 10% of their budget

Source: KPMG/Ca’ Foscari

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5. CONCLUSION Our research develops a Structured Literature Review to answer two main research questions related to the definition of Industry 4.0 and its role to promote the development of new Business Models. Results show that Industry 4.0 is a complex topic shaped mainly from nine different technologies. Each technology has specific impacts on a company’s BM. Interestingly, while most of the impacts of Industry 4.0 focus on the production building blocks of the BM, there are significant and interesting effects also on other dimensions such as customers, external processes, and society. A questionnaire was then conducted among 111 Italian companies from a variety of industrial sectors to double check the status of Industry 4.0 in Italy. Results clearly show that Italian companies seem not to be yet fully aware of the phenomenon and are behind the application of the technologies and the necessary skills. The paper is novel compared to previous studies since it tries to understand and map the literature to detect new successful BM, starting from the phenomenon of Industry 4.0. Our findings show that when more technologies are combined they allow the development of new BM. New ways of developing relationships with customers, suppliers and other stakeholders are pushing new approaches to deal with the relational capital and the knowledge management processes. Additionally, new knowledge-based products can be developed thanks to Industry 4.0, and new approaches can be defined to deal with the whole society more sustainably. Results depict four main typologies of BM named: mass customization BM, Servitization BM, Data-driven BM, Platform BM and show how each BM uses innovations provided by the Industry 4.0 to review traditional building blocks. These new BM could be successfully implemented by firms and organizations. The paper has several limitations. The survey was conducted only in Italy, and it reflects the situation and feelings of Italian entrepreneurs. Further researches could start from the results of this literature review providing concrete cases of companies that used the opportunities provided by the Industry 4.0 to develop new Business Models. In addition, the analysis of the questionnaire should be enlarged to other countries or contexts to better verify the results.

REFERENCES Bagnoli, C., Bravin, A., Massaro, M., & Vignotto, A. (2018), Business Model 4.0. I modelli di business vincenti per le imprese italiane nella quarta rivoluzione industriale. Venezia: Edizioni Ca‘ Foscari. Bagnoli, C., Garlatti, A., Massaro, M., Dal Mas, F., & Paschetto, M. (2018), Winning Business Models for the 4th Industrial Revolution. In Proceedings of the International Conference Theory and Applications in the Knowledge Economy (pp. 60-75). Biloslavo, R., Bagnoli, C., & Edgar, D. (2018). An eco-critical perspective on business models: The value triangle as an approach to closing the sustainability gap. Journal of Cleaner Production, 174, 746–762. doi:10.1016/j.jclepro.2017.10.281 Buaron, R. (1981), New-game strategies, The McKinsey Quarterly, Spring(1), 24–41. Dumay, J. (2014). 15 years of the Journal of Intellectual Capital and counting: A manifesto for transformational IC research. Journal of Intellectual Capital, 15(1), 2–37. doi:10.1108/JIC-09-2013-0098

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Dumay, J., Bernardi, C., Guthrie, J., & Demartini, P. (2016). Integrated reporting: A structured literature review. Accounting Forum, 40(3), 166–185. doi:10.1016/j.accfor.2016.06.001 Dumay, J., & Cai, L. (2014). A review and critique of content analysis as a methodology for inquiring into IC disclosure. Journal of Intellectual Capital, 15(2), 264–290. doi:10.1108/JIC-01-2014-0010 Guthrie, J., & Murthy, V. (2009). Past, present and possible future developments in human capital accounting. Journal of Human Resource Costing & Accounting, 13(2), 125–142. doi:10.1108/14013380910968647 Guthrie, J., & Parker, L. D. (2011). Reflections and projections 25 years of interdisciplinary perspectives on accounting, auditing and accountability research. Accounting, Auditing & Accountability Journal, 25(1), 6–26. doi:10.1108/09513571211196829 Guthrie, J., Ricceri, F., & Dumay, J. (2012). Reflections and projections: A decade of Intellectual Capital Accounting Research. The British Accounting Review, 44(2), 68–82. doi:10.1016/j.bar.2012.03.004 Manhart, M., & Thalmann, S. (2015). Protecting organizational knowledge: A structured literature review. Journal of Knowledge Management, 19(2), 190–211. doi:10.1108/JKM-05-2014-0198 Massaro, M., Dumay, J., & Garlatti, A. (2015). Public sector knowledge management: A structured literature review. Journal of Knowledge Management, 19(3), 530–558. doi:10.1108/JKM-11-2014-0466 Massaro, M., Dumay, J. C., & Guthrie, J. (2016). On the shoulders of giants: Undertaking a structured literature review in accounting. Accounting, Auditing & Accountability Journal, 29(5), 767–901. doi:10.1108/AAAJ-01-2015-1939 Massaro, M., Handley, K., Bagnoli, C., & Dumay, J. (2016). Knowledge Management in Small and Medium Enterprises. A structured literature review. Journal of Knowledge Management, 20(2), 258–291. doi:10.1108/JKM-08-2015-0320 Miles, M. B. Huberman, A. M. & Saldana, J. (2013). Qualitative Data Analysis: A Methods Sourcebook. Thousand Oaks, CA: Sage Publications Nielsen, C., Lund, M., Montemari, M., Paolone, F., Massaro, M., & Dumay, J. (2019). Business Models. A research overview. London: Routledge. Nielsen, C., Lund, M., Thomsen, P., Brøndum, K., Sort, J., Byrge C., … Dumay, J. (2018), Depicting A Performative Research Agenda: The 4th Stage Of Business Model Research. Journal of Business Models, 6(2), 59–64. Porter, M. E. (1996). What is Strategy? Harvard Business Review, 74(6), 61–78. PMID:10158474 Schlegelmilch, B. B., Diamantopoulos, A., & Kreuz, P. (2003). Strategic innovation: The construct, its drivers and its strategic outcomes. Journal of Strategic Marketing, 11(2), 117–132. doi:10.1080/0965254032000102948 Schumacher, A., Erol, S., & Sihn, W. (2016). A Maturity Model for Assessing Industry 4.0 Readiness and Maturity of Manufacturing Enterprises. Procedia CIRP, 52, 161–166. doi:10.1016/j.procir.2016.07.040 Schumpeter, J. (1971). Teoria dello sviluppo economico. Firenze: Sansoni Editore.

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Spender, J. C. (1989). Industry Recipes: The Nature and Sources of Managerial Judgement. Oxford: Blackwell. Treacy, M., & Wiersema, F. (1993). Customer Intimacy and Other Value Disciplines Customer Intimacy and Other Value Disciplines. Harvard Business Review, 71(9301), 84–93. Verganti, R. (2008). Design, meanings and radical innovation: A meta-model and a research agenda. Journal of Product Innovation Management, 25(5), 436–456. doi:10.1111/j.1540-5885.2008.00313.x Verganti, R. (2011). Innovazione di prodotto e sviluppo delle imprese. In Methodologies and Technologis for Networked Enterprises. New York: Springer.

This research was previously published in the International Journal of E-Services and Mobile Applications (IJESMA), 11(3); pages 34-47, copyright year 2019 by IGI Publishing (an imprint of IGI Global).

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Chapter 4

Process Excellence and Industry 4.0 Felipe Martinez Independent Researcher, Czech Republic

ABSTRACT The process excellence discipline comprises several philosophies, approaches, and techniques with different headings and origins. The history of the industrial revolutions is related with the beginnings and development of these process excellence approaches. Therefore, the aim of this chapter is to explore the evolution of process excellence throughout these four industrial revolutions in order to propose recommendations to successfully cope this new revolution from the process excellence perspective. The chapter reviews publications about the history of the industrial revolutions, technologies of the fourth industrial revolution, and it includes the outputs from a roundtable with Industry 4.0 experts. The chapter concludes that the concept of process and process excellence is essential to implement this new revolution and that the current practices of process excellence most be review in with the perspective of Industry 4.0.

INTRODUCTION Process excellence involves several philosophies, approaches and techniques such as Quality Management System (QMS), Toyota Production System (TPS), Theory of Constraints (TOC), Business Processes Reengineering (BPR), Lean Management (LM), Six Sigma (SS) and much more. Regardless of the heading or origin, all of them aim to improve organizations by exploring their processes and determining specific changes. The history of these approaches relates with the industrial revolutions. Each revolution brings into the scene new characteristics and aspects that influence these process excellence approaches and consequently, it is expected similar scenario from this current revolution. Therefore, the aim of this chapter is to explore the evolution of process excellence throughout these four industrial revolutions. The chapter explores milestones in the process excellence philosophy, techniques and tools. The chapter proposes recommendations to cope this new revolution from the process excellence perspective. DOI: 10.4018/978-1-7998-8548-1.ch004

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 Process Excellence and Industry 4.0

The chapter reviews publications in the topic to determine theoretical perspective. This includes the outputs of professional and academic conferences on the topic and the outputs from a roundtable with industry 4.0 experts (Martinez, Jirsak, & Lorenc, 2017). The main conclusions of the chapter are that the concept of process is essential to implement i4.0 initiatives and that the industry 4.0 provides process excellence methodologies with new aspects that enhance its activities and therefore the organizational improvement.

PROCESS EXCELLENCE AND ITS METHODOLOGIES The relationship between i4.0 and process excellence has little presence in the current literature. It relates with the technological aspects, but it has less attention to processes excellence. The analysis of abstracts of i4.0 related papers shows some keyword occurrence of main process excellence concepts such as process (40%), Value (8%), Waste (1%) and Continuous Improvement (18%) but it illustrates low correlation with i4.0 keywords within these papers (Martinez, Jirsak, & Lorenc, 2016). Therefore, this chapter focuses on the comparison of the process excellence approaches throughout the four industrial revolutions. This timeline perspective contributes to determine the importance of process excellence to develop i4.0. Processes are inherent to organizational performance. The pursuit of process excellence belongs to the strategy and management of organizations that continually seek to achieve better results. Early organizational theories introduce the importance of time in processes to manage and control organizations (Taylor, 1911). Henry Fayol publishes in 1916 “the administration theory” which summarizes fourteen principles of management. This also includes activities to achieve goals (Fayol, 1967). The creation of Ford Motor company in 1903 (Henry Ford Heritage Association, 2017) utilizes the manufacture model of Henry Ford (1863-1947) which increases process productivity with mass production. Contemporary models also include processes among their elements. The “7S” model contains the organizational processes in the element “systems” (Peters & Waterman, 1982). The mechanisms for collaboration and the workflow determine the element “processes” in the “Star Model” (Galbraith & Kates, 2008, p. 17). The “Six Box” model (Weisbord, 1987, p. 9) present similar element: “Helpful Mechanism”. The congruence model observes the entire organization from a process approach (Nadler & Tushman, 1980). Other models such as “Holonic Enterprise” (Ulieru & Este, 2004) and “Fractal Web” (McMillan, 2004, p. 170) also employ elements of the process approach. These different organizational performance approaches develop several definitions and understandings of the concept of process in organizations. The mechanisms to achieve goals; activities to perform; interconnected activities that allow the functioning of the organization; time measuring for performance and others are attempts to define processes in organizations. Contemporary definitions of the term involve these elements. Hammer, et al. (1993) defines process as a collection of activities that takes one or more kinds of input and creates an output that is of value to the customer. Similar, Rosing, et al. (2015) understand the concept of process as a collection of interrelated task and activities that are initiated in response to an event which aims to achieve a specific result for the consumer of the process.

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Moreover, the International Standards Association (ISO) presents a general and specific definition of process that combines all these elements in a simple sentence: A set of interrelated or interacting activities, which transforms inputs into outputs, it has a process owner and it has process parameters that allow measure its performance (ISO, 2015). Figure 1. Process definition

Source: Author’s own work from the definition of process ISO 9001:2015 (ISO, 2015)

These five elements (Outputs, Activities, Inputs, Owner and Parameters) summarize the definition of process (Figure 1). The Outputs are the most important element of the definition. They align the other elements. The Outputs contains customer’s requirements such as amount of units, delivery time, design, etc. In other words, Outputs are what the customer needs. The understanding of the outputs determines the necessary Activities to obtain the customer’s requirements. This comprise manufacture, services, marketing, finance and other tasks that in combination and synchronization create the desire output for the customer. The element Inputs refers to all material, tools, people and others necessary to perform the Activities. The process Owner is responsible for the correct functioning of the process. This person knows the process and knows how it works. Process parameters include time and other factors that allow the process Owner to manage and improve the process. The understanding of these elements facilitates the venture to pursuit process excellence. The definition of the word Excellence stands: The quality of being outstanding or extremely good (Oxford Dictionaries, 2017). This definition together with the ISO9001:2015 definition of process declare that process excellence is outperforming set of activities, which transforms inputs into outstanding outputs for the customer. The Output is the result of outperforming set of activities that exceptionally coordinate different inputs. It has a brilliant process Owner that always guarantee the excellent level of performance based on parameters that always provide truthful data. Is not this a utopia? Yes, it is, but the pursuit of excellence is not. Then, process excellence is about to constantly work to be closer to this utopia. This is continuous improvement. The systemic repetitive implementation of tools, techniques and methodologies allow the organization to obtain long-term results (Gonzalez Aleu & Van Aken, 2016). The process excellence discipline contains several approaches. The principles and evolution of these approaches are very similar. Some authors include some of the approaches as tools of the others or as complementary methodologies. This chapter only includes six of them: QMS, TPS, TOC, BPR, LM and SS.

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Quality Management System This approach influences the organizational performance (Kaynak, 2003) and it has great emphasis in the organizational process excellence. The QMS introduces the concept of quality: The degree to which a set of inherent characteristics fulfil requirements (ISO, 2015). The approach aims to develop an organization that fulfils customer’s requirements. QMS follows the PDCA cycle (Plan-Do-Check-Act) first discussed by Walter A. Shewhart (1939) with further development by Deming (1950) who also included the 14 points of Total Quality Management (TQM). The PDCA cycle is one of the main sources to argue continuous improvement in the pursuit of process excellence. The first step, Plan, includes the recognition of an opportunity to change in the organization and the creation of a plan that allows this change. The second phase, Do, realize the change in small-scale study. Then, it is necessary to verify the consequences of the change, Check, and learn from it. The last step, Act, implements the change if the results of the previous step are positive. Nevertheless, if the results are not satisfactory, it is necessary to come back and repeat the cycle from the first step. In both cases, the lessons learnt are input to the next use of the cycle (Tague, 2005). Apart from the PDCA cycle, QMS implements the use of seven tools to achieve quality: The flowchart illustrates a graphical representation of the process as a flow of connected activities or sub processes. Cause-and-effect diagram (also called Ishikawa or fishbone chart) search for the root causes that create a specific problem or situation. The data checklist is a structured form to capture the needed data for a small-scale study or measurement. Control charts illustrate the changes over the time of a certain process parameter. Histogram is a statistical graphical tool that shows the occurrence of each different value within a data set. Pareto chart and Lorenz curve facilitate the establishment of priority factors. Scatter diagram explores the relationship of two variables. Stratification complements the tool when the mean of the data is unclear (Tague, 2005). The ISO9001:2015 is the standard for QMS. The content of the standard includes context, leadership, planning, support, operations, evaluation and improvement (ISO, 2015). Context is about the organizational scope towards quality. This includes the organizational definition, expectations, stakeholders and the QMS documents system. Leadership refers to the activities and actions that the organization does to ensure a culture focused on quality and customers. This embraces the quality policy and the roles and responsibilities for the quality assurance. Planning involves risk management, objectives, plan performance and QMS control. The support element of the standard comprises all the activities and actions in order to provide the QMS with the necessary resources and competent people. Additionally, support explains the involvement of people with the QMS, the QMS communication and its documentation. Operations specify processes, products and services. This involves the development, implementation, control and monitoring of internal and external processes. It also includes the process to design products and services, its requirements, operation management practices, production control and procedures related with nonconforming outputs. The section evaluation concentrates on the practices to monitor, measure, analyze and assess the QMS performance. This includes QMS audit (internal and external) and management reviews. The last element develops improvements that the organization use to detect improvement opportunities, the procedures to reduces the occurrence of nonconformities, and the practices to enhance the effectiveness and efficiency of the QMS (ISO, 2015).

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Toyota Production System Toyota has become a reference of quality and process excellence. The books “The Machine That Changed the World” (Womack, Jones, & Roos, 1990) and “The Toyota Way” (Liker, 2004) have become a reference to the TPS practices. Moreover, the main reference author to the TPS system is Taiichi Ohno with publications such as “Just-In-Time for Today and Tomorrow” (Ohno, 1988), “Toyota Production System: Beyond Large-Scale Production” (Ohno, 1988) and “Workplace Management” (Ohno, 1988). Nevertheless, it origins involve the work of Eiji Toyoda (1913 – 2013) and Taiichi Ohno (1912 – 1990). The Toyota corporate website summarizes the key aspects of the TPS (Toyota Corporation, 2017). Figure 2. TPS conception

Source: Adaptation of TPS house (Lean Enterprise Institute, 2017)

The Figure 2 graphically explains TPS. This approach has a strong philosophical essence. The TPS goal is high quality, low cost and shortest lead-time. The term quality refers to the fulfilment of the customer’s requirements. All the activities at the TPS must be performed with lower cost and as fast as possible but without compromising the high quality of the outcome. Two pillars support the TPS. First is Jidoka. A Japanese word refers to automation with human element. The system separates the machine work from the human work but it combines both when necessary, for example, in the recognition of nonconformities. The system is able to detect the defects during manufacturing. Then, it has procedures to repair the issues or take out the piece (sometimes even stop the production line) and finally, it explores the causes of the abnormality and it develops solutions in order to improve the process. The second pillar is Just in Time (JIT). It seeks for a continuous manufacturing flow. It means that the manufacturing process runs without any interruptions and does what it should. The concept of Takt Time allows the recognition of the continuous flow. The takt time from the customer’s side is the rate of the customer’s demand while the takt time from the production line is the rate of process output. The pull system activates manufacturing processes based on demand. Then, just in time is a system that allows the manufacture of the needed product at the right moment in the right place.

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The base of the TPS includes tools such as heijunka, standardization and kaizen. Production levelling or heijunka facilitates the manufacturing process avoiding unevenness. Standardization allows the system to assess the performance of process and facilitates the improvements. The philosophical element of Kaizen refers to continuous improvement. The TPS argue three types of system deficiencies that every modern, sophisticated manufacturing system should avoid. These are Muda, Mura and Muri. Muda (Waste) is an activity that consumes resources without creating value for the customer. TPS argue eight types of waste (Womack & Jones, 1996). The next system deficiency is Mura. It comprises the activities that create unevenness, inconsistencies and irregularities. These changes lead to an unbalanced situation and generate production flow interruptions. Finally, Muri refers to overburden, unreasonable, impossible or overdoing. It includes the activities with disproportionately consumption, burden and efforts of workers, materials or equipment.

Theory of Constraints This theory searches activities, processes, actions or any other elements that prevent the organization to achieve its goals (Constraints). The TOC argues that “a chain is no stronger than its weakest link” and therefore managing constraints allow the organization to achieve their goals (Goldratt E. M., 1984). The author of the theory is Eliyahu Goldratt (1947 – 2011) an Israeli physicist that dedicated his life to teach organizations and managers principle of cause and effect in manufacturing and business (Goldratt Marketing Group, 2017). The most important book that explains TOC is “The Goal” (Goldratt E. M., 1984). The book is a novel for managers. It argues that the organizational goal is “to make money”. Moreover, Goldratt (1998) explains that “make money” is the goal for a profit organization and the instrument to achieve goals for a non-profit organization. The TOC proposes three measurements to assess organizational performance. First is throughput or the rate that the organization needs to generate money by sales. Second is inventory or the money that the organization uses to purchase elements that allow them to sell. The third is operational expenses or the money needed to transform inventory into throughput (Goldratt E. M., 1998; Goldratt E. M., 1984). The TOC implements the five-step procedure to improve the organizational processes (Goldratt E. M., 1998; Goldratt E. M., 1984). The procedure starts with the identification of system’s constraints. The next step concentrates in exploit constraint rather than search solutions for its elimination. This means that the analysis focuses on activities and actions that allow the process constraint to work as much as possible. The third step subordinates all other process activities to the constraint in order to permit the constraint work at its maximum. It means that the process is as good as its weakest part: the constraint. Just after these three steps, the procedure searches for solutions that elevate the possible work of the constraint. At the implementation of any of the previous steps, the constraint can to move to other part of the system. Then, it is necessary to start over again at the first step and always prevent inertia. This last step introduces continuous improvement principle to the theory.

Business Processes Reengineering The QMS and TPS are approaches based on continuous improvement. The combination of several individual projects at specific processes creates a process excellence system. However, there are organizations with archaic and ancient processes that needs a totally and radical redesign. This is the approach of BPR 58

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also called Reengineering. It takes the few acceptable things at the operation and strongly introduces process approach to develop new processes almost from scratch (Knights & Willmott, 2000). The origins of the BPR relates with the articles of Davenport and Short (1990) and Hammer (1990) and the book on reengineering (Hammer & Champy, 1993). The intention is to show that the total redesign of the organizational processes is worth more than small adjustments with long-term effect. Its application is radical and controversial, but it obtains great results in the short term (Knights & Willmott, 2000). The approach reviews the organizational goals, their processes and introduces IT solutions to the new processes. The methodology generally follows four steps. The first step of the cycle is the identification of processes for reengineering. The best candidates are those processes with outdated fundamentals and obsolete principles. The process review aims to understand the purpose of the process in the organization. This includes the analysis of the customers and their needs together with the organizational goals. Then, the new process arises with new fundamentals and principles. It usually involves IT solutions. The final step tests the redesigned process and makes its implementation to the system. Notice that the reengineering fails to provide a continuous improvement approach since the process identification search for archaic processes. Then, the next process improvement arrives when the redesigned process become outdated. Moreover, additional work to the methodology introduces the Process Reengineering Life Cycle that provides consideration to the continuous improvement (Guha, Kettinger, & James, 1993).

Lean Management The definition of the word Lean is: (of a person or animal) thin, especially healthily so; having no superfluous fat (Oxford Dictionaries, 2017). The use of this word is both a conscious and strategic choice (Womack & Jones, 1996). It indicates an organization with less waste (fat) in their processes and more value (muscles) for customers (Womack & Jones, 1996; Norlyk, 2011). The origins of Lean management refer to the book “The Machine That Changed the World” (Womack, Jones, & Roos, 1990). This book explores the implementation of TPS around the world and creates an enhanced version using different tools from TPS and QMS. Toyota Motor Corporation (2016) argues that Lean has become a fundamental component of Toyota’s success. The usual description of Lean management refers to waste reduction (Matawale & Datta, 2015; Matawale, Datta, & Mahapatra, 2014; Alemi & Akram, 2013; Vinodh & Balaji, 2011; Vinodh & Chintha, 2011). Moreover, Lean management is a methodology that creates value to the customer by reducing waste in a continuous improvement environment (Hines, Holwe, & Rich, 2004). The usual improvements delivered by Lean management implementations are more productivity, flexibility, performance and cost reduction. In general, the outcome on Lean management implementation is better processes. This includes efficiency, effectiveness, shorter lead, cycle times, lower inventories, and others achievements related with the TPS Muda, Mura and Muri. Lean frequently uses tools from different approaches. For example, Kanban, SMED and 5S are tools from TPS while FMEA, DFMA and APQP arrive from QMS and automobile sector. Kanban belongs to JIT and creates a smoothly production flow (Ohno, 1988). Usually, the tools relates with cards and boards on the factory, but Kanban means the signal that informs the moment to start the production of a certain amount of pieces or products. Single Minute Exchange of Die (SMED) is a tool that reduces setup machine time and therefore, it is possible to manufacture different types of products within the same working hours (Shingo, 1985).

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The 5S is a five-step tool that guides the workers to organize their workplace (George, Maxey, Rowlands, & Price, 2005). It eliminates from the workplace all unnecessary elements (sort). It places the needed elements in best possible layout to develop an effective and efficient work (straighten). It cleans the workplace to increase the possibility to identify abnormalities (shine). It determines standards of the new designed workplace (standardize). Finally, the tool defines mechanisms to keep the workplace in excellent conditions in the long term (sustain) (George, Maxey, Rowlands, & Price, 2005). The tool Failure Mode and Effects Analysis (FMEA) aims to prevent possible problems and issues in the process and product even before they exist (Mikulak, McDermott, & Beauregard, 2008). It is consider an essential part of any QMS (ISO, 2015). The tool explores any possible failure modes, its causes and effects. Then, it assesses their probability of occurrence (P), the severity of the impact to the system is failure happens (S) and the capability of the system to detect the occurrence of the failure (D). The risk level is a combination of the three assessments (P*S (+D)). The risk level determines the failure modes priority in order to create actions to prevent the occurrence of the failures. The Design For Manufacture and Assembly (DFMA) is a tool for the design the easiest manufacture process and the respective assembly process. FMEA and DFMA are essential tools for the implementation of Advanced Product Quality Planning (APQP). The aim of APQP is to introduce quality in the product from its very beginning. The majority of nonconformities arise from a design that fails to introduce quality (AIAG, 2008). Moreover, the implementation Lean management and its tools is beyond automobile manufacturing. There are several examples of Lean implementation in services (Piercy & Rich, 2009), higher education (Comm & Mathaisel, 2005), health care (Kimsey, 2010), human recourses (Higgins, 2007) or even the application of Lean in entrepreneurship knows as Lean StartUp (Ries, 2011). Then, Lean management prove that process excellence is relevant in a diverse type of processes at manufacturing systems but also in systems that outside these industries.

Six Sigma The implementation of Lean management determines standards that the organization follows to create customer value. However, probability of nonconformities or systems errors occur. The Six Sigma methodology focuses in this phenomenon. It deeply explores individual processes to determine their variability and reduce the probability of occurrence (Brue, 2015). The origin of the methodology refers to a group of engineers from Motorola Company in the min1980s (Tennant, 2001). They observed and studied the Japanese quality techniques and realized that quality variability in the process outcomes is their major challenge. Moreover, the company General Electric is one of the major promoters of the Six Sigma methodology. The methodology introduces the DMAIC cycle (Define, Measure, Analyze, Improve and Control) which guide the development of the process improvement projects (George, Maxey, Rowlands, & Price, 2005). It is similar to the PDCA cycle and it provides the methodology with the continuous improvement philosophy (Sahno, Shevtshenko, Karaulova, & Tahera, 2015). Furthermore, Six Sigma introduces other important tools such as SIPOC (Supplier, Inputs, Process, Outputs and Customer) which facilitates the specification of processes or the concept of “Voice of the Customer” (VoC) that aims to determine customer’s requirements. This last one is a great complement of the Quality function deployment (QFD) tool or House of Quality from QMS.

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THE FOUR REVOLUTIONS OF INDUSTRY The description and background of these process excellence approaches (QMS, TPS, TOC, BPR, LM and SS) determine the timeline milestones to review approaches appearance and development throughout the four industrial revolutions. These four phases belongs to the i4.0 concept (Schwab, 2016). Additionally, the economic and industrial revolutions comprise challenges and determine the new context for organizational performance (Perez, 2010). Therefore, this chapter explores the evolution of process excellence throughout these four industrial revolutions. The first industrial revolution relates with the major historical and economical milestone of our era (Deane, 1979). The industrial revolution at the end of the eighteen century. It was a change in the production systems of the nations. Industrial machine products replace agriculture and handcraft goods. The development of waterpower and steam power machines facilitates goods manufacture at higher volumes. The use of machines increases productivity but sometimes this substitution (manual vs. machines) is insufficient. Process analysis is required. That is one of the contributions of Frederick Taylor (1856 – 1915). He deeply analyses the production processes and develops several techniques to increase efficiency (Dixit, Hazarika, & Davim, 2017). These techniques establish work standards of better working methods based on the time limit of the production activities. It means that the coordination of the times among machines and human work need balance to optimize the production output. The understanding of organizations as system of processes regulated by time permits the development of mass production. The system implements the conveyor belt. The origin of this technology dates back to the beginning of the first industrial revolution when Oliver Evans (1755 - 1819) assembly a primitive form of band conveyor for flour mill (Walker, 2012). His work also includes several water and steam machines. However, Henry Ford (1863-1947) implements the conveyor belt for the Model T manufacture process (Henry Ford Heritage Association, 2017). Then, the factory behaves as a unique large process on which sub processes feed the main process in coordination with individual machine times. Machines and production systems increase their productivity with electric power. Therefore, the characteristic of the second industrial revolution is the introduction of electricity to the production processes (Schwab, 2016). The electrification of industry increases flexibility, lowers costs, enhances workers moral, improves quality, is cleaner, more silent and handier (Tugwell, 1927). Electricity is the next step that Henry Ford uses to increase productivity and quality at affordable prices (Beaudreau, 2005). He is convinced that make the best possible goods quality at lowest cost paying highest wages is possible and it is the best combination to avoid situations such as the great depression (Beaudreau, 2005). The word Quality appears in the industry lexicon during those days. Books such as “Economic Control of Quality of Manufactured Product” (Shewhart W. A., 1931) and tools such as PDCA (Shewhart & Deming, 1939) evidence the importance of quality in the production processes. Moreover, just after the Second World War the concept of quality develops in organizations mainly in Japan based on Deming’s work (19001993) and the introduction of the 14 points of Total Quality Management (TQM) (1950). Then, Toyota Corporation reviews the work and philosophy of Eiji Toyoda (1913 – 2013) and implements TQM and other approaches to develop TPS (Ohno, 1988). Moreover, the introduction of computers and robots to the manufacturing systems creates new challenges. Computers and production processes automation define the third industrial revolution (Schwab, 2016). The first commercial available microprocessor developed by Intel in 1971 (Reilly, 2003) begins this revolution. Computer science professionals begin the development of better machines for industry such as Computer Numeric Control (CNC) or industrial robots mainly for the automobile industry. Toyota also 61

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develops their own robots since 1980s (Ota, 2012). Moreover, authors such as Eliyahu Goldratt (1947 – 2011) warns that the implementation of automation in specific manufacturing cell might increase the productivity of the cell but destroys the productivity of the entire factory (Goldratt E. M., 1984). TOC challenges automation with process approach. The automation should follow the factory logics of the five-step procedure on which the constraint determines the place of the system for automation. Then, the automation of manufacturing processes is far beyond the installation of computers, CNCs and robots in the factory. It is about process improvement by technology implementation. Since industrial processes automation requires integral analysis, then it could be easier to start a new factory with new technology and close the old one (Green field vs Brown field). This is the approach of BPR (Hammer & Champy, 1993). There are major economic, political and social changes in the 1990s (Fukuyama, 1993). The organizations structures are outdated to challenge the new markets, politics, economy, systems, etc. Globalization speedup (Wesseling, 2009) and Information and Communication Technologies (ICT) add to the equation more challenges for the manufacturing facilities (Frenkel, 1990). Then, the BPR approach arrive as an excellent answer to design and implement process excellence in new manufacturing systems that cope these major changes with the support of ICT. Moreover, the radical system improvement needs continuous improvement (CI) to increase performance (Davenport & Short, 1990). Figure 3 illustrates this combination. Figure 3. Combining BPR and CI Source: (Greasley, 2009, p. 277)

Therefore, CI methodologies such as Lean management and Six Sigma still have the opportunity to provide the industry with techniques and tools for process improvement. In one hand, Lean management approach facilitates standards implementation while Six Sigma helps to reduce their variability. Then, the combination of all process excellence approaches allow manufacturing facilities to improve and perform in accordance with their strategies. Today the industry faces Industry 4.0 (Baur & Wee, 2015). The main characteristics of this revolution are cyber-physical systems, real-time connectivity, big data analytics and others new technologies

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throughout the entire supply chain (Lasi, Fettke, Kemper, Feld, & Hoffmann, 2014). This includes concepts such as Internet of Things (IoT) (Islam, Kwak, Kabir, Hossain, & Kwak, 2015) or smart factories (Rashid, Sayin, Oureshi, Shami, & Khan, 2011). An important milestone for i4.0 is Internet and its spread use among people and companies. People around the world are in constant use of emails, social media and other technologies (Haigh, Russell, & Dutton, 2015). This connectivity facilitates the creation and evolution of new business such as Uber or Airbnb. These organizations provide better services with less infrastructure thanks to the possibilities of internet and the social connectivity. The business expectations for this new generation of business is more than 11 trillion USD for 2025 and the 70% of these possibilities are at industry (Manyika, James; Chui, Michael; Bisson, Peter; Woetzel, Jonathan; Dobbs, Richard; Bughin, Jacques; Aharon, Dan, 2015). Several new technologies facilitate the development of this revolution. Augmented reality introduces new possibilities in maintenance (Zhu, Ong, & Nee, 2013) or artificial intelligence facilitates the development of human-technology teamwork (Norman, 2017). Process excellence approaches need to understand and use these technologies in order to provide organizations with tools and techniques for their improvement. The Figure 4 and Table 1 summarize the timeline analysis of the four industrial revolutions and the development of these six process excellence methodologies. Figure 4. Process excellence methodologies and industrial revolutions Source: Author’s own work

The fourth industrial revolution promises higher manufacturing processes flexibility, faster and precise processes, error rates reduction, accurate data and others benefits (Schwab, 2016). However, the systematic literature review reveals that the majority of publications on i4.0 dedicates to explain and present specific technologies and its benefits and so far the inclusion of i4.0 as a next step in process excellence is poor (Martinez, Jirsak, & Lorenc, 2016). Then, it is necessary to determine the aspects that process excellence approaches need to develop in order to provide organizations with successful implementation of this new revolution.

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Table 1. Process excellence methodologies and industrial revolutions Industrial Revolution

Process Excellence

1 industrial revolution. Machines, steam machines. A major change from an agriculture based economy to an industry-based economy.

Manufacture processes speed up. The measure of production time and productivity become the first parameter of process excellence (Taylor, 1911).

2nd industrial revolution. Electricity allows the machines and manufacture systems to improve their performance.

Mass production increases the volume of products and manufacture speed. Time measurement still basic for process management and improvements but product defects occur and it is important to manufacture without them. Beginnings of QMS and TPS.

3rd industrial revolution. Electronics and computer-based machines allow the manufacturing systems to create better products and balanced processes. Customers want more individualized products.

The introduction of IT in business introduces several possibilities to create new processes BPR. This period consolidates QMS and TPS and it develops TOC and Lean management. Process variability is an issue that Six Sigma covers.

4th industrial revolution. Cyber-physical systems, big data, smart factories, smart products.

More data from machines. Less people working at the shop floor. Complex interactions of IT systems with machine and human systems. High individualization of products. Connectivity throughout the entire supply chain.

st

Source: Author’s own work

IMPLEMENTING PROCESS EXCELLENCE IN I4.0 The fourth industrial revolution has just begun. Governments and companies promote it by presenting benefits in conferences and submits. However, it is still complicated to find an entire successful i4.0 implementation on which scholars and practitioners might obtain best practices to develop new methodologies or techniques. Moreover, this section of the chapter presents possible changes for process excellence approaches based on new technologies understanding possible applications in the i4.0 framework. The review of the history of the four revolutions and processes excellence approaches (Figure 4 and Table 1) illustrates that the principles of process excellence were valid, are valid and will remain valid. However, the new revolution challenges process excellence with higher complexity but at the same time, it brings new technologies that provide insights to improve current techniques and tools or to create new ones. The principles of process excellence are processes and continuous improvement. Organizations are collection of processes. Outputs, inputs, activities, owner and parameters remain as the framework to understand the mechanisms that organizations use to perform (Figure 1). However, there are more subjects in the process approach. The first industrial revolution concentrates in manufacturing processes. The subjects are materials, work in progress and final goods. The second revolution introduces electricity. The ICT brings in to the business context communication as new subject. Today, approaches such a Lean management introduces new subjects such as customer experience in services (Piercy & Rich, 2009), learning process in higher education (Comm & Mathaisel, 2005), operational processes in health care (Kimsey, 2010), recruitment and carrier path in human recourses (Higgins, 2007) or time and money in entrepreneurship (Ries, 2011). Nevertheless, these new subjects enhances the validity of process approach and the introduction of i4.0 brings new subjects to it. Then, from this historical perspective, the concept of process contributes with i4.0 development. Organizational continuous improvement is the second process excellence element that remains during history. Steam machines, electricity and then electronics demonstrate that each revolution searches for improvements. Kaizen is one of the basis of TPS (Figure 2) and the combination of CI and BPR pro64

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vides better results (Figure 3). Today i4.0 is a great opportunity to improve business and organizations dramatically (Dolejs, 2016). It brings the possibility to create new business based on new technologies (John, 2016). The German government pioneers with i4.0 to improve their industry (Germany Trade & Invest, 2014). Then i4.0 is about improvements. It is the willing to be better that determine excellence. Process excellence is the continual and persistent thinking about new and better mechanisms to provide the customer with the required or better product or service. This principle still valid and provide i4.0 with a long-term background. Process excellence has excellent elements for i4.0 development such as DMAIC or PDCA tools. These comprise activities that guide the development of process excellence implementations. However, i4.0 challenges these current procedures. These tools require an enhancement within the i4.0 context. The following lines explore the current practices of process excellence and possible changes of these tools under the i4.0 context.

Process Identification This activity is an essential part of QMS and TPS. It is about the definition of each process element (Figure 1). The PDCA cycle, ISO 9001:2015, DMAIC and others have process identification as one of the first activities to develop and it is usually the first. For example, Six Sigma utilizes SIPOC. A tool for process identification. Voice of the Customer (VoC) complements the identification. Process mapping outcomes from flowcharts, layouts, process diagrams, among others illustrate the process in different perspectives (Damelio, 2011). Process identification requires process knowledge. It is necessary to meet with the people related with the processes and visit the process itself. Brainstorming sessions and gemba walks provide information from the people in order to define the process (Gesinger, 2016). Documentation is other source to understand the process. The collection of this information facilitates the process mapping (Damelio, 2011). It is usual to develop several versions of the process mapping outcomes until these graphical representations have all needed characteristics to be useful for the respective improvement. Virtual reality, augmented reality, simulations, virtual communication are technologies that have the possibility to enhance process identification. For example augmented reality present several applications (Carmigniani, Furht, Anisetti, Ceravolo, & Damiani, 2011). The knowledge of the people related with the process is important for process identification. Then, real-time communications technologies allow the development of brainstorming session without the need to visit the process. Virtual reality and augmented reality have the potential to replace gemba walks. Simulations and real-time data develop better outcomes from process mapping tools. Moreover, since customers’ requirements rapidly change, real-time data to determine VoC accelerates the DMAIC or PDCA cycles.

Measurement The process identification determines the parameters that allow process management but also its improvement. The concept of Critical To Quality (CTQ) complements the decision on the variables to measure. Therefore, it is necessary to establish mechanisms to capture data about those parameters. The activity measurement consolidates those mechanisms and aims for accurate and usable data. Time, distances, temperature, pressure or number of nonconformities are some of usual process parameters needed for its management or improvement. 65

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The usual representation of these measurements are tools that allow their interpretation and analysis to determine actions on the process. For example, QMS has the seven basic quality tools for process improvement. These are Cause-and-effect diagram, Check sheet, Control charts, Histogram, Pareto chart, Scatter diagram and Flowchart (AIAG, 2008). Then, it is necessary to verify the accuracy of the collected data. The tool Measurement System Analysis (MSA) validates the procedure of data collection and calculates the uncertainty of devices and systems (Taghizadegan, 2013). However, data caption from a high-end technology machines is paper written form from the machine’s high-tech screen (Bares, 2016). The fourth industrial revolution is also about data. Chronometers, measuring tapes, pressure gauges, thermometers, radio-frequency identification (RFID), small parts counters, traffic counters among others are instruments able to extract the required data from processes. Industrial wireless networks are a good example (Li, Xiaomin ; Li, Di; Wan, Jiafu; Vasilakos, Athanasios V; Lai, Chin-Feng; Wang, Shiyong, 2017). Clouds, networks and internet provide real-time data. The installation of sensors at the machines and other parts of the process facilitates the measurement but it also creates large and complex amount of data (Bares, 2016). Then, the progress on big data fits requirements to develop i4.0.

Data Analysis The aim of data analysis is the transformation of the raw data into information that provide insights about possible process improvements or signals about process behavior for its management. The raw data comes from measurement. However, it is necessary to review it with tools and statistics. This exploration reveals information that determines the actions to perform. These actions manage the process or introduce improvement for better performance. The seven quality tools of QMS are instruments for data caption and analysis. Further statistical analysis of the data complements the outcomes from these tools. Analysis of variance (ANOVA), hypothesis test of the mean (t-test), chi-square method and others provide additional information about the process parameters and their relationships. Statistical software facilitates the implementation of these tools and methods. Big data analytics promises to support i4.0 implementation (LaValle, Lesser, Shockley, Hopkins, & Kruschwitz, 2011). Data visualization, scenarios, simulations, regression and mathematical models are part of the big data features. In this perspective, it seems like there is nothing new but the problem is that there are “piles of data” that nobody seems to use or barely understands (Bares, 2016). Then, decision makers fail to take advantage of the possibilities offer by big data analytics.

Process Management and Control The completion of process identification, measurement and analysis provide the necessary information to take management or improvement actions. Operations management takes decisions about the amount of units to manufacture (Loading), the arrangement of the manufacture orders in the productions lines (Sequencing), the specific times to begin manufacture (Scheduling) and the mechanisms to track the processes (Monitoring & Controlling) (Slack, Chambers, & Johnston, 2010). Machines with automation systems like Jidoka or communication systems like Andon had already overtaken the activities of process monitoring and controlling. Moreover, real-time detection systems facilitate the caption, analysis and presentation of data for further decision-making (Hsu, Wang, Jiang, 66

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& Wei, 2015). The digital twins approach replicates the real factory in virtual system (Bares, 2016). Statistical process control (SPC) is a robust method that provide the elements for real-time monitoring of processes (Young, Bond, & Wiedenbeck, 2007). Technologies such as artificial intelligence (AI) have the possibility to overtake the decisions on operations management tasks. In a pull system, the demand sets loading and real-time demand information determine the production loads. Real-time capacity analysis facilitates sequence of orders and its scheduling. For example, the quality by design of pharmaceutical companies have developments with AI (Aksu, Buket; Paradkar, Anant; de Matas, Marcel; Özer, Özgen; Güneri, Tamer; York, Peter, 2013).

Process Improvement The improvement actions utilize several tools and methods that introduces changes on the process to increase their performance. For example, 5S method organizes the machines, tools and people in a specific layout. Single Minute Exchange of Die (SMED) reduces the machine setup time or change over time. Cellular flow creates special work units to develop different assembly jobs. Kanban a TPS method, facilitate the material and products flow together with JIT and JIS. Pull principle changes the principles of factory logics since the manufacture start production based on demand. Process improvement within i4.0 is a big challenge. The knowledge on process improvement is open. TPS itself promotes the open source as a mean for continuous improvement of the system itself. Therefore, AI technologies have the possibility to compare the information from processes with previous improvements to determine the necessary changes in the process. Cognitive technologies such as Doctor Watson uses the current knowledge and learn from its own cases to provide solutions (IBM Watson Health, 2017). Table 2. Possible i4.0 process excellence practices Activity

Some Current Practices

Possible i4.0 Practices

Process Identification

Brainstorming sessions, gemba walks, process mapping.

Virtual reality, augmented reality, simulations, virtual communication.

Measurement

The seven basic quality tools. MSA.

Online measurement instruments, RFID, Industrial wireless networks, clouds, real-time data.

Data Analysis

Statistical analysis such as ANOVA, t-test, chisquare, etc.

Big data. Real-time data visualization, scenarios, simulations, regression and mathematical models.

Process Management and Control

Loading, sequencing, scheduling, monitoring and controlling. Jidoka. Andon. SPC.

Digital twins. Real-time detection systems. Real-time SPC. Artificial intelligence connecting demand with manufacture.

Process Improvement

5S method, SMED, Kanban, JIT, JIS.

Artificial intelligence and cognitive technologies.

Source: Author’s own work

The Table 2 summarizes some of the possibilities that the new technologies bring to the current practices of process excellence approaches. Digital twins, big data analytics, augmented realty or RFID are technologies from the new revolution already installed in some manufacture facilities. Furthermore, there are others technologies that gives some positive insights but still under development. Moreover, the importance for process excellence is that the introduction of i4.0 brings new technologies that change

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manufacture processes and therefore, there are changes in the procedures used by the discipline to provide organizations with improvements.

FUTURE RESEARCH DIRECTIONS The introduction of i4.0 opens new perspectives in research of process excellence in organizations. The main premises is that the concept of process still valid. Organizational performance growth depends on organization knowledge of their own processes. Then, research about the relationship between process approach and successful technology implementation provides evidence that support the importance of processes in organizations. The next big aspect to develop new research relates with changes of the procedures used to manage and improve processes. Research teams have the opportunity to focus in a specific tool and investigate the changes that the execution of the tool has while using new technologies. For example, procedures such as quality audits. Digital twins, smart factories, real-time data constantly provide data and therefore, factory visit, a small part of the audit, changes its role of data caption. The reduction of human-work in the manufacture processes is other aspect for research (Germany Trade & Invest, 2014). The manufacture activities with higher risk and routine are the best candidates for automation. Then, the investigation is about the role change of the human force in the manufacture facilities. For example, the operations manager role in the manufacture system. Decisions about loading, sequencing or scheduling become automatic as well as monitoring and control. Then, the question is about the subject of management and the respective procedures on it. Moreover, the new main subject of research in process excellence is data. The 3C concept (Caption – Communicate – Compete) discloses several opportunities to investigate how organizations apply process approach in its own data. The processes constantly provide data but it is important to obtain the right data in the correct moment. Data Caption is about all mechanisms that organizations have to obtain the data from the processes. It is about instruments, but also the type (Analog or digital) and the amount. The phase Communicate is about taking the data from those instruments to the required clients in the appropriate format. Finally, the phase Compete refers to implementation of the data for process management or improvement. Today, organizations have many data but they fail to use it (Bares, 2016). The research is about understanding the types of data for each type of decision. Under the 3C concept for data, research teams have the opportunity to observe Mura, Mura or Muri of those processes and the organizational challenges for the improvement.

CONCLUSION The historical review of the industrial revolutions history evidences that the concepts of process and process excellence are basic elements of the organizations and they are still valid. The review of the first three revolutions shows that these concepts endure. Then, an organization willing to obtain benefits from the fourth industrial revolution requires processes review and process excellence approach implementation. The first three industrial revolutions determine contexts on which process excellence approaches have the opportunity to provide improvement to organizations. However, the fourth industrial revolution just began and a proper formal process excellence methodology for this new revolution is still missing. The 68

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implementation of the new technologies in the i4.0 context provides new examples, cases and evidences that eventually determine the development of new process excellence approaches. The Quality Management System (QMS), Toyota Production System (TPS), Business Theory of Constraints (TOC), Processes Reengineering (BPR), Lean Management (LM), Six Sigma (SS) are current process excellence methodologies that provide the basic elements to implement i4.0. However, since the proposed changes from i4.0 are radical, BPR offers good perspectives to support the i4.0 implementation. Moreover, it is important to review that the combination of BPR and CI (Figure 3) gives the organization better results. Then, approaches such as Design For Six Sigma (DFSS) combines both perspectives and it becomes convenient for future implementations. The process thinking has an important role in successful i4.0 implementations. However, the fourth industrial revolution is the next step in the evolution of process excellence thinking itself. New technologies bring to organizations the possibility to develop new procedures. This include the procedures, activities and techniques of the process excellence approach. Then, it is mandatory to review these procedures from the i4.0 perspective. For example tools such as SMED. Today, the procedure to develop a full SMED is manual based. In the future, thanks to the i4.0 context, the procedure implies the use of real-time data, simulations, digital twines and others technologies. The simple collection of documents for a quality audit to obtain a required certification utilizes today email communication. In the future, the auditor observes from their mobile device the factory performance in real time. Then, the main conclusion of this chapter is determine the necessity of process improvement to develop i4.0 but, more important, the necessity to implement i4.0 in the procedures and techniques to develop process excellence. Then, industry 4.0 provides process excellence methodologies with new aspects that enhance its activities and therefore the organizational improvement.

REFERENCES AIAG. (2008). Advanced Product Quality Planning and Control Plan. Southfield, MI: Automotive Industry Action Group - AIAG. Aksu, B., Paradkar, A., de Matas, M., Özer, Ö., Güneri, T., & York, P. (2013). A quality by design approach using artificial intelligence techniques to control the critical quality attributes of ramipril tablets manufactured by wet granulation. Pharmaceutical Development and Technology, 18(1), 236–245. doi: 10.3109/10837450.2012.705294 PMID:22881350 Alemi, M., & Akram, R. (2013). Measuring the leanness of manufacturing systems by using fuzzy topsis: A case study of the ‘Parizan Sanat’ company. South African Journal of Industrial Engineering, 3, 166–174. Bares, M. (2016). Implications, Applications, Trends, and Topics of Industry 4.0. Round Table session at the International Business and Management Conference, Prague, Czech Republic. Baur, C., & Wee, D. (2015). Manufacturing’s next act. Retrieved 04 24, 2016, from McKinsey & Company: http://www.mckinsey.com/business-functions/operations/our-insights/manufacturings-next-act Beaudreau, B. (2005). The National Industrial Recovery Act Redux: Technology and Transitions. Lincoln, NE: iUniverse. Brue, G. (2015). Six Sigma for managers. New York: McGraw-Hill Education.

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KEY TERMS AND DEFINITIONS 3C Concept: It is the introduction of process approach into data. Big Data: It is a term related with the large amount of data produced from devices and the data analytics that provide information for decision making. BPR: Business processes reengineering is a process excellence approach that prefers radical and total changes that continuous improvement. Cyber-Physical Systems: These are one of the revolutionary systems in manufacturing. It is about automation strongly connected with end users thru internet. Lean Management: A process excellence approach that increases value by reducing waste in a continuous improvement philosophy. QMS: Abbreviation stands for quality management system. It is a process excellence approach with certification at ISO 9001:2015. Six Sigma: This process excellence methodology focuses in reduce the variability of processes. TOC: The acronym refers to the process excellence approach theory of constraints developed by Eliyahu Goldratt. TPS: Toyota production system is a process excellence approach from Toyota Corporation.

This research was previously published in Analyzing the Impacts of Industry 4.0 in Modern Business Environments; pages 328-350, copyright year 2018 by Business Science Reference (an imprint of IGI Global).

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Chapter 5

Evolution of Maintenance Processes in Industry 4.0 Adithya Thaduri https://orcid.org/0000-0002-1938-0985 Luleå University of Technology, Sweden Stephen Mayowa Famurewa Luleå University of Technology, Sweden

ABSTRACT Several industries are looking for smart methods to increase their production throughput and operational efficiency at the lowest cost, reduced risk, and reduced spending of resources considering demands from stakeholders, governments, and competitors. To achieve this, industries are looking for possible solutions to the above problems by adopting emerging technologies. A foremost concept that is setting the pace and direction for many sectors and services is Industry 4.0. The focus is on augmenting machines and infrastructure with wireless connectivity, sensors, and intelligent systems to monitor, visualize, and communicate incidences between different entities for decision making. An aspect of physical asset management that has been enormously influenced by the new industrial set-up is the maintenance process. This chapter highlights the issues and challenges of Industry 4.0 from maintenance process viewpoint according to EN 60300-3-14. Further, a conceptual model on how maintenance process can be integrated into Industrial 4.0 architecture is proposed to enhance its value.

INTRODUCTION Several industries are looking for intelligent systems, smart methods and functional processes to increase their production throughput and operational efficiency at the lowest cost. At the same time there is a steady need to reduce operational risk and product or service quality considering demands from stakeholders, governments and competitors. In this process, these industries suffer from operational flaws, human errors, systematic failures and process ineffectiveness leading to unanticipated delays in production and other negative incidences. To reduce these technical and operational deficiencies as well as improve their DOI: 10.4018/978-1-7998-8548-1.ch005

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 Evolution of Maintenance Processes in Industry 4.0

productivity, industries are looking for possible solutions to the above problems by adopting emerging technologies. A foremost concept that is setting the pace and direction for many sectors and services is industry 4.0. The focus is on augmenting machines, infrastructure and systems with wireless connectivity and sensors to monitor, visualize and communicate incidences between different entities for decision making. This new approach is essential for competitiveness in current industrial set up and for assuring a successful business enterprise. This new trend entails the use of well - designed technologies process, and models in the form of internet of things (IoT), cyber-physical systems (CPS), cloud computing, Big Data and artificial intelligence (AI) to facilitate data exchange and automation. This disruptive revolution has caused substantial evolution in physical asset management. An aspect of physical asset management that has been enormously influenced by the new industrial set-up is the maintenance process. Maintenance practices, perception and prospect have been influenced in different industrial context, thus there is a need to adopt the concept of maintenance process according to the international standard EN 60300-3-14 into Industry 4.0 framework. This book chapter gives an overview on the evolution of maintenance function and the role of maintenance process in the bigger picture of industry 4.0 revolution. It also highlights the issues and challenges of maintenance process within the context of this new business approach. Some of the drawbacks of developing an industry 4.0 solution without adequate emphasis on maintenance process will be discussed. Further, some essential features and assisting technologies of industry 4.0 will be discussed with interest on how they can be used to connect the various elements of maintenance process. These elements include maintenance management, planning, preparation, execution, assessment and improvement according to maintenance standards. The purpose, characteristics and contents of each of these maintenance process elements differs thus there is a need to adequately investigate how they can be addressed in industry 4.0 context. This book chapter also gives a conceptual model on how maintenance process can be integrated into industrial 4.0 architecture. This conceptual model will support a seamless integration of operation and maintenance processes to facilitate effective and efficient maintenance decision making.

Concept of Maintenance Maintenance is a function that combines technical, administrative and management actions intended to retain an item in, or restore it to, a state in which it can perform as required (CEN, 2001). It is necessary for all physical engineering asset that are intended to add value to an organization or individual to be maintained in relation to its value creation capability. The maintenance function can be defined as “activities for retaining a system in an operating state or restoring it to a state that is considered necessary for its operation and utilization”. Hence, the important step in the effective management of the maintenance process is the precise identification of the need of maintenance, that is further demanded by the present and future state of the machine, and the necessary actions that need to be taken to restore it or retain it in an operating condition (Kumar, 2008). These activities cover the period from the creation of an asset to the end of its life. This is against the common misunderstanding of maintenance, where it’s only limited to the operation phase of an asset life cycle (Ben-Daya, Kumar, & Murthy, 2016). With recent advancements in technology, modern systems have become massive in size, extensive in functionality, complex in configuration and connected for automation. The sustainability and dependability demand on such systems are on the increase thus created need for new processes, technology, models and solutions for effective and efficient maintenance of the complex systems. This need is as77

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sociated to competitive business environment and extremely increasing cost (direct and consequential cost) of unavailability and poor service quality. Further, breakdowns or poor equipment performance also result in a loss of product or service quality. It could also negatively impair safety, health and environment aspects of physical asset management goals (Bream, 2006). Prevention of these consequential incidences have become mandatory to survive the competitive nature of production, manufacturing and service delivery. In short, these are some of the driving forces for the evolution of maintenance discipline to becoming an indispensable area in engineering and technology.

Evolution of Maintenance In general, the perception and implementation of maintenance in different industries have evolved from unplanned to planned maintenance, fail and fix to monitor and maintain, reactive to proactive maintenance, corrective to preventive maintenance, time based to condition based maintenance, inspection to predictive maintenance, prognostics to prescriptive maintenance. Figure 1 exemplifies the chronological development of the business of maintenance through the years (Lee & Wang, 2008). Figure 1. Evolution of maintenance concept with time [Adapted from (Lee & Wang, 2008)]

In the past maintenance was perceived as a necessary evil and treated as a dirty, non-value adding and unplanned job. It’s neither seen as core function as production/operation nor recognized as a key component of revenue generation and business goal achievement. The paradigm overview in Figure 1 starts with No maintenance scenario where there is neither no way to fix the fault or no finance to fix it (i.e. it is cost effective to discard the failed system or component). For reactive maintenance, the focus of reactive maintenance is run to failure and then “fix it after it’s broken”. Basically, knowledge of the equipment degradation behavior is lacking thus, little to no maintenance is performed and the machinery operates up until a failure occurs making maintenance a

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fire-fighting function. The competitive nature of industrial operation and environmental/safety issues have rendered this approach inappropriate. Preventive maintenance is a policy which adapts a measure of time such as calendar or machine time, cycles, distance travelled, number of products for triggering replacement, overhauling or adjustment of an item. It is done at fixed or varying intervals, irrespective of its condition at that time. There exists different modifications and adaptation of this policy. This approach is no longer considered efficient especially for large and complex systems and continuous operations where remaining useful life is considered important. Condition based maintenance is based on the failure limit policy in which maintenance is executed only when a feature describing the health of a unit reaches a predetermined level. This policy requires condition monitoring technology to control certain performance indices periodically or continuously. Maintenance is triggered whenever a feature value crosses predefined threshold to restore the machine to its original state or satisfactory level in comparison to the threshold. It analyzes the current level of measured physical parameters against established engineering limits for the objective of detecting, analyzing and correcting a problem before the occurrence of failure. This method gives information required for failure diagnostics, maintenance planning, and thereby reducing unexpected operating costs and loss. However, the lead time for maintenance is often too short for effective operation and maintenance decision making. Predictive maintenance is a concept that is rapidly evolving along with the development in data technology. The core is seamless integration of maintenance diagnosis and prognosis of machine health via relevant communication technology. Trend of system behavior and the degradation pattern is very important aspect in this concept. Specifically, there are three main aspects of this concept: • • •

Sensor technology and intelligent applications to enable the systems monitor, predict, and optimize their performance in an intelligent way. Philosophical inclination towards “failure proactive” and not “failure reactive” i.e. capability to find underlying conditions that can lead to machine faults and degradation. Effective reporting to complete the maintenance loop by feeding the maintenance information back to re-design, modification and maintenance improvements.

Prescriptive maintenance is a new design and maintenance concept. In this approach, engineering assets are equipped with capability to monitor, diagnose, be aware of their state and propose repair action in case there is indication of failure or degraded performance. The prescribed intervention takes several factors into consideration, such as operating environment, business goals, safety standards, decision scenarios, maintenance context. In some instances, systems are expected to restore themselves to perfect state or give actionable information to maintenance robot or send information directly to the appropriate maintenance service provider. In other instances, prescriptive maintenance implements self-repair by recovering the required function of a degrading system through a tradeoff for other functions. The required capabilities of prescriptive maintenance policy with self-maintenance include the following: monitoring capability, fault judging capability, diagnosing capability, prescriptive capability, repair executing capability, self-learning and improvement (Lee & Wang, 2008). An integral aspect of this approach is automated service trigger function that enables a system to initiate service request after prescription and before a failure occurs, this task can be eventually carried out by a maintenance team.

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MAINTENANCE PROCESS Understanding maintenance function, maintenance support and maintenance process is a pre-requisite for the development of effective operation and maintenance related solutions. Inadequate consideration to maintenance process is one of the major reasons for unsuccessful implementations of several emerging technologies in the field of engineering asset management. Emerging maintenance technologies and methodologies should be connected to necessary actions and support resources required under given conditions. For such technologies to have expected impact especially at the shop floor level, it should give actionable information that can guide the maintenance execution. In addition, it should have clear linkage with necessary resources such as human resources, support equipment, materials and spare parts, maintenance facilities, documentation, information and maintenance information systems. A well-designed maintenance process can be implemented in the emerging industry 4.0 framework to ensure consistent application of maintenance and maintenance support (Kans, Galar, & Thaduri, 2016). The other recognized applications of maintenance process in the context of industry 4.0 for smart manufacturing (Thoben, Wiesner, & Wuest, 2017), to improve preventive maintenance (Wan et al., 2017), for self-aware machines (Bagheri, Yang, Kao, & Lee, 2015), for process monitoring (Reis & Gins, 2017), for smart grid (Batista, Melício, & Mendes, 2017), fault diagnosis and prognosis (Li, Wang, & Wang, 2017), smart maintenance and logistics (Rakyta, Fusko, Herčko, Závodská, & Zrnić, 2016) and human centric maintenance process (Fantini, Pinzone, & Taisch, 2018) and for linear assets (Seneviratne, Ciani, Catelani, & Galar, 2018). This will not only make the technology complete in terms of maintenance but will also raise its value-addition to organizational goals. A maintenance process to be integrated into and assured by the promising industry 4.0 technology should include these essential elements: maintenance management, planning, preparation, execution, assessment and improvement. A general description of the constituting elements of this process is shown in Figure 2 and explained thereafter according to the existing European standard (EN 60300-3-14, 2004). It should be noted that the contents and underlying activities of each elements might vary depending on organizational structures, needs, values, processes, strategies and priorities. Therefore, it is necessary to adapt or redesign this generic process to fit the organizational environment in which the technology is to be applied.

Maintenance Management There is a need to consider dynamic maintenance management information, concept and activities as an integral aspect of industry 4.0. Some aspects of maintenance management are still left of the current framework and model for industry 4.0. The management activities and its associated information required to assure effective and efficient maintenance in any industry 4.0 deployment should include the following: • • • • •

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Development of maintenance policy for plant, system and critical components Development of finances and budget for maintenance Design of maintenance coordination and supervision structures Acquisition of adequate information on processes, methods and procedures relevant to the abovementioned activities. Collection of information on operation and maintenance organizational structures.

 Evolution of Maintenance Processes in Industry 4.0

Figure 2. Maintenance process (EN 60300-3-14, 2004)

Maintenance and Maintenance Support Planning In the development of maintenance centered disruptive technology such as industry 4.0, it is essential to cover the planning aspect of maintenance. This will assist in establishing maintenance concept for critical items at the appropriate life cycle phase. The planning activities and the associated information required in an integrated industry 4.0 deployment should include the following: • •

•

Maintenance task identification using manufacturer’s recommendation, conventional approaches (e.g. in-house experience) or structured approaches (e.g. RCM) Maintenance task analysis to determine the essential information and required resources for each maintenance significant item. These includes: ◦◦ Description of the maintenance task as required for self-reconfiguration or for intervention by maintenance personnel ◦◦ Task interval in relevant time measure, i.e. elapsed time, operational cycles or distance ◦◦ Task times and skills of personnel required to perform the tasks ◦◦ Maintenance, safety and handling procedures ◦◦ Specials tools, test equipment and support equipment spare parts, materials and consumables required; ◦◦ Observations and recordings to be made; ◦◦ Test and checkout procedures to verify successful completion of the ◦◦ Maintenance task Identification of maintenance support resources to determine whether intervention will be carried out by the system itself, robots or by the maintenance service provider. Further, it determines which maintenance organizational level will provide the service required for certain system/items. Items may be maintained on-site, at a local repair shop or by an external repair facility. In case of self-trigger maintenance planning, the following information are required for each item: ◦◦ Who provides the maintenance: internal or external maintenance department, or operators;

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◦◦ ◦◦ ◦◦

Who provides the spare parts, materials and consumables, and the information about their stock level; Who provides the special tools, transportation, lifting, testing and support equipment; Who provides the condition monitoring equipment and the associated information management system;

Maintenance Preparation This is an aspect of maintenance that should be given due attention when developing operation and maintenance enablers. The following activities are important in order to create enough lead time to plan and supply the necessary resources in an I4.0 implementation: • • • • • • •

Identifying and assigning personnel or robots Acquiring materials and spare parts Ensuring tools, transportation, lifting and support equipment are available Preparing workplans and callout procedures for all intervention Preparing required operating, maintenance, safety and environmental procedures and Providing necessary personnel training and robot calibration Scheduling of activities, based on a priority system to ensure the most urgent works are carried out first by either available crew or robots

Maintenance execution This perspective is not fully addressed in the current frameworks and models for industry 4.0. The execution of maintenance tasks either by robots, operating machine, internal or external maintenance personnel should be performed with due attention to the technical procedure for isolation, disassembling, cleaning, repairing, refurbishing, replacing and testing. For effective integration of this element of maintenance process into an industry 4.0 framework, it is essential to address the following activities and information: • • • • • •

Recording of observations, readings, measurements, tasks carried out and resources used. Gathering of technical data and task description Obtaining spare parts, tools and support equipment; Recording task times including travel time to the worksite and active maintenance time Preparation of the worksite such as equipment shutdown, isolating and lockout Gathering of information related to testing, checkout and clearing of worksite;

Maintenance Assessment The assessment of maintenance interventions should be performed either at the point of intervention or periodically to review overall maintenance performance. A standard method should be established for analyzing the performance of maintenance in terms of logistic, economic, dependability and quality performance of all maintenance efforts. The results could be used to identify and justify improvements. The following activities are considered relevant for this element of maintenance process:

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• • • • •

Measurement of maintenance performance and analysis of result Assessment of actions to be taken Assessment of maintenance effectiveness Assessment of technical aspects of the maintenance task- resource adequacy, effectiveness of operating, safety and environmental procedures. Overall review of maintenance procedure in terms of reworks, trends related to operating conditions, design and manufacturing quality problems

Maintenance Improvement Maintenance improvement is another aspect of maintenance that should be implemented in the emerging industry 4.0 framework to ensure completion of the value-addition process. Information and activities related to maintenance improvement that should be assured in the emerging I4.0 framework include changes in: • • • • • • •

Procedure and process for establishing maintenance concept, strategy and policy Specification for skills and training of maintenance and operations personnel Specification for robot upgrade, calibrations and update Policies for spare parts and materials management Quality and type of tools and support equipment Operating, safety and environmental procedures Equipment and system design

ENABLING TECHNOLOGIES OF INDUSTRY4.0 Conventionally, industries functions in fragmented silos with almost of the departments inside an organization. Due to this dis-integration of silos, it takes lot of time to take decision and information unavailability in the event of a failure, hazard, accident, loss of profit, etc. These lagging decisions will significantly affect the performance of the organization in the coming age of growing demands and dynamic behavior of the market. These lagging indicators also effect the human behavior that lead to lot of stress and have societal implications. Hence, there is a necessary to integrate several systems in the organization to improve the performability to reach the competition in the market. One of the technologies that industries are focusing is Industry 4.0 (Pascual, Daponte, & Kumar, 2019). Though it is originated from the manufacturing industry to improve the productivity of the production process with high automation and efficiency, this technology is also getting attention from the other industries where they modify according to their own requirements and demands, mostly to improve the performance, efficiency and effectivity. It encapsulates different enabling technologies such as IoTs for data acquisition and connectivity, Artificial Intelligence for data analytics and decision modelling, Big data for handling large amount and variety of data, service-oriented architecture for defining stakeholder’s requirements context aware systems for decision support systems and cyber-physical systems for architecture. The unification of these technologies will enable the organizations to improve overall life cycle management of their assets thus also improve the maintenance process, in general to increase

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the usage life of the assets. Several other technologies are mentioned in (Chen et al., 2017; Lu, 2017; Zhou, Liu, & Zhou, 2015).

Internet of Things (IoTs) IoT concept facilitates access to internet for any type of “device/thing”. An interesting review on IoT is written by (Holler et al., 2015). Each “thing” will have an Internet Protocol (IP) address and will be referred using standard internet technology like Domain Name System (DNS). Applications of the Internet of Things in automation mainly include acquisition of relevant data from the sensors, actuators for actuating a task, Programmable logic controllers (PLCs) for controlling the variables, and Control loops. Hence, the IoT can be perceived as a physical device or as a functionality that is processed in a software that will be executed on any type of thing/device that is having enough computational resources. Presently, there is no coherent or standard technology that can be recognized to an IoT (Trappey, Trappey, Govindarajan, Chuang, & Sun, 2017). Hence, mainly, in the context of Industry 4.0, IoTs can be used to obtain the necessary data from the environment to facilitate the maintenance decision making. These IoTs can be connected through other IoTs by using a standard networking technology such as Bluetooth/ WiFi or other technologies to transfer all acquired data to the cloud. An application of IoT for the application in assessing the condition of roads in winter domain is demonstrated by (Odelius, Famurewa, Forslöf, Casselgren, & Konttaniemi, 2017).

Artificial Intelligence (AI) Interesting applications of data mining and artificial intelligence (A.I.) in industrial production process are in maintenance (Bastos, Lopes, & Pires, 2014), in predictive maintenance reading Internet of Thing (IoT) sensor data and generally in predictive analytics (Winters, Adae, & Silipo, 2014). External data mining tool (KNIME): this external tool can improve advance analytics related to predictive maintenance of the production lines (Massaro, Maritati, Galiano, Birardi, & Pellicani, 2018). There are case studies that provides AI methodologies for sustainable, predictive maintenance of production equipment (Tretten & Karim, 2016), context-driven maintenance services (Thaduri, Galar, Kumar, & Verma, 2016) and infrastructure maintenance decision support (Famurewa, Zhang, & Asplund, 2017). (Diez-Olivan, Del Ser, Galar, & Sierra, 2019; Seneviratne et al., 2018) provided several artificial intelligence tools for diagnosis and prognosis of general and linear assets. Applicable AI algorithms must be developed and trained to distinguish which specified engineering limits of the monitored parameter requests an immediate maintenance intervention. The most relevant benefits of maintenance strategies based on the continuous condition monitoring of an operating parameter are listed below (Alsina, Chica, Trawiński, & Regattieri, 2018): • • • •

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Improved plant system availability Reduced total cost of ownership Enhancement in the design of complex systems RAMS (Reliability, Availability, Maintainability and Safety) analysis, maintenance optimization, and forecasting of part consumption.

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Big Data (BD) and Data Mining (DM) Gartner research defined big data in terms of three Vs i.e., Volume (growing rate of data), Velocity (speediness of data) and Variety (Gartner,). Now, other two more Vs have been attached to it i.e., Veracity and Value. Big Data Analytics can be used to detect and predict the fault/failure in the component/ system by analyzing the vast amount of data gathered from the sensors in coherent and real-time and lessen unplanned or unexpected service delays to enhance the efficiency or productivity. There are several potential applications of Big Data in Railway sector (Thaduri, Galar, & Kumar, 2015). Its impact of maintenance in the era of big data within asset management is stipulated by (Galar & Kans, 2017). Some of the big data tools and technologies for transportation systems used to store, clean, integrate, manage, analyze and visualize big data are mentioned in (Kour, Thaduri, Singh, & Martinetti, 2019). DM can be described as the process of discovering appealing and understandable patterns and to discover the knowledge from large amounts of data (Galar, Gustafson, Tormos Martínez, & Berges, 2012). The evolution in the data mining is an vital process, where several existing intelligent methods are applied on the big data to extract relevant data patterns and obtain knowledge from data to perform maintenance decision support (Galar, Kans, & Schmidt, 2016). These data sources can comprises of several databases, data warehouses, internet, other information sources, or data from the human or data that are flowed into a system dynamically (Thaduri et al., 2015). In additional words, DM is the extracting the answers to the questions by searching through database for with specific rules, relationships, and patterns among different parameters not obtained by conventional query tools using extrapolatory analysis. This can also be helpful for conducting process mining for maintenance decision support system for railways (Thaduri, Famurewa, Verma, & Kumar, 2019).

Cloud Computing (CC) Cloud computing is a technological product with integration of the networking and traditional computing, such as parallel computing, distributed computing, grid computing, edge computing, utility computing, virtualization, network storage, load balancing, etc. (Zou, Deng, & Qiu, 2013). Cloud computing is a convenient model for enabling on-demand network access to a shared pool of data with configurable computing resources. It can be swiftly provided with minimum effort or service to provider interaction. It consists of mainly three services, (Mell & Grance, 2011) namely Software as a Service (SaaS): This service has a capability to facilitate the customer’s applications running on a cloud infrastructure. These applications can be accessible from any devices through either a client interface. The consumer doesn’t need to control the cloud infrastructure such as installed servers, disk operation systems, data storage, etc., but able to do limited application specific configuration settings. Platform as a Service (PaaS): This service has capability to facilitate consumer to mount onto the cloud infrastructure with customized application using programming languages, libraries, services, and tools. Like previous service, customer doesn’t need to control the cloud infrastructure, but has control over the number of applications deployed and application specific configuration settings. Infrastructure as a Service (IaaS): This service has capability to facilitate consumer for processing, storage, networks, and other essential computing resources where he/she can deploy and run software. The consumer has control over operating systems, storage, and installed applications with full settings.

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Service Oriented Architecture (SOA) Presently, the industries and governments demand for sustainability, flexibility, efficiency and competitiveness because of dynamic nature of societal and market trends. It is important to handle the requirements of multi-stakeholder environment and need for efficient co-operation and collaboration which requires a system wide architecture. This process also needs to be followed through the value chain and the life cycle of the product and its process (Karim, et al, 2016). This also required a digitization procedure to be able to connect through different stakeholders in real-time to acquire data, process the information and disseminate the knowledge among the partners from Industry 4.0. From the perspective of organization, cooperation and managing the operation assets, the three domains required are: • • •

Product life cycle management (design to support) Supply chain management (suppliers to customers) Stakeholder integration management (shop floor to business)

These three management domains must work together so that the requirements of multi-stakeholders must be achieved. The dynamic collaboration among each of these domains has the potential possibility of integrated learning among them and transfer of data and information being the key. To provision these developments in these domains, there are still several gaps in administrative, managerial and technological gaps that needs to be address where the present state of the art is not enough. The combination of digitization, digitalization and automation could improve the competitiveness, flexibility and sustainability.

Context-Aware Systems A context-aware system (CAS) is a system that acclimates actively and autonomously adapts according to the required function that enables for more relevant information to the users based on information gathered from machines/people’s contextual information. The concept of context-aware computing was as ‘‘the ability of a mobile user’s applications to discover and react to changes in the environment they are situation’’ (Schilit and Theimer, 1994, Schilit, et al, 1994). Context-aware systems are complex, and they can do different tasks such as data representation, data modelling, data management, reasoning from data, and analysis of contextual information (Thaduri, et al, 2014). There also exists different contextaware systems that are difficult to provide a generic process though it consists of four main steps are (Thaduri, Kumar, & Verma, 2017): • •

•

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Context Acquisition: The first step is to select and acquire necessary data from the sensors. The sensors can be physical sensors such as any wired or wireless sensors depending on the availability. The information can also be gathered from so called virtual to get secondary information. Storing Information: The data will be stored in repository. Before modelling the data, it is necessary to organize the data in taxonomical order so that the modelling because easier. There are several closed and open standards to store the data from bottom to top level characterizing with different entities. These entities can be defined with failure modes, failure effects, etc. Context Abstraction: The context-aware system requires the abstraction level by interpreting or aggregating them which can be useful for data analytics.

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•

Context Utilization: At the last step, this data is analyzed with data analytics to provide decision support system.

Cyber-Physical Systems Cyber-physical systems (CPS) is the upcoming jargon resembles the integration of physical systems with cyber capabilities such as cloud, networking, computation, etc. It is being in testing phase in lot of applications mainly, medical, automation, manufacturing and aviation to take advantages of this integration. It is very important to the consider that most of the existing technologies in the capabilities are not fully developed and the expectations from the business is to properly integrate these technologies to meet their demands. Hence, it requires amalgamation of comprehensive variety of scientific areas, substantial quantity of efforts and research are essential to design, develop and implement CPS methodologies. CPS systems are implemented by the assimilation of physical methods with software and communication with detailed generalizations and design, modelling, and analysis techniques for a sub-system, system or systems of systems. The prediction and control of the dynamic behavior of the systems consisting of multidisciplinary areas of computers, networking, and physical systems and their interaction in ways that necessitate essentially novel innovative design technologies. A starting point for developing such an architecture is for example the five-level architecture of CPS is outlined in Figure 3. Figure 3. Cyber-physical systems (CPS) architecture of Industry 4.0

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This architecture includes the following basic architectural levels: •

•

•

•

•

Connectivity Level: Data acquisition using condition monitoring techniques using existing sensors or Internet of Things (IoTs) or unmanned vehicles. The information must be acquired from item level to the system level depending on the objective of the function expected from CPS architecture. Due to increase in demands from the real-time communication, the data acquired from the systems must be stored in cloud through different communication protocols using service-oriented architecture. Conversion Level: The next level in the CPS is to extract the required information from the data obtained in Smart connectivity level. Sometimes, it will be difficult to extract information due to complexity in nature, accuracy of the data and huge storage. Hence, there is a need to perform advanced data manipulation or pre-processing methods such as data quality, data cleaning, feature extraction to implement the efficient data for condition assessment and the prediction. There are also other tools such as data reduction, data normalization and soft sensors to acquire secondary information from the existing data (called as precursors or covariates). When wireless sensor systems with limited power harvesting resources are involved this process is particularly challenging and requires use of energy-efficient computing concepts. Cyber Level: Once information is obtained from every connected component and system is available, modelling and simulation methods are performed to evaluate different operation and maintenance scenarios. Online maintenance analytics tools are implemented to extract useful information to support the above scenarios (Karim, Westerberg, Galar, & Kumar, 2016). Information about different machines of the same type can be compared and used for prediction. Cognition Level: Decision-making is performed by analyzing different maintenance scenarios with optimizing the parameters such as required availability, total cost, total risk and maintainability aspects using efficient genetic algorithms. The main intention of this level is to capitalize the artificial cognitive systems that can relate with specific objectives to the strategies and actions. At this level the capabilities to perform detailed simulations at the cyber level is combined with synthesis capabilities enabled by cognitive computation and human collaborative diagnostics and decision making. Configuration Level: The virtual architecture is organized by required entities and operational requirements/indicators defined by business organizations such as Key Performance Indicators (KPIs). The main purpose of the configuration level is to adapt and adopt the strategies according to the dynamic nature of the environment, contextual requirements and business demands by implementing the self-configured and self-optimizing systems. To implement these systems, the lower systems need to be supported/changed according to the needs of the organization.

All the above enabling technologies mentioned in this section can be integrated into the 5C framework for CPS-based I4.0 as illustrated in Figure 4. The interconnections between the enabling technologies, their applications and techniques in a 5C-based framework might vary based on industrial applications business objectives and other limitations.

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Figure 4. Integration of the enabling technologies of industry 4.0 in a CPS-based framework

ISSUES AND CHALLENGES IN MAINTENANCE PROCESS WITHIN INDUSTRY 4.0 CONTEXT Industry 4.0 is rapidly growing in deployment and has been contributing to the operational performance in different sectors. However, the ineffective implementation of well-designed maintenance process is a major setback that should be investigated for improvement. Some of the issues and challenges of industry 4.0 as raised by (Kumar & Galar, 2018) including related to maintenance process are highlighted below:

Domain Knowledge In order to apply Industry 4.0 and leverage these technologies to specific industrial problems, it is essential to consider domain maintenance knowledge and experience. It is needful to map the industrial process with relevant maintenance process. Adequate knowledge about the maintenance and maintenance support of industrial operation is often an issue that is not well addressed in the development of industry 4.0 solutions. Detail understanding of the elements of maintenance process are often lacking in several industry 4.0 implementations. It is crucial to understand and improve the challenges and difficulties prevailing within the present maintenance functions when designing the enabling technologies of industry 4.0. Hence, there is a need to include specific requirements for effective maintenance process when developing industry 4.0 framework.

Organizational Constraints The organizational structure of many industries is not supportive enough for the implementation of maintenance process in industry 4.0. Traditionally, decisions are made at different asset life cycle phases and organizational hierarchical levels such as strategic, tactical and operational levels but these levels exist as distinct layers without due co-ordination and diligence. These constraints often lead to multi-level

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decision making, sub-optimization and multiple information silos. Hence, there is a need of integrated organization framework which enables the integration of maintenance process in industry 4.0 framework.

Self-Configurable The end focus of many industry 4.0 solutions is high-level configuration in terms of self-adjustment, organization and maintenance. Thus, other possible maturity levels of configuration are often neglected. In some organizations, the achievement of very high level of self-configuration is almost impossible because of requirement of huge investment and involvement of huge risk. Hence, there is a need to consider other self-capabilities in combination with conventional maintenance process i.e. self-maintenance triggering, remote adjustment, self-alignment with remote supervision, set call for maintenance service or intervention and self-resource management and preparation.

Data Management There are lot of issues and challenges related to data handling, processing, storage and analysis due to the veracity, volume and velocity of data. These issues are due to •

•

•

Disorganization of data: At each stage of the maintenance process, data is either generated or leveraged from the previous stages. There are issues related to streamlining the data within whole process due to lack of proper structure and framework interconnectivity-workflow within the maintenance process itself Human Machine Interface (HMI): The use of maintenance information, recordings and measurements gathered by the Human is still a challenge within the scope of data management model. These challenges arise due to lack of seamless interface between human and machine, lack of common interaction data platform and format, and control logic for data integration between human and machine, etc. Data quality and data quantity: Though the sensors can give lot of information on the condition of the assets, there are still lot of issues related to data quality such as reliability of the sensor data, accuracy and precision of the data, applicability of the data, technical feasibility of data handling, incorrect and non-standard data transformation

Standardization of Framework and Solutions The effective implement of Industry 4.0 can be achieved by properly integrating the enabling technologies such as IoT, AI, Big data, and CPS, etc. But these technologies itself suffers from standardization issues related to development protocols, methods and processes. Furthermore, the high-level integration of these technologies further complicates the process of standardization of different devices/modules incorporating with the Industry 4.0. These standardization issues will result in problems such as failures within device activations, data accuracy, delay in processing the maintenance process, incorrect mutual understanding of technical jargon, incorrect condition assessment of assets that lead to inaccurate decisions which often doesn’t solve main problems.

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Static Maintenance Process Information The assumption of static maintenance process information is an issue that remains unaddressed in industry 4.0 development and implementation. This is a very relevant issue especially in scenarios where external maintenance service is needed. There is a need to handle and model maintenance data such as resources, documentation, standards, equipment, budget, crews in a dynamic way because their availability, content or condition changes with time. This makes the integration of maintenance process into industry 4.0 framework very crucial.

Operation Based Framework Most of the Industry 4.0 solutions are developed using frameworks based on the operation of the asset with good insight on the health condition but without holistic view of the required maintenance process. This makes the entire loop incomplete in several instances as the decision making is not connected to the maintenance actions and support resources namely machines, consumables, manuals and standards, and maintenance crew, etc. This issue can be solved by adapting standard maintenance process and its element, defining its data and information requirements from relevant sources and incorporating it into the data management protocol. This can be achieved by integrating an effective maintenance process into the Industry 4.0 framework during the design and development stage.

Integration of Maintenance process into I4.0 The effective implementation of maintenance process within Industry 4.0 can address some of the above issues and challenges of industry 4.0. This can be achieved by mapping the individual task to be performed at each stage of the maintenance process to the 5Cs of the cyber-physical systems. A conceptual method for the integration of the different elements of the maintenance process according to EN standard with the five levels (5Cs) of the CPS-based Industry 4.0 framework is presented in Table 1 and described below. For each stage of the maintenance process, there is a list of activities to be assured at the different levels of the CPS-based Industry 4.0 framework. Incorporating these tasks in the design and development of industry 4.0 framework will make the solution of equal benefit to operation process and maintenance function. The mapping in Table 1 entails identifying, relating and integrating the elements of a standard maintenance process to the industry 4.0 framework in a holistic way. Further, the input and output data for the constituent elements of the maintenance process is specified and linked to the appropriate component of the I4.0 architecture. This linkage also includes the definition, format, structure, type, acquisition frequency and source of the data. For example, for the “Maintenance Support Planning” elements of maintenance process, the data and information regarding defined maintenance tasks, required resources, task costs, respective operational and environmental conditions are collected at the Connection Level. The data collected for maintenance support planning are thereafter processed and manipulated at the conversion level to extract actionable information and relevant indicators for each system and its critical component. The information extracted can be an index such as item criticality, risk levels, remaining useful life (RUL), inventory levels etc. At Cyber Level, the indicators from conversion level are computed at higher system level for example plant or network level. Index such as criticality and risk level of the plant can be estimated, and systems criticality comparison can be done at the same time. This will provide the required input for the cogni91

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tion level or the decision optimization module of the I4.0 architecture. At the cognition level, the output of cyber level process and an overview of the system architecture is required for the optimization and planning of respective maintenance tasks. The cognition level handles the following activities as relevant for maintenance planning: scenario generation, simulation, optimization, analysis and assessment, costbenefit analysis and collaborative decision modelling. It is important to mention that activities at this level is considered common for all the maintenance process elements but the objectives, input data and final decision may differ. At the Configure Level, the asset is expected to respond according to the condition and maintenance planning information gathered and processed. In some instances, no response is required as this information will be transferred to subsequent maintenance process stage (i.e. maintenance scheduling) while in other instances specific action is triggered by the asset itself in the form of: • • •

Self-awareness of the possible consequences of different planning scenarios Self-adjustment of loading and operating conditions based on processed planning information Self-operational and functional trade-offs to meet support planning limitations

Table 1 presents the constituent activities of all the elements of a standard maintenance process in connection to the CPS framework within an industry 4.0. However, there is a need to adapt it to prevailing implementation conditions and environment. This might require combination of some of the elements of maintenance process since there might be is no distinct difference between these elements in some organizational set-up.

CONCLUSION A foremost concept that is setting the pace and direction for many sectors and services is industry 4.0. The focus is on augmenting machines, infrastructure and systems with wireless connectivity and sensors to monitor, visualize and communicate incidences between different entities for decision making. This new trend entails the use of enabling technologies process, and models in the form of internet of things (IoT), cyber-physical systems (CPS), big data and data mining, cloud computing, context-aware systems and artificial intelligence (AI) to facilitate data exchange and automation. This disruptive revolution has caused substantial evolution in physical asset management. An aspect of physical asset management that has been enormously influenced by the new industrial set-up is the maintenance process. Hence, there is a need to adopt the concept of maintenance process according to the international standard EN 60300-3-14 into Industry 4.0 framework. This book chapter addressed several issues and challenges in the Industry 4.0 framework with respect to maintenance process that need attention to improve its value adding capability and productivity. Furthermore, a conceptual integration of the different elements of the maintenance process according to EN standard with five levels (5Cs) of CPS based I.40 architecture is proposed to address some of the issues raised. This procedure will help to assure a functioning maintenance process to support effective and efficient decision process within the scope of I4.0.

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Assessment of • Maintenance report quality for each system • Maintenance task effectiveness for each system

Assessment of • Indicators (KPIs)* • Maintenance efficiency * • Maintenance effectiveness * • Safety and environmental performance* * These activities are carried out at system and network level for conversion and cyber levels respectively.

Collection of • Realtime maintenance report • Observations and recordings • Maintenance process times • Information about troubleshooting, testing and checkout

Gathering of • Information about relevant KPIs • Data required for KPIs assessment • Benchmarks and standards • Operational, safety and environment procedures

Gathering of • OEM manuals • Regulations • Standards improvement models • Maintenance, safety and environment procedures • Design specifications

Maintenance Preparation

Maintenance Execution

Maintenance Assessment

Maintenance Improvement

Assessment of • Indicator about need for system improvement • Indicator about required level of system improvement

Estimation of • Inventory levels • Resource availability performance • Personnel availability performance • Skill level

Acquisition of • Inventory data • Availability of the asset (maintenance window) • Communication channels about resources • Information about tools, transportation, lifting and support equipment • Safety, Health and Environment procedures and work plan • Training resources

Maintenance Support Planning

Assessment of • Indicator about need for network improvement (comparison and aggregation) • Indicator about required level of network improvement

Assessment of • Overall quality of maintenance reports pat network level • Maintenance effectiveness at network level

• Inventory levels at network • Resource availability performance for network • Personnel availability performance for network • Skill level

Assessment of • Plant or network criticality • Risk comparison at network level • Remaining useful life at network level • Maintenance task/support requirements at network level • Inventory levels at network

Estimation of • Item criticality • System risk levels • Remaining useful life (RUL) • Maintenance task/support requirements • Inventory levels

Collection of information about • Maintenance resource and equipment capacity, reliability and availability • Relevant maintenance tasks • Task and component costs • Operational profile • Environmental conditions • Documentation (standards, manuals, thresholds, etc.) • Inventory

• Assessment of maintenance goal • Business and finance analytics for assessment of budget effectiveness • Business risk analysis

Cyber

Maintenance management

Conversion Extraction of • Maintenance performance index • CAPEX/OPEX • Human performance index

Connection

Acquisition of information regarding • Maintenance policy • Organization structure • Financing and Budgeting • Insourcing/ outsourcing • Maintenance supervision structure

Process/Level

Table 1. Integration of maintenance process into I4.0

• Scenario generation, simulation, optimization, analysis and assessment • Visualization to all stakeholders • Cost-benefit analysis • Multi-objective optimization • Collaborative decision modelling

Cognition

• Modifications to improve functionality by redesigning and resource management

• Self-adjustment, correction and alignment

• Self-trigger for service and intervention

• Self-preparation for resource training and skill • Self-allocation of inventory • Self-optimizing of loading and operating conditions • Self-operational and functional trade-offs to meet preparation limits

• Self-awareness of possible consequences of different scenarios • Self-adjustment of loading and operating conditions • Self-operational and functional trade-offs to meet support planning limitations

• Self-optimization for disturbances within organizational limitations • Self-adjustment of configuration with specific budget

Configure

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Seneviratne, D., Ciani, L., Catelani, M., & Galar, D. (2018). Smart maintenance and inspection of linear assets: An industry 4.0 approach. Acta Imeko. Thaduri, A., Famurewa, S. M., Verma, A. K., & Kumar, U. (2019). Process mining for maintenance decision support. In System performance and management analytics (pp. 279–293). Springer. Thaduri, A., Galar, D., & Kumar, U. (2015). Railway assets: A potential domain for big data analytics. Procedia Computer Science, 53, 457–467. doi:10.1016/j.procs.2015.07.323 Thaduri, A., Galar, D., Kumar, U., & Verma, A. K. (2016). Context-based maintenance and repair shop suggestion for a moving vehicle. In Current trends in reliability, availability, maintainability and safety (pp. 67–81). Springer. doi:10.1007/978-3-319-23597-4_6 Thaduri, A., Kumar, U., & Verma, A. K. (2017). Computational intelligence framework for contextaware decision making. International Journal of System Assurance Engineering and Management, 8(4), 2146–2157. doi:10.100713198-014-0320-8 Thoben, K., Wiesner, S., & Wuest, T. (2017). “Industrie 4.0” and smart manufacturing-a review of research issues and application examples. International Journal of Automotive Technology, 11(1), 4–16. Trappey, A. J., Trappey, C. V., Govindarajan, U. H., Chuang, A. C., & Sun, J. J. (2017). A review of essential standards and patent landscapes for the internet of things: A key enabler for industry 4.0. Advanced Engineering Informatics, 33, 208–229. doi:10.1016/j.aei.2016.11.007 Tretten, P., & Karim, R. (2016). Project: iMain ‘Methodologies and tools for the sustainable, predictive maintenance of production equipment’. Academic Press. Wan, J., Tang, S., Li, D., Wang, S., Liu, C., Abbas, H., & Vasilakos, A. V. (2017). A manufacturing big data solution for active preventive maintenance. IEEE Transactions on Industrial Informatics, 13(4), 2039–2047. doi:10.1109/TII.2017.2670505 Winters, R., Adae, I., & Silipo, R. (2014). Anomaly detection in predictive maintenance anomaly detection with time series analysis. Paper presented at the Knime. Zhou, K., Liu, T., & Zhou, L. (2015). Industry 4.0: Towards future industrial opportunities and challenges. 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2147-2152. Zou, C., Deng, H., & Qiu, Q. (2013). Design and implementation of hybrid cloud computing architecture based on cloud bus. 2013 IEEE 9th International Conference on Mobile Ad-Hoc and Sensor Networks, 289-293. 10.1109/MSN.2013.72

This research was previously published in Applications and Challenges of Maintenance and Safety Engineering in Industry 4.0; pages 49-69, copyright year 2020 by Engineering Science Reference (an imprint of IGI Global).

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Chapter 6

How Industry 4.0 Changes the Value Co-Creation Process Rebecca Castagnoli University of Turin, Italy Giacomo Büchi University of Turin, Italy Monica Cugno https://orcid.org/0000-0001-8305-8248 University of Turin, Italy

ABSTRACT The chapter analyses the literature on Industry 4.0 to understand the effect that Industry 4.0 has on customer co-creation process. The chapter is conceptual and is based on a literature analysis—conducted through ISI-Thompson Web of Science—that answers two research question: (RQ1) if and (RQ2) how the Industry 4.0 changes the customer value co-creation process. The results are summarized into a conceptual framework that shows how Industry 4.0 transforms the creation of value for customers, of customers, and with customers. The implications encourage managers and policymakers to implement a wider range of enabling technologies along the various phases of the supply chain and to adopt a new way to manage the company itself and the relations with customers involving them in the co-creation of products.

INTRODUCTION The Fourth Industrial Revolution or Industry 4.0 (Kagerman, Helbig & Wahlster, 2013), has profoundly modified the factory by transforming it into a smart factory. The new scenario comes from the convergence of different emerging technologies that allow the transition to a digitalized era that introduces in the factories a smart environment in which machines, devices and products are interconnected to adapt, be flexible and respond quickly to market changes (Wei et al, 2017). Industry 4.0 has received increasDOI: 10.4018/978-1-7998-8548-1.ch006

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 How Industry 4.0 Changes the Value Co-Creation Process

ing attention from policy makers, managers and academics. Current researches are mainly focused on technological development (more than 50% of existing studies are of engineering), while are still limited studies on how the changes in technical and production developments transform the factory, the management, the relations with stakeholders and the way value is created. Management research explores the phenomenon almost exclusively through conceptual studies or case studies that verify the effects of Industry 4.0 on individual and isolated aspects of the value creation such as the customer relationship management (Dukić, Dugandžić & Dukić, 2017), the business model innovation (Müller et al., 2018) and the service dominant logic (Bullinger et al., 2017). Industry 4.0 modifies genetic heritage and strategic design of the factory creating new opportunities and threats that need to be managed. Early studies show that these changes have a significant impact on relationships with several stakeholders. For instance, Industry 4.0 introduces controversial changes in the relationships with internal stakeholders reducing low skill occupations and introducing new high skill occupations. However, since the 90-95% of the value of a company is made by customers (Gupta & Lehmann, 2006), the paper aims to fill the gap investigating the potential changes that Industry 4.0 brings to the customer value co-creation process. The paper has two research questions: Research Question One: Does the Industry 4.0 changes the customer value co-creation process? Research Question Two: How does the Industry 4.0 changes the customer value co-creation process? The paper is conceptual and is based on a literature analysis conducted through ISI-Thompson Web of Science database to identify two research objects: (1) to analyze opportunities and threats of the single enabling technologies; (2) to identify if and how the Industry 4.0 modifies the customer value co-creation process. The research is based on a critical review because there are only few studies that analyze the topic and the most of them are based on specific case studies. In addition, the studies identified analyze how Industry 4.0 changes individual aspects of the value creation, of the business model innovation, or of the supply chain. However, there are no studies that analyze, with a holistic approach, the specific implications of the Industry 4.0 in the customer value co-creation process. The contribution to scholarship of the paper is to create an abstracting description of the Industry 4.0 phenomenon (finding its definitions and its main characteristics and mapping its opportunities and threats) and to re-conceptualize the existing theory on customer value co-creation process in the light of the Industry 4.0. The originality of the paper is that it has reconstruct a conceptual framework, with a holistic approach, on opportunities and threats that Industry 4.0 has on customer value co-creation process. Figure 1. Conceptual framework

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The paper is divided into five paragraphs. The second paragraph summarizes the theoretical background on Industry 4.0. The third paragraph responds to research object 1 by describing the opportunities and threats of Industry 4.0. The fourth responds to research object 2 by outlining the theoretical framework. The fifth paragraph reports discussions and conclusions enriched by practical implications, limits and future research developments.

THEORETICAL BACKGROUND ON INDUSTRY 4.0 Industry 4.0 is a controversial process by nature, definition, enabling technologies, opportunities and threats. The expression the ‘Fourth Industrial Revolution’ took on the current name of Industry 4.0 after Germany’s industrial plan (Industrie 4.0, Kagermann, Helbig & Wahlster, 2013). Actually, Industry 4.0 also has other names used in different countries, such as Industrial Internet or Advanced Manufacturing (US), Factories of the Future (European Commission), Future of Manufacturing (UK) or more used terms such as Digital Factory, Digital Manufacturing, Smart Factory, Interconnected Factory, Integrated Industry, Production 4.0, Internet Plus. No conceptual or operative definition of Industry 4.0 has yet been identified so far that is universally accepted (Piccarozzi et al., 2018). This is because of: the many enabling technologies that it is made up of, in fact more than 1200 enabling technologies are estimated (Chiarello et al., 2018); the rapid obsolescence of its innovations; the variety of domains where it can be applied such as smart products and services (Schmidt et al., 2015; Porter & Heppelmann; 2014), smart objects (Atzori et al., 2014), smart machines and factories (Kagermann et al., 2013), smart manufacturing and industry (Davis et al., 2012), smart spaces (Leminen et al., 2012) or smart cities (Letaifa, 2015); the different disciplines that analyze the subject such as engineering, ICT, economics, management, etc.; the different points of view of the various stakeholders such as policy makers, managers, entrepreneurs and academics. However, it is possible to determine certain common elements such as automation systems, Internet, connections between the physical and virtual worlds, recognition of a set of enabling technologies, digitalization, changes in the relationships with stakeholders and in the governance and some common principles such as smartness, interoperability, virtualization, decentralization, real-time capability, service orientation and modularity (Hermann et al., 2015) and controlling complex systems (Müller, Kiel & Voigt, 2018; Liao et al., 2017). In particular, the introduction of the internet of things realizes an environment 4.0 establishing global networks that incorporate machinery, warehousing systems and production facilities in the shape of Cyber-Physical Systems (CPS) and Cyber Physical Production System (CPPS). In the environment 4.0, the Cyber-Physical Systems are based on smart machines, storage systems and production facilities capable of autonomously exchanging information, triggering actions and controlling each other independently. This allows improvements of the industrial processes involved, optimizing value chain and supply chain (Kagermann, 2013). A more embracing definition of the phenomenon Industry 4.0 is the adoption of industrial automation systems over the entire value chain and throughout the product’s life cycle (Liao et al., 2017; Reischauer, 2018; Yin et al., 2017). The two key factors for Industry 4.0’s success are: integration and interoperability (Lu, 2017; Lasi et al., 2014; Wei et al., 2017). 99

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Integrating industrial automation systems results in greater and more innovative features via networking with stakeholders (both horizontally and vertically). It also helps to create connections between the cyber and physical worlds. CPS and CPPS are based on the creation of a virtual image of all physical non-human components involved in the production process, such as machinery, plant, products and devices. These physical components do not only exist as we perceive them with the five senses, but they also exist within a virtual image found in the world of information technology that represents all the possibilities and capabilities of the physical components as well as their current states. On the basis of the information provided by the virtual image, the single decentralized physical component is able to make decisions autonomously and to communicate them directly to nearby physical components (Lasi et al., 2014). Interoperability in fact facilitates production processes, even without continuity, within and beyond the bounds of a business interconnecting systems and exchanging know-how and skills. Industry 4.0 uses a series of enabling technologies which can be categorized into ten macro-categories: advanced manufacturing solution, augmented reality, internet of things, big data analytics, cloud computing, cyber-security, additive manufacturing, simulation, horizontal and vertical integration, other enabling technologies. The first nine categories come from a study by the Boston Consulting Group (Rußmann et al., 2015) and some Authors (Wan et al., 2015; Kinsy et al., 2011) add the last one ‘Others enabling technologies’ that include a series of no-less significant innovations, but with limited application domains: agri-food, bio-based economy, technologies supporting the optimization of energy consumption (Maksimchuk & Pershina, 2017; Birkel et al., 2019).

METHODOLOGY The paper is conceptual and aims at answering two research questions. Research Question One: Does Industry 4.0 change the customer value co-creation process? Research Question Two: How does Industry 4.0 change the customer value co-creation process? Starting from these RQs the study is divided into two main parts answering two research objectives. Research Objective One: To analyze opportunities and threats of the single enabling technologies. Research Objective Two: To identify which elements of the customer value co-creation process are modified by Industry 4.0. The first part creates an abstracting description of the Industry 4.0 phenomenon (mapping its opportunities and threats). The second part re-read the existing theory on customer value co-creation process in the light of the Industry 4.0. Both the research parts are based on a critical review that aims to reconstruct a conceptual framework on the possible influence that Industry 4.0 has on the customer value co-creation process. The research is based on a critical review because there are only few studies that analyze the topic and the most of them are based on specific case studies. In addition, the studies identified analyze how Industry 4.0 changes individual aspects of the value creation, of the business model innovation, or of the

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supply chain. However, there are no studies analyzing with a holistic approach the specific implications of the Industry 4.0 in the customer value co-creation process.

RESEARCH OBJECT 1: OPPORTUNITIES AND THREATS OF ENABLING TECHNOLOGIES With lab tests results, managerial literature on Industry 4.0 shows how openness to enabling technologies in one or more phases of the value chain allows a business to obtain some opportunities. These opportunities can be classified into six main macro-typologies (Davies, 2015; Mejtoft, 2011): production flexibility (manufacturing small lots); speed of serial prototypes; greater output capacity; reduced set-up costs and fewer errors and machine stoppages; higher product quality and less production rejects; clients’ better opinion of products. However, the current literature on Industry 4.0 does not report in a holistic way the opportunities and threats of the individual enabling technologies. The paper reconstructs them in Table 1 thanks to a precise examination of the literature on the individual enabling technologies.

RESEARCH OBJECT 2: TO IDENTIFY WHICH ELEMENTS OF THE CUSTOMER VALUE CO-CREATION PROCESS ARE MODIFIED BY INDUSTRY 4.0 Traditionally, the value of companies has always been created within the company itself with minimal customer interaction. However, the process has evolved as a result of three technological developments: first the Internet, then Web 2.0 and finally Industry 4.0. This is how co-creation develops, based on the collaboration between the actors (producers and consumers) facilitated by technology. The third phase of development of co-creation, Industry 4.0, adds a new dimension to the ordinary “anytime, anyplace connectivity for anyone”, to include this type of connectivity for everything. (Mejtoft, 2011). The customer value co-creation process approach sees the participation of customers in the value creation processes as receiving, generating and co-producing subjects of value. The three elements feed into each other creating a result that is superior to the sum of the parts. On the other hand, if left to chance or if improperly managed, they can cause conflicting effects that risk amplifying negative effects by creating an impoverishment of value more than proportional. The perspective is based on the observation that a loyal and collaborative customer base is one of the main forms of wealth of the company not only for the contribution it makes in terms of current cash flows but also for the potential and further increase in value that the stability of relations with customers can generate over time (Gupta & Lehmann, 2006). Depending on the role played by the customer, it is possible to distinguish between three value configurations: value for customer; value of customer (or customer equity), co-creation of value with customer. The plurality of opportunities and threats derived from the enabling technologies of Industry 4.0 and the smart environment in which people, objects, machines, products and plants are interconnected even without solutions of continuity impact on the customer value co-creation process and this requires the need to realize a conceptual framework.

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102 Threats

-problems linked to a reduction of low-skilled employees.

The literature analysis didn’t found any threats related to this technology. However, the threats could be linked to the general problems of Industry 4.0 such as privacy implications etc.

-greater risk of hacking; -privacy issues: need to protect the bigger exchange of data throughout the supply and distribution chains.

-privacy issues; -possible threats to democracy in the absence of transnational regulations; -lack of skills because big data present the need for companies to have the in-house skills to analyse the information.

Opportunities -Reduction of set-up cost, errors and machine downtime, given by the capacity to learn the tasks from the operator; -Flexibility, given by direct participation of men in the most complex work and control phases and by the elimination of structural and technological constraints of automatic and fixed systems; - Higher production capacity, given by the possibility to modify the criteria for the attribution of work activities between operator and machine and to allow greater efficiency and effectiveness of work. -Higher speed in prototyping, given by the possibility to design products and process with augmented-virtual reality; - Reduction of set-up cost, errors and machine downtime + Better product quality and less production waste, due to the possibility to receive information in real time and to provide virtual training and consequently improving work procedures and decision-making processes. -Higher product evaluation by the customer, given by: the greater knowledge of customer needs and preferences with the aim of personalizing products; the inclusion of the customers in the production (co-creation of value); the greater guarantee regarding origin, use and destination of products, guaranteeing effective traceability of the product from the factory to the customer; -Reduction of set-up cost, errors and machine downtime + Better product quality and less production waste, due to the greater interconnection along the supply and distribution chain and due to the ability to reveal machinery wear, tear and breakdown in real time allowing for preventive/predictive maintenance. -Higher product evaluation by the customer due to a faster communication and customized products and to the capacity to profile customers and relative needs; - Flexibility due to the possibility of demand estimation; - Better product quality and less production waste optimizing supply chain thanks to improved efficiency of the warehouse, distribution and sales and thanks to the contained production costs.

Definition

The term advanced manufacturing solution refers to the creation of interconnected and modular systems that guarantee automated industrial plans. These technologies include automatic material-moving systems and advanced robotics that are now on the market as cobots (collaborative robots) or automated guided vehicles and unmanned aerial vehicle.

The concept of augmented reality indicates a series of devices that enriches (or lessens) human sensory perception via access to virtual environments and accompanied by elements of sound, smell, touch, etc. These elements can be added to mobile devices (smartphones, tablets, pcs) or other sensors for vision (augmentedreality glasses) or sound (earphones) or touch (gloves) that provide multimedia information.

The term internet of things (IoT) corresponds to a set of devices and intelligent sensors that facilitate communication between people, products and machines.

The concept of big data analytics (BDA) is related to the technologies that capture, archive, analyze and disseminate large quantities of data that derive from products, processes, machines and people interconnected and the environment around a company.

Macro-categories of enabling technologies

Advanced manufacturing solution

Augmented reality

Internet of things

Big data analytics

Table 1. Macro-categories of enabling technologies: definition, opportunities, threats and authors

continues onf ollowing page

Lee, 2015; McAffee & Brijolfsson, 2012a; McAffee & Brijolfsson, 2012b; Wamba et al., 2015; Yadegaridehkordi et al., 2018

Gershenfeld & Euchner, 2015; Euchner, 2018; Rong et al., 2015; Lu, Papagiannidis & Alamanos, 2018

Gorecky et al., 2014

Durakbasa et al., 2013; Szalavetz, 2018

Authors

 How Industry 4.0 Changes the Value Co-Creation Process

- Reduction of set-up cost, errors and machine downtime + Better product quality and less production waste due to: lower cost; ability to self-learn in order to identify, diagnose and solve problems; better connections in the in-coming and out-going supply chains; -Higher production capacity and increased productivity.

- Better product quality and less production waste, to optimize productions and to decrease waste costs.

The integration offered by Industry 4.0 is characterized by two dimensions: one internal, the other external. The first, called horizontal integration, concerns the integration and exchange of information between the different areas of the company. The second, called vertical integration, concerns the relationships with suppliers and customers.

Other enabling technologies are several technologies used for specific fields (see agrifood, bio-based economy, …) and tools determining where, when and how energy resources are used with the aim of eliminating or reducing waste.

Horizontal e vertical integration

Other enabling technologies

Source: own elaboration

-Higher speed in prototyping that increase production times; - Reduction of set-up cost, errors and machine downtime.

Simulation

The literature analysis didn’t found any threats related to this technology. However, the threats could be linked to the general problems of Industry 4.0 such as privacy implications etc.

The literature analysis didn’t found any threats related to this technology. However, the threats could be linked to the general problems of Industry 4.0 such as privacy implications etc.

The literature analysis didn’t found any threats related to this technology. However, the threats could be linked to the general problems of Industry 4.0 such as privacy implications etc.

The term simulation means reproducing the physical world into virtual models and allowing operators to test and optimize the settings in order to obtain materials, productive processes (discrete elements) and products (finished or distinct elements).

Additive manufacturing

-possible inefficiencies due to the lack of economies of scale. Their use is generally limited to prototypes, high-value products or spare parts for products no longer available and for customised production.

-Higher speed in prototyping due to faster times in complex design and prototyping phases; -Reduction of set-up cost, errors and machine downtime + Better product quality and less production waste creating small customized production lots with possible advantages in terms of lower production costs and waste and eliminating the separation between manufacturing and assembly phases allows a significant reduction in lead time between order and delivery.

Additive manufacturing is a process of additive production allowing for complex products through depositing layers of material including different types of material (plastic, ceramic, metals, resins, …) onto each other thus eliminating the need to assemble the material. A significant example is 3D printing.

Maksimchuk & Pershina, 2017

Anderl et al., 2018; Lu, 2017

Rodič, 2017

Lasi et al., 2014; Weller et al., 2015; Janssen et al., 2014; Sasson & Johnson, 2016; Laplume et al., 2016; Borger et al., 2016; Baumers et al., 2016; Jiang, Kleer & Piller, 2017

Tuptuk & Hailes, 2018

These technologies are designed to support others by limiting the threats linked to the ever increasing spread of information.

These technologies are designed to support others by limiting the threats linked to the ever increasing spread of information.

The concept of cyber-security includes security measures designed to protect the flow of information over interconnected corporate systems.

Cyber-security

Authors

Mitra et al., 2018; Nieuwenhuis et al., 2018

Threats

The opportunities and threats of these technologies can be added to those big data analytics and the internet of things technologies.

The opportunities and threats of these technologies can be added to those big data analytics and the internet of things technologies.

Opportunities

Cloud computing

Definition

Cloud computing technologies facilitate the archiving and processing of large quantities of data with high performance in terms of speed, flexibility and efficiency. Cloud computing also results in developing a greater number of services based on data for the productive system including monitoring and control functions in order to ensure quality and improve operative and productive excellence.

Macro-categories of enabling technologies

Table 1. Continued

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Value for Customer The value for customer is based on the company’s ability to deliver a system of supply to which customers assign, at the level of perception, a value in use greater than or less than the expected value. A company that wishes to manage relations with its customers will have to make available to the customer a value proposal (value offer) that allows to create a satisfaction for the customer and that the offer over time can be able to create a degree of trust and loyalty. Industry 4.0 impacts on the value of the offer in four areas. 1. Increased knowledge of the customer’s needs: A set of technologies, among which big data and the Internet of Things play a predominant role, allow for better profilation of customers and of their needs. 2. Impact on product regards three areas. a Product development. Enabling technologies of Industry 4.0 (such as augmented reality) allow customers to actively participate in the prototyping and testing phase of products. b Product realization. Industry 4.0 allows the realization of production, even without spatial continuity, within and beyond the company’s borders, thanks to the combination of three production scenarios (Büchi, Cugno & Castagnoli, 2018): the traditional mass production (scenario i) can be joined with mass customization (scenario ii) and mass personalization (scenario iii). Mass customization, is where products are made to satisfy the need of individual customers whose efficiency of production is near the mass (production) but with limited volumes (Fogliatto, da Silvera, & Borenstein, 2012; Tseng, Jiao & Wang, 2010). Mass personalization is where products and purchase experiences are created for individual customer tastes based on their preferences and in contained volumes (Tseng, Jiao & Wang, 2010; Chellappa & Sin, 2005). Mass customization and mass personalization help implement a varied product range – from many of a kind to one of a kind – which can then be adapted as demand changes over time, leading to further reductions of average unit costs. c Product differentiation. Enabling technologies of Industry 4.0 also increase the number and quality of pre and post sales services that are added to the core product through CPS-based platforms. These services provide greater benefits to the customer and a greater differentiation of its offer compared to that of competitors. This implementation of services is strictly linked to the service dominant logic in two ways. Firstly, the co-creation itself is considered as a service offered to the client in a broad sense. Secondly although product personalization and customization can concerne physical and technological features of a product, it is evident that in many cases the personalization can consist of one or more service linked to the product.

Value of Customer The value of the customer or customer equity refers to the ability to define and monitor the economic value of the customer base during the life cycle of the relationship, through the configuration of specific measures. The customer’s life cycle in relation to the time and value created for the company generates a cost of customer acquisition for the company. Subsequently, the break even point is reached, after which the customer becomes profitable. 104

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To increase profitability, it is necessary to reduce acquisition costs - acquisition strategy -; bring the break-even point of the customer’s profitability closer; increase customer loyalty, generating more sales through up-selling, cross-selling and trading-up. These three objectives can be pursued individually or through a combination of these - loyalty strategy - to increase the company’s profits. The creation of customer value, however, should not be limited to considering only the historical and prospective value of the cash flows that each customer generates for the company. The evaluation of these should also highlight the intangible values that they are able to exercise, in terms of reputation and contributions to strengthening the image, the ability to generate input to innovation and learning (Niray et al., 2001). Enabling technologies of Industry 4.0 enable the company to improve customer loyalty strategies through the use of big data, cloud computing and the Internet of things. On the other hand, the same technologies also allow customers to obtain more information in real time on the offer of the various manufacturers on the market. This means that the life cycle of the customer changes significantly, as customers can choose to change supplier at any time. Customer loyalty tends to be more variable with the risk for companies to lose their customer base more quickly.

Value with Customer The creation of value with the customer is based on the ability of the company to build and manage a space around the customer’s experiences, through interactions with the company. The customer becomes a co-creator and co-producer of the system of offering of the company of which he is a user (Prahalad et al. 2000, 2003 and 2004; Bendapudi et al., 2003). In the context of Industry 4.0 the creation of value with the customer becomes more relevant because some technologies, such as additive manufacturing or augmented reality, allow the decentralization of production and the direct involvement of the consumer in the conception and design of the final product.

DISCUSSION AND CONCLUSION In the conclusions, the results are summarized with particular attention to the comparison with the existing literature on the topic. As a preliminary observation, it must be underlined that the paper discusses as a main goal the relationships between Industry 4.0 and co-creation process analyzing if and how Industry 4.0 changes the value creation for customer, of customer and with customer. Since this literature is still limited with reference to the purposes of the paper, it is also analyzed the general literature related to co-creation, servitization and business model innovation. Answering to the RQ1 (Does the Industry 4.0 changes the customer value co-creation process?) it is possible to state that as the previous Industrial Revolutions, Industry 4.0 transforms workplaces and society implying social, environmental and economic changes (Kiel et al., 2017) and modifies the value creation for customer, of customer and with customer. The paper answers the RQ2 (How does the Industry 4.0 changes the customer value co-creation process?) in the following section to better understand how Industry 4.0 changes the value co-creation process (Figure 2).

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Figure 2. Results: Changes of Industry 4.0 on value co-creation process

Source: own elaboration

As the previous Industrial Revolutions, Industry 4.0 aims to transform workplaces and society implying social, environmental and economic changes (Kiel et al., 2017). The productive flexibility allows to improve the creation of value with the customer and for the customer. The increased amount of customer data owned by companies has two consequences: companies can reach new customers who are either or not satisfied with current solutions (Jhonson et al., 2008) or cannot access them (Yunus et al., 2010); companies can increase customer loyalty through more comprehensive value offer (Enkel & Mezger, 2013) or to reduce costs faced by customers (Mitchell & Coles, 2004). In addition, Industry 4.0 allows the supply of customized and personalized products at lower and lower prices in the long run, but these remain strongly influenced by the huge initial investments. For this reason, a strong increase in pre-sales and after-sales services is necessary to justify the price increase due to investments in new technologies (Schlechtendahl et al., 2015; Xu et al., 2014). Adding services to the original products allows companies to create value added or barriers to customer exit (Dukić, Dugandžić & Dukić, 2017; Rennung, 2016). The increasing servitization of production results in a further change in the value creation for the customer. The need of additional services results that is no more enough to create value by identifying customer needs and producing state of the art products. Through a product customers access to a web-based services (Ferber, 2013) and are these services, and not only the product itself, that generate income (Carruthers, 2014). As previously explained, the company modifies the customer value co-creation process through the availability of large amounts of information on customer preferences. On the other hand, customers change suppliers by obtaining real-time information on product characteristics and prices of competitors with lower information costs. This changes some prerequisites of the customer co-creation process with shorter customer’s life cycle and less loyalty in the long run. However, there are also some dark side of the Industry 4.0. These dark sides are mainly related to economic, social, legal/political, ecological, technical risks (Birkel et al., 2019) such as privacy concerns, initial costs and standardization. In addition, there is the network effect occurring when value depend on the number of other users. For Industry 4.0 a driving force but also one of the initial problems for the obstacles above. Finally, the information on customers and on their needs that Industry 4.0 provides

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must be managed and analyzed with the necessary skills in order to respond to the emerging needs by identifying ad hoc solutions. Industry 4.0 promotes the emergence of new business models mainly related to digital platforms. These have the potential to cannibalize existing business models, making implementation difficult. At present, many companies are (still) focused on marketing their products. In the future, the focus will rather be on providing solutions and addressing customer problems. Therefore, companies face the risk that their business models will not be able to adapt quickly enough to these future requirements. At the same time, radical changes are necessary in order to implement new models, which inherently creates severe challenges for companies with a variety of potential issues. Since these developments are difficult to predict, there are particularly considerable risks for the existence of a company (Birkel et al., 2019). The main contribution of the study is the integration of the theoretical framework of the customer co-creation process (Vargo & Lusch, 2008; Grönroos & Voima, 2013) in the context of the transformations of the Industry 4.0. Despite the still limited application of Industry 4.0, the results obtained encourage managers and policy makers to implement a wider range of enabling technologies along the various phases of the supply chain and to adopt a new way to manage the company itself and the relations with customers involving them in the co-creation of products. The limited number of studies currently available on the subject highlights the need for further investigation. In addition, it is interesting to develop empirical research that verifies the validity of the theoretical framework on a sample of companies and customers.

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This research was previously published in Customer Satisfaction and Sustainability Initiatives in the Fourth Industrial Revolution; pages 21-36, copyright year 2020 by Business Science Reference (an imprint of IGI Global).

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Industry 4.0 and Sustainability Sibel Yildiz Çankaya Bolu Abant Izzet Baysal University, Turkey Bülent Sezen Gebze Technical University, Turkey

ABSTRACT Modern industry developed over several centuries and three industrial revolutions. Today, we experience the fourth era of the industrial revolution, Industry 4.0. The advance of industrialization brought along many problems, including environmental pollution, global warming, and depletion of natural resources. As a result, the concept of sustainability began to gain importance. Sustainability can be achieved through a balance between economic, social, and environmental processes. In order to establish such balance, businesses need new business models or insights. At this point, Industry 4.0 can be regarded as a new business mindset that will help businesses and communities move towards sustainable development. The technologies used by Industry 4.0 bear a strong promise to solve these problems, after all. Even though Industry 4.0 attracts a lot of attention lately, few works are available on its impact on sustainability. This chapter examines the impact of Industry 4.0 on sustainability.

INTRODUCTION The first industrial revolution brought many dramatic changes in our world and our lives. As a result of industrialization, urban populations increased and living conditions improved. Industrial production, which expanded with increasing population, brought along some environmental issues over the centuries. The most important issues probably include the pollution of the natural environment, the depletion of natural resources, and global warming. As these issues deepened, the emerging concept of sustainability began to gain importance. Sustainability is defined “meeting today’s needs without compromising the ability of future generations to meet theirs” (Brundtland, 1987). This definition has three dimensions: social, economic, and environmental.

DOI: 10.4018/978-1-7998-8548-1.ch007

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 Industry 4.0 and Sustainability

Sustainability in an enterprise requires sustainable processes throughout the value chain, from product design and processing of raw materials to recycling. As this does not entail a simple transformation, businesses need new business models or insights to implement this. At this point, Industry 4.0 can be considered a new business mindset that will help businesses and communities move towards a sustainable development (Luthra & Mangla, 2018). Industry 4.0 refers to smart factories with machines and production modules capable of communicating with each other and a high degree of automation in the industry, changing the way things work. The aim of this revolution is to provide flexible, fast, and personalized production, as well as efficiency in resource use. With Industry 4.0, production time and cost are expected to decrease, while production quantity and quality are expected to increase. Industry 4.0 recently started to attract a lot of attention and there are not many studies yet on its possible effects on sustainability. However, Industry 4.0 has tremendous potential for the creation of sustainable industries (Kamble, Gunasekaran, & Gawankar, 2018). With smart devices and a smart production system, Industry 4.0 can contribute to improved sustainability by reducing overproduction, material waste, and energy consumption (Branke, Farid, & Shah, 2016; Waibel, Steenkamp, Moloko, & Oosthuizen, 2017; Wagner, Herrmann, & Thiede, 2017). For example, energy consumption (water, electricity, gas, etc.) can be monitored and optimized thanks to new energy management techniques Industry 4.0 supports (Ding, 2018). However, enterprises have difficulties with the disposal of ever-increasing amounts of waste, especially when it comes to keeping control over dangerous chemical waste in developing countries, where accidents are not infrequent. The technologies of Industry 4.0 make waste control and monitoring more effective. Moreover, with automation Industry 4.0 minimizes human errors and maximizes efficiency and quality in production (Ding, 2018). It also helps reduce uncertainty by ensuring that accurate information is transmitted full-time among the members of a supply chain. This data transparency improves accuracy in delivery and reduces waiting time. With respect to Industry 4.0, the aforementioned energy savings, resource efficiency, waste control, and improved delivery are important for both economic and environmental sustainability. Regarding the social dimension of Industry 4.0, several benefits for employees can be listed, including enhanced human learning through intelligent assistance systems or human-machine interfaces. This may help increase employee satisfaction (Herrmann, Schmidt, Kurle, Blume, & Thiede, 2014). Moreover, assigning smart devices and robots to ergonomically unfavorable and physically demanding workstations can provide significant improvement in employee health (Hirsch-Kreinsen, 2014). However, the current literature cannot provide a common perspective on whether Industry 4.0 will increase or decrease the number of employees in industry. While simple tasks are expected to disappear with Industry 4.0, tasks such as monitoring, collaboration, and training are still considered necessary (Kiel, Müller, Arnold, & Voigt, 2017). In short, Industry 4.0 is expected to transform industrial production, as well as the structure of society with its economic, ecological, and social achievements (Kiel et al., 2017; Herrmann et al., 2014). However, as mentioned before, studies in this field are yet few. Industry managers need to achieve a better comprehension of the possible effects of Industry 4.0 on sustainability, if they aim to gain a better edge. Therefore, this study discusses the effects of Industry 4.0 on sustainability. First, it explains on the concept of sustainability, then expands on the concept of Industry 4.0, and finally discusses the effects of Industry 4.0 on sustainability.

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BACKGROUND Sustainability As the impact of human activities and especially industry deepened, environmental problems became the subject of widespread and intensive debate. Global warming, ozone depletion, and the decrease in natural resources and biodiversity are the major environmental problems. These are not restricted to a local or regional scale, but their impact is global. Therefore, communities, governments, and businesses must act to achieve economic and social goals without harming the environment, i.e. to achieve a balanced growth (Eltayeb & Zailani, 2009). This urgency turned the issue of sustainability into an area of​​ interest for governments on the one hand, and academicians and practitioners on the other, for this issue encompasses the themes of environment, profitability, and human elements, after all. Especially since the 1987 Brudtland Report, sustainable development and sustainability are increasingly incorporated into institutional strategies and government policies. The Brundtland Report defines sustainability as meeting the needs of the current generations without compromising the ability of future generations to meet their own needs. This definition has three dimensions: social, economic, and environmental (Brundtland, 1987). Environmental sustainability relates to the environmental impact of organizational activities. The sources of environmental problems (such as production, transportation, supply, and product) need to be identified in order to develop a better understanding of the fundamental environmental problems and devise effective solutions. Industrial businesses consume limited resources and at the same time produce waste that causes environmental pollution. Therefore, the effects of the product on the entire supply chain should be considered with a life-cycle approach (Azapagic, 2003). Economic sustainability is attained when businesses do not cause any harm for the natural or social environment they operate in while pursuing their goals of profit. After all, the heart of sustainable development is the economic aspect of the business which generates profit, creates jobs, and contributes to social welfare in general. Therefore, enterprises face two types of issues: micro and macro. The issues at the micro level include ordinary financial metrics such as shareholder value, turnover, profit, cash flow and sales, which are directly related to the economic performance of the business. Macro-level issues, on the other hand, are generally related to that business’s contribution to employment and the GDP (Azapagic, 2003). Social sustainability is to “ensure efficient use of natural resources today and in the future through developing and preserving the social environment that will support human needs and environmental sustainability” (Buckingham-Hatfield & Evans, 1996). Social sustainability concerns the responsibility of the business for the whole community, both now and in the future.

INDUSTRY 4.0 Four different industrial revolutions took place in history. The first industrial revolution, now referred to as Industry 1.0, began in England, then spread to continental Europe and to the rest of the world. In this era, machines powered with water and steam energy increasingly became dominant in production. As the steam engine became actively involved in the production processes, economies formerly based on agriculture and crafts turned towards an industrial basis. With all the changes this brought about, 115

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Industry 1.0 is seen as a breaking point in history. Industry 2.0 refers to switching to the use of electrical energy in industrial processes and the mass production as a result of Henry Ford’s assembly line based on a conveyor belt system. Industry 3.0 refers to moving towards a technology-intensive system in production and work in general. As for the fourth industrial revolution, according to researchers the internet of objects based on cyber-physical systems is the driving force of Industry 4.0 (Lin, Shyu, & Ding, 2017). Industry 4.0, which was first introduced at the Hannover Fair in Germany in 2011, ushers in a new era with fundamental changes in industries, labor use, and production processes. It refers to smart factories with a fundamentally different way of work and production, a high degree of automation, and machines and production modules capable of communicating with each other. Industry 4.0 is the process of digitizing the entire value chain. In this industrial revolution, people, objects, and systems can be interconnected through a real-time data exchange. Moreover, machines and devices with artificial intelligence (AI) can learn on their own and adapt to changes (Hecklau, Galeitzke, Flachs, & Kohl, 2016). In this era, physical processes involved in production can be monitored, a ‘digital twin’ (or, ‘cyber-twin’) of the physical world can be created, and intelligent decisions can be made through real-time communication and collaboration with people, machines, and sensors. In short, production systems are smarter in Industry 4.0 (Zhong, Xu, Klotz, & Newman, 2017). The main purpose of the Industry 4.0 process is to ensure the emergence of Smart or Dark Factories. Some of the key features can be listed as follows and are often used to describe smart factories in Industry 4.0 (Lin et al., 2017; Bonilla, Silva, Silva, Gonçalves, & Sacomano, 2018): • • • • •

Interoperability: machines, devices, sensors, and people can communicate with each other and work together. Virtualization: a virtual copy of the physical world can be created based on information from sensor data. Technical assistance: systems have the ability to support people in the processes of decision making and problem solving, as well as undertaking the tasks that are too hard or dangerous for humans. Decentralized decision making: cyber ​​physical systems have the ability to make simple decisions on their own and to act as autonomously as possible. Real-time data acquisition: systems have the ability to collect, process, and communicate data in real time. This will allow rapid adaptation to changes.

Industry 4.0 significantly impacts the production environment by bringing fundamental changes to operations. Unlike the traditional predictive production planning, Industry 4.0 enables real-time production plans. With the development of intelligent machines, intelligent storage systems and intelligent production facilities, an integration based on end-to-end information and communication systems can be possible throughout the supply chain from logistics to production and marketing (Sanders, Elangeswaran, & Wulfsberg, 2016). In summary, with Industry 4.0 makes faster, more flexible and more efficient production processes possible, not to mention the higher product quality at a lower cost. The following section describes some concepts/technologies related to Industry 4.0. They are also referred to as the components of Industry 4.0 in some sources: Cyber ​​Physical Systems, the Internet of Things, Cloud Computing, Big Data, Autonomous Robots, Simulation, Augmented Reality, and 3D Printing. Even though these technologies actually emerged during the Industry 3.0 era, their development still continues. 116

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The Components of Industry 4.0 Cyber-Physical Systems (CPS) Cyber-physical systems (CPS) are the structures that involve communication and coordination between the physical and cyber worlds. This concept allows the synchronization of information about a physical workshop and a virtual computational space. They are defined as systems that integrate many processes such as production, distribution, and procurement of factories by creating a digital network structure. This ensures efficiency, transparency, traceability, and controllability of the production process (Hofmann & Rüsch, 2017). Furthermore, it creates intelligent systems (smart factories, houses, cities, etc.) by enabling objects to communicate with one another through the internet and makes it possible to obtain and analyze data.

The Internet of Things (IoT) As a newly developing and rapidly growing technology, IoT receives great attention worldwide. The Internet of Things is defined as “the network system of devices, machines, vehicles, buildings, and various objects to collect and distribute data, and to communicate.” IoT can be seen as a technological revolution in computing and communication. This concept refers to a structure in which objects around us can connect to the Internet and communicate with each other without human intervention (Perera, Liu, & Jayawardena, 2015). In addition to communicating with other objects, they can also use the data other objects generate. Objects and sensors communicate over a wireless network connection such as RFID, NFC, Wi-Fi, Bluetooth, or Zigbee. The Internet of Things can enable data collection from any point in living and working areas. Sensors in the network monitor the conditions of quantity, position, vibration, motion, speed, temperature, pressure, humidity, etc. and communicate the digital results they obtain to relevant points. Clearly, IoT has a broad area of use for businesses. In some businesses, activities such as lighting, heating, machining, robotic vacuums and remote monitoring are performed by IoT (Zhong et al., 2017). Intelligent objects equipped with IoT can provide managers with the necessary data on all processes and situations (from stocks to costs) by monitoring the conditions required for factory production. And outside the business environment, IoT finds many uses in environmental monitoring, object tracking, traffic management, healthcare, and smart home technology (Hong et al., 2014).

Cloud Computing Cloud computing is a new model that provides access to data storage and software applications over the Internet (Mohlameane & Ruxwana, 2014). Today, businesses have to work with large amounts of data from many different sources. Cloud computing makes it possible for them to store these large volumes of data even on low-capacity devices. The cloud where the data is stored can be accessed from any device and location, such as mobile phones, tablets, or computers, as long as they are connected to the Internet. Through cloud computing, users can share information and run applications or programs over the Internet. Cloud computing system provides users as a single server by combining the servers in different physical locations via the Internet (Kumar & Vidhyalakshmi, 2012). Also, when using cloud computing, there is no need to install software on each computer. 117

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Big Data Big data is characterized by high levels of volume (amount of data), variety (number of types), and velocity (speed). Thanks to technologies such as the Internet and IoT, a lot of data is now available, resulting in large data stacks. These data are obtained from a variety of sources, such as sensors, devices, networks, logfiles, transactional applications, the Web, and social media (Zhong et al., 2017). The data collected from these different sources is not only in large amounts, but also of complex quality. Conventional methods of data analysis are therefore inadequate to handle such data. Therefore, big data approaches are needed to analyze and manage these data in a more efficient and effective way. Big data can meet different requirements, in particular combining multiple irrelevant data sets, processing large amounts of unstructured data, and collecting information in a time-sensitive manner. As a result, big data emerged to meet the needs of analyzing, querying, and storing data in order to make better decisions (Song et al., 2017).

3D Printing and Additive Manufacturing (AM) Additive manufacturing, also known as 3D printing technology, attracts increasing attention in several industries. Processing techniques such as cutting, drilling, grinding, and sanding, which are used in traditional manufacturing processes, are called ‘subtractive manufacturing’ because they are based on the method of dismantling parts and components. These parts and components are then combined to form the final products. In contrast, 3D printing creates the final products by forming consecutive layers of material. Therefore, it is called ‘additive manufacturing.’ In this method, since the product is produced in layers, there is no need to assemble parts and components. AM uses computer-aided design software to create a digital model first. The 3D printer then proceeds to create a three-dimensional object from the raw material in the form of a liquid or particle. At this stage, thin layers of raw material are deposited microscopically by the printer. The final product is completed by releasing successive layers (Kamble et al., 2018). Additive manufacturing possibly has three main advantages over traditional production. Firstly, this method eliminates the constraints of traditional production and brings a freedom of design for innovative products (Tang, Mak, & Zhao, 2016). Secondly, AM’s benefits such as reduced transport distances and inventories allow enterprises to increase their margin (Kamble et al., 2018). And thirdly, AM technologies have great potential to reduce the environmental impacts of traditional production (Tang et al., 2016). However, the fact that 3D printers allow the use of only a limited number of material types is one of the challenges of this technology (Kamble et al., 2018).

Autonomous Robots The term robot is used to for machines or devices which are usually made of metal, in various forms, including that of the human, and are capable of doing the work of humans. Today, robots have an indispensible role in modern production. They have a significant contribution to high productivity by reducing labor costs and improving product quality (Bi et al., 2015). Robots can be connected to an operator, as well as functioning completely without operator intervention. Those in this second group, i.e. the robots operating independently of the operator are called autonomous robots. They can also be classified as semi-autonomous or fully autonomous, depending 118

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on their degree of operator-independent functioning. From the robot’s point of view, the concept of autonomy means the ability of the intelligent and communicable machine to make its own decisions and to translate them into action through an embedded artificial intelligence application. Before Industry 4.0, robots were already in use for a long time to perform complex tasks in a variety of industry sectors. But today, they are increasingly autonomous, flexible, and collaborative. Even autonomous robots can interact with each other, work and learn side-by-side with people. Therefore, it can be said that these autonomous robots are much more capable those used in the past (Rüßmann et al., 2015). It is thought that robots will perform tasks such as weight lifting, storage, transportation, or sensitive/ dangerous tasks for humans and also perform them in more effectively and efficient ways than humans. Robots communicate with each other through a wireless network while performing the tasks assigned to them. The main objective is to enable people to communicate with robots and work in harmony.

Simulation Simulation can be defined as the visualization of elements of the physical world in a digital environment. Simulation technologies are already in use for long to make future predictions and perform sensitive vocational training. They are also used in the designing stage of a product. However, in the future, simulations are expected to be used more widely in business operations. These simulations will be able to represent machines, products, and people in real time in a virtual model. This will allow operators to test applications first in the virtual world. For example, the operator can test machine settings for the next product. This can reduce machine installation times and improve quality (Rüßmann et al., 2015).

Augmented Reality (AR) Augmented Reality is another technology that recently attracted attention with its applications in various sectors including education and entertainment. Augmented reality is a live environment where objects are augmented by computer-generated sensory input, such as sound, graphics, video, or GPS data (Jeong & Yoon, 2017). It can also be defined as the change and augmentation of the reality that users feel. This technology extends people’s perceptual experiences in a variety of ways, blurring the distinction between real-world objects or environments and computer-generated virtual environments (Jeong & Yoon, 2017).

IMPLICATIONS OF INDUSTRY 4.0 ON SUSTAINABILITY Environmental Sustainability Manufacturing plays an indispensable role for economies, however, the further it developed, the faster became the depletion of natural resources and the more severe the environmental pollution. Components of Industry 4.0, such as IoT and robots, show great promise for solving these problems. Industry 4.0 contributes to the development of environmental sustainability by providing benefits in many areas from waste to resource management.

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Waste Management Waste management consists of different processes such as collection, transportation, processing, disposal, management, and monitoring of waste materials. Failure to perform these processes effectively both causes pollution and increases the costs (Perera, Zaslavsky, Christen, & Georgakopoulos, 2014). For example, the lack of a good waste collection system brings about serious environmental hazards and significantly cost increases. The decomposition of the waste, which is not collected in time causes the growth of bacteria and viruses and adversely affects public health (Dugdhe, Shelar, Jire, & Apte, 2016). Researchers and practitioners mention that IoT, one of the components of Industry 4.0, can be applied effectively in waste management (Hong et al., 2014). For example, efficient garbage collection planning can be achieved through smart garbage cans which notify cleaning staff when full (Perera et al., 2015). Hong et al. (2014) proposed the IoT-based smart garbage system. The authors state that the system proposed is not only for reducing food waste, but also helps governments save from costs, as well (Hong et al., 2014). IoT technology also facilitates reverse logistics applications. One of the most difficult features of reverse logistics applications is the uncertainty about the quantity of products to be returned. Especially through the IoT installed in electrical and electronic products, businesses can collect all the information they need including initial installation, maintenance, repair, and disposal. As a result, it will be easier to identify and recycle end-of-life products with IoT. In this way, recycling companies will be able to estimate and monitor the amount of waste to be delivered to their facilities (Perera et al., 2014). One other technology that can help with waste management is AM, which produces less waste compared to conventional production (Huang et al., 2016).

Efficiency of Energy and Resources As a result of problems such as increasing energy costs and global warming, energy consumption is at the center of much discussion in recent years. Businesses and governments in particular are looking for ways to use energy and resources in a more efficient and sustainable way. For efficient energy management, it is necessary to access and analyze energy data in real time (Tan, Ng, & Low, 2017). The components of Industry 4.0 have the potential to help businesses and governments in this respect. For example, by developing an intelligent street lighting solution using the Internet of Things, the right lighting level can be achieved (taking into account the city, time of day, season or weather), thus the amount of energy used for lighting can be reduced (Perera et al., 2015). Similarly, this technology can be used to save energy in indoor lighting and heating (Tao, Wang, Zuo, Yang, & Zhang, 2016). In terms of enterprises, energy parameters in the production process can be monitored in real time thanks to raw materials, components, machinery, products and facilities equipped with IoT technology (Tao et al., 2016). This helps businesses find the best solutions to save energy by making it possible to monitor not only the total energy consumption, but the individual energy consumption of all items in the production process, as well. There are two other Industry 4.0 components that work together with IoT: cloud computing and big data. As mentioned earlier, the use of intelligent meters and sensors allows remote monitoring of energy consumption data in factories and cities. Large amounts of data collected from these meters and sensors can be stored in the cloud (Shrouf, Ordieres, & Miragliatta, 2014). In addition, intelligent techniques and algorithms are required to analyze this data stored in the cloud. For example, deep learning algorithms can be used to efficiently analyze large data generated by a large 120

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number of devices (Mehmood et al., 2017). In this way, policies can be formed to optimize resource and energy use. In addition to the ability of storing large amounts of data from IoT technologies, cloud computing decreases hardware use (Issa, Chang, & Issa, 2010). because businesses or individuals using cloud computing do not need frequent hardware updates and can use their devices for many years (Namboodiri, 2010). This will ensure both the efficient use of resources and the reduction of waste. Robots are another component of Industry 4.0 that enables efficient use of resources. They ensure efficient material use by performing the job without errors. Furthermore, compared to humans, robots can further reduce raw material consumption and waste. Despite these positive characteristics, some robots need a lot of energy for operation and release greenhouse gas emissions if they are powered by non-renewable energy (Pan, Linner, Pan, Cheng, & Bock, 2018. Another technology that affects environmental sustainability is AM. Important criteria for environmental sustainability of the production process are the use of energy and materials, product life, waste, recycling, part consolidation and process optimization. Therefore, the sustainability of the production process is largely based on the working principle. Conventional manufacturing is subject to some difficulties and constraints as it is based on subtractive manufacturing. In addition, subtractive manufacturing causes large amounts of waste during production (Ahn, 2016). Unlike subtractive manufacturing, AM, the additive manufacturing contributes positively to the development of environmental sustainability by increasing material and energy efficiency, reducing life-cycle effects, and providing greater functionality in the field of engineering compared to traditional manufacturing. For example, the aircraft industry can improve fuel efficiency by producing some components (such as seat buckles) that can reduce the aircraft mass with AM (Huang et al., 2016).

Reducing Pollution As mentioned before, data are collected from many different sources today. Therefore, big data approaches are needed to analyze and manage data in more efficient and effective ways. Big data can help develop policies in many areas such as minimizing soil erosion, preventing/reducing water, air and soil pollution, climate change, and more efficient use of resources (Song et al., 2017). For instance, farmers can use fertilizer more efficiently by collecting data on the nutritional needs of their fields. In this way, fertilizer is used only in amounts that can be absorbed by crops and the risk of polluting water with excess fertilized is eliminated (Wu, Guo, Li, & Zeng, 2016). By appropriately collecting and using large data, analysts can identify the relationships between them. Then, by analyzing the causality between these data, accurate estimates can be made (Song et al., 2017). In this way, big data can help scientists, policy makers, and city planners to develop environmental policies and strategies. Robots, which are widely used in many fields, also play an effective role in reducing environmental pollution. For example, they make sure chemicals are applied in the exact amounts required and thus fewer pollutants are released into the environment (Comba, Gay, Piccarolo, & Ricauda Aimonino, 2010). AM, another technology, shortens transportation distances by bringing production and consumption points close to each other. In conclusion, the positive effects of Industry 4.0 on environmental sustainability can be summarized as follows (Bonilla et al., 2018): • • •

Resource efficiency Efficient use of materials, water, and energy Use of renewable energy 121

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• • • • •

Compliance with environmental strategies/standards Reliability of environmental cost accounting Development of innovative environmental policies Reduction of environmental impact Reduction of waste and greenhouse gas emissions

Social Sustainability Industry 4.0 can contribute to the development of social sustainability by bringing benefits in many areas, such as community health and well-being. For example, a lot of research is made to develop technologies to complement and reinforce existing healthcare services. Implementation of IoT in the field of healthcare, in particular, can provide many conveniences. An IoT-based healthcare system enables all the resources needed to perform activities such as diagnostics, monitoring, and remote surgeries over the Internet. Thus, health services can be extended from hospitals to homes (Yin, Zeng, Chen, & Fan, 2016). In addition, people’s health status can be monitored instantly through wearable sensors. In this way, a reliable, effective and intelligent health service can be provided to the elderly or people with chronic diseases (Yin et al., 2016). As this system can collect all the data needed, an effective healthcare system can be easily developed. In summary, the use of IoT technologies in the field of healthcare can provide the elderly a safer and more independent lifestyle. Moreover, the installation of smart systems in buildings can help reduce the risk of disasters, such as fire, through an early warning system, increase the comfort levels of individuals living in smart cities, and make daily activities much easier. IoT installed in buildings or bridges can also instantly analyze the possible effects of earthquakes on buildings. Similarly, landslides and forest fires can be monitored, not to mention the possibility of monitoring the quality of air and water with IoT. This data from IoT can be stored in the cloud, allowing new decisions or practices to be implemented to improve human well-being. For instance, new applications can be developed in areas such as air, water, and noise pollution, safety, population movements, traffic or healthcare. Just like IoT and Cloud Computing, robots are also used in healthcare services in addition to many other systems. For instance, magnetic microbots remove plaque from a patient’s arteries or robots such as the Bestic arm help patients eat or regain the ability to walk. Moreover, considering the fact that the population is aging, robots are likely to play a part in the care of the elderly and patients. This is because robots are cheaper to maintain, not bored of repetitive work, do not need to rest and can be trained faster than humans. As a result, they can help nurses provide a better healthcare (Qureshi & Sajjad, 2014). In fact, robots already help employees in many industries. In particular, they have a positive impact on the well-being of employees by performing dangerous, monotonous, unreasonable, and dirty tasks more efficiently and accurately. They contribute to the reduction of the incidences of injury and death in the workplace (Pan et al., 2018). Similarly, augmented reality (AR) applications prevent employees from facing any risk or danger in performing critical tasks. Another technology that can contribute to social sustainability is big data. Studies in this area indicate that big data can be used to predict natural disasters such as earthquakes and tsunami. Moreover, big data helps with predictions about the melting of glaciers, deforestation, and extreme hot or cold weather through the use of satellite images, meteorological radars and terrestrial monitoring devices. Big data is also used in the field of human health. The sum of data on patient health and well-being constitutes the largest data in the health sector. By analyzing these data, chronic and epidemic diseases can be 122

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kept in check, slowed down, or even prevented altogether. For example, some researchers use big data analytics to identify biomarkers of prostate cancer (Mani, Delgado, Hazen, & Patel, 2017). Similarly, it is possible to come across studies indicating the importance of big data in the fight against influenza (Bort, 2012). As a result, big data can reduce health costs and improve the quality and effectiveness of the healthcare system. AM is yet another component with important implications in terms of social sustainability. It can prevent long-term exposure to hazardous work conditions as opposed to traditional production processes (Ford & Despeisse, 2016). At the same time, work is underway to reproduce human organs with 3D printers and transplant them to human body. In this way, organ failure, large burn marks, as well as many other health problems can become history. The positive impacts of Industry 4.0 as well as the possible negative impacts are a matter of debate. There is a consensus that robots will increase the productivity and competitiveness of businesses. But the real debate is whether this will lead to an increase in employment and wages. Autonomous robots, which constantly communicate with each other in industrial processes and have self-decision mechanisms make people worry about unemployment. The view that robots will replace human labor force gains more ground everyday (IFR, 2017). On the other hand, according to the International Federation of Robotics (IFR), the use of robots increased employment in many countries, including South Korea, China, Germany, and the United States. A brief study of employment statistics suggests that there is a small decrease in the manufacturing sector in developed countries (Qureshi & Sajjad, 2014). The use of such technologies leads to the loss of many low-skilled jobs, but also contributes to the development of many new jobs. (Qureshi & Sajjad, 2014). Another important issue concerns wages. Some researchers suggest that if robots replace workers completely, then wages drop, especially in low-skilled jobs (Sachs & Kotlikoff, 2012). An analysis of the economic impact of industrial robots in 17 countries showed that robots did not have a significant effect on the total working hours, but still wages increased in these countries (Graetz & Michaels, 2015). In general, it can be said that while ordinary or dangerous tasks that do not require expertise can be safely performed by robots, humans can turn to jobs that require expertise and creativity. Thus, the workforce will undergo a transformation with high level of expertise, optimizing systems and solving problems that may occur or are likely to occur in the process. As a result, it is certain that robots will create changes in professions and ways of doing business as in all industrial revolutions (IFR, 2017).

Economic Sustainability Industry 4.0 contributes to improving economic performance by enabling businesses to reduce costs and increase sales, productivity, and customer satisfaction. IoT possibly plays an important role, especially in terms of business enterprises. The production and supply chain management of businesses that incorporate the IoT technology into their systems run more smoothly (Perera et al., 2015). With this technology, the visibility of the supply chain increases and businesses can monitor their products in real time. Moreover, they can collect data on their products or the environment such as temperature, light, humidity, or pressure (Perera et al., 2015). It is especially important to monitor the conditions (i.e. temperature, humidity, etc.) of perishable products such as meat and milk during the transportation process (Atzori, Iera, & Morabito, 2010). Increased visibility of the supply chain enables businesses to respond to complex and variable markets without losing time. While traditional businesses have a response time of around 120 days for a 123

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change, those using such technologies (e.g. Walmart or Metro) are capable of responding in a few days (Atzori et al., 2010). Remote monitoring of products will also allow the creation of an optimal replenishment strategy (i.e. the elimination of unnecessary truck travel) (Perera et al., 2015). Moreover, thanks to this technology, businesses are capable of early intervention. For instance, the use of the data generated while the product is in use can help monitor deviations from normal conditions and thus detect potential issues immediately. This technology makes it possible to solve problems with a minimal cost. Furthermore, as mentioned in other sections, business costs are also reduced as IoT provides the most efficient use of energy and resources. Another important component of Industry 4.0 for businesses is cloud computing. One of the main benefits of cloud computing is that it enables businesses to save capital expenditure as they can reduce maintenance, upgrades, and administration of Information and Communication Technology (ICT). Moreover, businesses using the cloud do not need to hire an IT professional, because the cloud provider performs management operations on behalf of the customer. Therefore, businesses can also save on labor costs through the cloud. In short, with the use of cloud computing, additional costs such as software licenses, hardware, ICT infrastructure support, and maintenance are eliminated. As a result, businesses can use their resources more efficiently. Another important advantage of cloud computing is accessibility. Businesses can easily access cloud computing from anywhere (Mohlameane & Ruxwana, 2014). This allows people to keep track of their business data even when they are on the move. As another Industry 4.0 component, robots have the potential to increase the competitiveness of a business if used effectively. They can enable a faster development and delivery of products (IFR, 2017). Businesses that use more robots achieve their targets of cost reduction as they employ a small number of people. With fewer people in the business, the costs of labor, lighting, healthcare, legalities, food, heating, transportation, and everything conceivable are lower. Furthermore, workplace accidents resulting in injury or death are prevented. Robots also minimize the rate of errors in production. Big data, which is another technology, attracts attention because it provides a systematic enabling businesses to be effective especially in the decision making process and achieving targets. With big data, enterprises can take advantage of both the market and the business processes by using the data they obtain in various ways. This is because it has the power to directly analyze and direct the decision-making and implementation processes of the business by analyzing the big data stacks correctly and producing meaningful results. Research shows that retailers can achieve a 15 to 20% increase in ROI by using big data analytics (BDA) technologies. For many businesses, using these technologies is recognized as an effective way to increase customer engagement and satisfaction. For example, businesses can design products that improve customer satisfaction through a historical analysis of orders and feedback. In short, customer behavior can be monitored continuously with this technology. Furthermore, through a deeper analysis of data from various sources, productivity and competitiveness of enterprises can be improved. For example, a manufacturer can process critical data and find critical parameters that have the greatest impact on quality and yield (Zhong et al., 2017). The results of a research conducted by McKinsey Global Institute confirm that the use of big data has several benefits in many sectors including healthcare, public services, retail, manufacturing, and services. Organizations that successfully analyze and use big data benefit from (i) rapid decision-making based on real data, (ii) an improvement and development of consumer experience, (iii) increased sales, (iv) increased efforts to introduce new products, (v) reduced risks, (vi) effective operational activities, and (g) the introduction of high quality products and services to the market (Wielki, 2013). 124

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Another technology that affects economic sustainability is AM. One of the major advantages that AM provides for businesses is the production of designs that are difficult or impossible to produce with traditional manufacturing techniques (Huang et al., 2016). AM reduces the need for storage, packaging, and transport and all associated costs (Kamble et al., 2018). Thus, supply chains will be shorter and simpler (Ford & Despeisse, 2016). AM’s business benefits can be summarized as follows: • • • • • • •

Reduction of logistics costs by moving the points of production close to consumer locations Eliminating stocks through production on demand Offering custom tailored products Rapid replacement of spare parts Ensuring more efficient use of raw materials and other materials Reduced time and costs of installation Shortening the time between design and production

Machines and materials required for AM are still expensive. However, the prices are expected to decrease as AM becomes a more widely used production technique. Another technology to focus on is simulation. Simulating production or service systems will allow for the identification of many problems and their reasons. Moreover, how possible changes can affect efficiency, effectiveness, and general operation can be determined by simulation. On the other hand, augmented reality (AR) can be used in many different fields such as production, marketing, repair-maintenance services, and employee training. For example, employees can get repair instructions on how to replace a particular part by looking at a real system that requires repairs. These instructions can be displayed directly in the field of view of workers through devices such as augmented reality glasses (Rüßmann et al., 2015). With this application, time can be saved and the process can be performed without error. Augmented reality applications are used for marketing purposes as well as in production processes. They are primarily involved in promotional activities and contribute to the positive development of the brand-consumer relationship. Another field of application of augmented reality is education. For example, Siemens developed a three-dimensional virtual facility operator training module with augmented reality glasses to train employees on how to deal with emergencies (Rüßmann et al., 2015). In conclusion, augmented reality applications can minimize the human error factor in businesses, help train employees, contribute to improve collaboration and provide assistance for processes.

CONCLUSION Industry 4.0 ushers in fundamental changes in industries, labor use, and production processes. As a result of the digitization of the entire value chain with Industry 4.0, smart factories emerged. The aim of this revolution is to provide flexible, fast, and personalized production, as well as resource efficiency. An expected result is a decrease in the time and cost of production and increase in its quantity and quality. Many researches were done on the revolution of Industry 4.0, mostly focusing on the changes it brings along in production and consumption, together with the effects of these changes on commercial applications. However, the changes experienced with Industry 4.0 also have their repercussions on corporate sustainability. Kamble et al. (2018) and Stock and Seliger (2016) pointed at the tremendous potential of Industry 4.0 to create sustainable industries. However, studies in this field are very limited. Some authors 125

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address the importance of Industry 4.0 components in reducing environmental pollution and efficient use of resources, while some examine these components in terms of public health and social benefits. Moreover, some scientists argue that Industry 4.0 can pose a threat to humanity. When smart factories emerged, intelligent machinery/robots were thought to fulfill all the tasks of the blue-collar working class, pushing them to extinction and making some professions obsolete. However, the key point that should be kept in mind is that human capital is the most important factor for the success of Industry 4.0. Even if it is suggested that robots will replace human labor altogether, in fact robots ensure that jobs are completed and complemented by human labor. Furthermore, robots can be used to perform dangerous and non-value-adding processes that cause inefficiency in enterprises. In short, robots can undertake tasks that do not require any skill or expertise, while the workforce focuses on jobs that require expertise to add value. Thus, the workforce will turn into qualified workers with high-level expertise, optimizing systems and solving problems that occur or are likely to occur in the process. In a study conducted in Germany, the birth country of Industry 4.0, the employment rate in the production and automotive sectors was predicted to increase by 6-10% in the next ten years. In conclusion, it should be emphasized that Industry 4.0 has significant effects on all three dimensions of sustainability.

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KEY TERMS AND DEFINITIONS 3D Printers: 3D printers are machines that provide three-dimensional output of products designed in virtual environments. Augmented Reality: Augmented reality is the physical appearance of the real-world elements created by enriching them in computer environment. Big Data: Big data can be referred to as data characterized by high levels of volume (amount of data), variety (number of types), and velocity (speed). Cloud Computing: Cloud computing is a new computing model providing data storage and access to software applications over the internet. Cyber-Physical Systems: Cyber-physical systems allows connection and communication between humans, machines, and products, such as the exchange of information, triggering of actions, and independent control. Internet of Things: IoT is term that combines different technologies and approaches, based on the connection between physical things and the internet. Simulation: Simulation is the visualization of the elements in the physical world in digital environments.

This research was previously published in the Handbook of Research on Creating Sustainable Value in the Global Economy; pages 67-84, copyright year 2020 by Business Science Reference (an imprint of IGI Global).

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Industry 4.0 in the Context of the Triple Bottom Line of Sustainability: A Systematic Literature Review Julian M. Müller https://orcid.org/0000-0002-7372-2405 Salzburg University of Applied Sciences, Austria

ABSTRACT Industry 4.0 and sustainability are trending topics in the industry and scientific research. However, there is currently no comparable study, which summarizes the impacts of Industry 4.0 on all three dimensions of the Triple Bottom Line at the same time. This chapter aims to present a comprehensive overview of Industry 4.0 in the context of the Triple Bottom Line of sustainability. For this reason, a systematic literature review is conducted to find out the current state of literature about this topic. The chapter presents a systematic literature review on 64 peer-reviewed journal articles, which have been published between 2014 and 2019. An in-depth analysis of the content as well as an analysis of the empirical methodologies are conducted. To structure the existing knowledge, a framework is developed, and the findings are categorized into ecological and social aspects. On this basis the content is evaluated to discuss key findings and relating interdependencies.

INTRODUCTION So far, Industry 4.0-related literature has mainly concerned technical aspects of the phenomenon (Kiel et al., 2017). Whereas further research disciplines, such as business management have begun to examine Industry 4.0 (Piccarozzi et al., 2018), ecological and social aspects of Industry 4.0 have been even less regarded. In particular, integrative investigations of economic, ecological, and social aspects remain rare (Birkel et al., 2019; Kiel et al., 2017). Several authors find that improving ecological and social aspects of industrial value creation whilst maintaining economic profitability is a challenging task. Several poDOI: 10.4018/978-1-7998-8548-1.ch008

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 Industry 4.0 in the Context of the Triple Bottom Line of Sustainability

tentials and challenges of Industry 4.0 seem contradictory in each of the three dimensions of the Triple Bottom Line. For instance, working conditions shall be improved, whilst job losses are expected through Industry 4.0. Therefore, an interdisciplinary and integrative investigation of Industry 4.0 is necessary, not only balancing, but combining ecological and social potentials with market success (Müller et al., 2018a; Müller & Däschle, 2018; Stock et al., 2018). Further, several authors find that sustainability aspects of Industry 4.0 differ across countries and need to be incorporated in managerial practice and policy, for instance been the German initiative “Industry 4.0” and the “Internet Plus” initiative within the concept “Made in China 2025” (Müller and Voigt, 2018). Still, sustainability in the context of Industry 4.0 has been addressed by some authors in recent time. According to Scopus, out of 137 articles that have “Industry 4.0” and “Sustainability” in abstract, title or keywords, 103 were published since 2018. This relates to articles in English language in journals or conference proceedings. In order to provide a comprehensive overview of the current state of research regarding Industry 4.0 in the context of the Triple Bottom Line of sustainability, the article performs a systematic literature review. Thereupon, the article discusses and evaluates recent research findings, presenting managerial and theoretical implications, and promising avenues for future research.

Background The term “Industry 4.0” is derived from the German “Industrie 4.0” initiative launched by the German government in 2011. It aims at ensuring future competitiveness of the German manufacturing industry (Kagermann et al., 2013; Lasi et al., 2014; Müller et al., 2017). In this context, Industry 4.0 indicates a predicted fourth industrial revolution. Industry 4.0 builds on several technological developments, including the Internet of Things and Cyber-Physical Systems, that shall enable real-time interconnection of products, production facilities, humans, and smart devices in a vertical and horizontal manner. Vertical interconnection means across several departments within an enterprise, horizontal interconnection expresses digital information sharing across several partners within a supply chain, including the customer. Further, the entire product lifecycle shall be encompassed, from production, to product usage, to recycling (Kagermann et al., 2013; Lasi et al., 2014; Liao et al., 2017; Müller et al., 2018b). After 2011, when the German initiative “Industrie 4.0” was launched in the “High tech strategy” of the German federal government, several comparable programs and initiatives have been launched worldwide. For instance, the European Union initiated a program called “Factories of the Future”. It intends to ensure a digital and sustainable production in order to maintain global competitiveness. In the United States of America, The “Industrial Internet Consortium” represents a comparable initiative, whereas China launched the program “Internet Plus” within the program “Made in China 2025” (Liao et al., 2017; Müller & Voigt, 2018). As the first industrial revolution (mechanization), the second industrial revolution (electrification), and the third industrial revolution, Industry 4.0 is expected to inflict changes in an economic, ecological, and social context (Birkel et al., 2019; Maynard, 2015; Kiel et al., 2017; Müller & Voigt, 2018). Economic, ecological and social aspects subsume the Triple Bottom Line of sustainability (Elkington, 1987; Elkington, 1998; Norman et al., 2004). Since the World Commission on Environment and Development’s “Brundtland report” in 1987 (World Commission on Environment and Development, 1987), an increasing expectation of society to achieve ecological and social welfare whilst maintaining economic success can be observed. This is also and especially true for industrial value creation, which 132

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is built on global supply chains, requiring, among many further aspects, large amounts of energy and fuels, whilst social conditions might not always be acceptable in many manufacturing locations. The United Nations’ Sustainable Development Goal 12 - “Ensure sustainable consumption and production patterns” expresses this increased attention on sustainable industrial value creation (Glavič & Lukman, 2007; McWilliams et al., 2016; Müller & Voigt, 2018). While many authors regard sustainability mainly from an ecological or social perspective, mostly without combining both, the Triple Bottom Line of sustainability must also consider economic aspects and their interplay with ecological and social considerations in order to achieve the maximum possible benefits (Littig & Griessler, 2005). In recent time, the term “circular economy” can be found increasingly, subsuming the sustainability initiatives towards industrial value creation. For Industry 4.0, most authors have focused on technological aspects, whilst economic, ecological and social aspects are comparably less understood. Especially their interplay has hardly been investigated so far, as most authors focus on one of the three dimensions within the Triple Bottom Line of sustainability (Birkel et al., 2019; Kiel et al., 2017; Müller & Voigt, 2018). In order to further develop the understanding of benefits and challenges in each of the three dimensions, the present article systematically reviews extant literature, as explained in the following.

Methodology This chapter is based on a systematic literature review of extant literature. The Scopus database was used to find relevant articles. The keywords “Industry 4.0 AND sustainability” and “Industry 4.0 AND circular economy” were used for the systematic literature search. Only journals and proceedings in English were selected. The specified keywords were used to search the title, the abstract and the keywords. This resulted in a list of 100 articles from journals and proceedings. After a thorough selection of results for relevance to this topic, taking into account any duplications, the researchers obtained a total number of 64 relevant and qualitative articles. In particular, articles that solely focus on technical solutions or economic considerations without ecological and social aspects were excluded from the further research process. As articles on economic aspects are found considerably more in literature than those on the ecological and social dimensions, only such articles that investigate economical aspects together with ecological and social aspects were analyzed in order to avoid an imbalance towards the economic dimension.

FINDINGS For the search term “Industry 4.0 AND sustainability”, 42 journal papers and 17 proceedings were included in the literature review, and the search term “Industry 4.0 AND circular economy” included 5 journal papers. From the final sample of 64 research articles found in journals or conference proceedings, 14 articles were published in 2019, 39 articles in 2018, 8 in 2017 and one was published in 2016, 2015 and 2014 each. The most used empirical methods are literature review (15 times), case studies (14 times). Also interviews (9 times) and surveys (6 times) were used in a quarter of the articles. Further used empirical methodologies are mathematical modelling or programming (4 times), application cases (2 times) and experiments (2 times). Further, 14 articles are not based on an empirical methodology. Relating to the journal or conference proceedings the articles were published in, the following picture can be obtained, as shown in Table 1 below. 133

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Table 1. Journals and conference proceedings in which articles were published (n = 64) Journal

2019

2018

2017

5

7

1

Procedia Manufacturing

7

1

Procedia CIRP

2

2

Process Safety and Environmental Protection

4

4

2

3

3

3

2

3

Sustainability

Computers and Industrial Engineering

1

IFAC-PapersOnLine Journal of Manufacturing Technology Management

1

Entrepreneurship and Sustainability Issues

1

Resources, Conservation and Recycling

1

2015

2014

8 1

1

6

2

1

2 1

Annals of Operations Research

Total 13

1

Advances in Manufacturing Annual Reviews in Control

2016

1

1

1

1

1

Applied Sciences

1

1

Benchmarking

1

1

Economies

1

1

Energies

1

1

IEEE Access

1

1

International Journal of Agile Systems and Management

1

1

International Journal of Energy Research

1

1

International Journal of Environmental Research and Public Health

1

1

International Journal of Information Management

1

1

International Journal of Innovation Management

1

International Journal of Precision Engineering and Manufacturing International Journal of Production Research

1

1

1

1

Journal of Cleaner Production

1 1

1

Journal of Management Analytics

1

1

Research Technology Management

1

1

Social Sciences

1

1

Technological Forecasting and Social Change

1

1

Total

14

39

8

1

1

1

64

Most frequently, publications from the journal Sustainability were found, namely 13 articles. Furthermore, 8 articles of Procedia Manufacturing and 6 articles of Procedia CIRP were added to the list. Four articles were published in Process Safety and Environmental Protection, whereas in Computers and Industrial Enginering, IFAC-PapersOnLine, and Journal of Manufacturing Technology Management, three articles were published each. Further, two articles were published in Entrepreneurship and Sustainability Issues and Ressources, Conservation and Recycling. Further journals and conference proceedings only list one article so far, as can be obtained from Table 1.

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When examining the methodology used within the research articles, Table 2 presents an overview. Table 2. Methods used within articles published (n = 64) Methodology

Number

Systematic literature review

15

Case study

14

Theoretical (not empirical-based)

14

Interviews (semi- structured interviews, expert interviews, …)

9

Surveys (questionnaire-based surveys, online surveys)

7

Mathematical programming or modelling

4

Application case

2

Experiments

2

Additionally to Tables 1 and 2, Table 3 in the appendix gives an overview to all 64 articles reviewed and their respective titles, authors, methods used, and further publication details. Regarding the aspects covered in the 64 articles reviewed, the following picture can be obtained: Articles covering ecological and social aspects: 28 articles Articles covering only ecological aspects: 19 articles Articles covering only social aspects: 17 articles However, the aspects covered in the 64 research articles sometimes cannot be clearly assigned to one dimension of the Triple Bottom Line, since there are certain dependencies that relate to two or more dimensions simultaneously. One of the most important effects results from the effects of energy efficiency on the potential of cost efficiency. Since electrical energy accounts for a large part of the costs in industrial value creation, Industry 4.0 offers several potentials. With smart energy distribution, improved process efficiency and improved design efficiency, the eco-effective production of the future can also make a significant contribution to the economic dimension (Kiel et al., 2017). For example, intelligent energy systems are capable of predicting a company’s energy consumption and adjusting it to energy production (Birkel et al., 2019). Through the intelligent planning and scheduling of energy peaks, times with cheaper energy costs can be used with the possibility of adapting production speed to the energy supply (Stock et al., 2018). On the other hand, Birkel et al. (2019) and Stock et al. (2018) argue that Industry 4.0 leads to higher energy consumption, for instance through required data centers. Furthermore, new technologies and machines related to Industry 4.0 must be produced first, which requires a great deal of additional resources (Birkel et al., 2019). Another issue that needs to be critically assessed is the creation of new jobs and job losses inflicted by Industry 4.0 (Birkel et al., 2017; Kiel et al., 2017). While there is no doubt in academic research that industry 4.0 will influence the labor market, it is still unclear which kind of jobs will develop in which direction.

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FUTURE RESEARCH DIRECTIONS In the following, six recommendations for future research are presented, that were identified in the literature analysis. First, small and medium-sized enterprises (SMEs) have been shown to play a decisive role for the unfolding of Industry 4.0 across the supply chain, relating to their importance within industrial value creation, but have lacking resources and lacking understanding of the concept Industry 4.0, among further (Müller, 2019a; Müller et al., 2018b; Müller & Voigt, 2016). However, for sustainability aspects within the Triple Bottom Line, only one article within the sample of 64 articles is directly related to SMEs (Müller & Voigt, 2018). Therefore, it is recommended to further regard SMEs, focusing on sustainability aspects within the Triple Bottom Line in future research. Second, only two articles within the total sample of 64 articles are related directly to supply chain management and logistics, referring to for sustainability aspects within the Triple Bottom Line (Kayikci, 2018; Strandhagen et al., 2017). However, the benefits of Industry 4.0 relating to horizontal interconnection of the supply chain for sustainability aspects necessarily require the integration and adaption of supply chain management (Birkel et al., 2019). Therefore, it is recommended to regard supply chains, logistics, and supply chain management more closely in the context of Industry 4.0 and the Triple Bottom Line of sustainability. Third, new technologies such as digital platforms and ecosystems (Müller, 2019b; Schmidt et al., 2019; Veile et al., 2019) are comparably less understood from a sustainability perspective. The same applies for technologies such as blockchain, 3D-printing, or artificial intelligence, which have been investigated from a mainly technological perspective so far, but neglecting aspects within the Triple Bottom Line (Birkel et al., 2019). Fourth, only few articles compare sustainability and Industry 4.0 in an international comparison, where perceptions, necessities and expectations towards sustainability might be different, showing a fourth possible research avenue for the future (Müller & Voigt, 2018). Fifth, the majority of articles rather describes the opinions of experts or technological showcases descriptively. A critical assessment of their implementation, especially across the supply chain, is still missing and should be achieved, combining expert opinions and, for instance, process data, that shows benefits towards sustainability. Sixth, the interdependencies among several dimensions of the Triple Bottom Line of sustainability must still be understood better, as the majority of articles does not cover all three dimensions of the Triple Bottom Line simultaneously. This is especially true for research across the supply chain and linking research areas like business model innovation to sustainability (Birkel et al., 2019; Müller et al., 2018b).

CONCLUSION The chapter is able to present a comprehensive overview of research articles dedicated to the Triple Bottom Line of sustainability in the context of Industry 4.0. A final sample of 64 research articles was examined, highlighting methods used, publication focuses, compound effects of several aspects within the Triple Bottom Line, aspects less regarded in current literature, and possible future research directions. Regarding the limitations of this chapter, it has to be noted that the majority of journals and conference proceedings are listed in Scopus. However, a few of them might not appear in Scopus and are 136

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therefore also not regarded in this chapter. The same is true for the term “Industry 4.0”: Articles related to terms like “Smart Manufacturing”, “Smart Factory” or comparable terms might not all be covered in this literature review. Several articles, but not presenting an extensive list, are presented in the section dedicated to Further Reading of this chapter.

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Gu, F., Guo, J., Hall, P., & Gu, X. (2018). An integrated architecture for implementing extended producer responsibility in the context of Industry 4.0. International Journal of Production Research, 1–20. Kagermann, H., Wahlster, W., & Helbig, J. (2013). Recommendations for implementing the strategic initiative Industrie 4.0. Final report of the Industrie 4.0 Working Group. Frankfurt am Main, Germany: acatech. Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2018). Sustainable Industry 4.0 framework: A systematic literature review identifying the current trends and future perspectives. Process Safety and Environmental Protection, 117, 408–425. doi:10.1016/j.psep.2018.05.009 Kayikci, Y. (2018). Sustainability impact of digitization in logistics. In Procedia Manufacturing (Vol. 21, pp. 782–789). Amsterdam, Netherlands: Elsevier. Kenett, R. S., Zonnenshain, A., & Fortuna, G. (2018). A road map for applied data sciences supporting sustainability in advanced manufacturing: the information quality dimensions. In Procedia Manufacturing (Vol. 21, pp. 141–148). Amsterdam, Netherlands: Elsevier. Kiel, D., Müller, J. M., Arnold, C., & Voigt, K. I. (2017). Sustainable industrial value creation: Benefits and challenges of industry 4.0. International Journal of Innovation Management, 21(08), 1740015. doi:10.1142/S1363919617400151 Kumar, R., Singh, S. P., & Lamba, K. (2018). Sustainable robust layout using Big Data approach: A key towards industry 4.0. Journal of Cleaner Production, 204, 643–659. doi:10.1016/j.jclepro.2018.08.327 Kusiak, A. (2019). Fundamentals of smart manufacturing: A multi-thread perspective. Annual Reviews in Control, 47, 214–220. doi:10.1016/j.arcontrol.2019.02.001 Lasi, H., Fettke, P., Kemper, H.-G., Feld, T., & Hoffmann, M. (2014). Industry 4.0. Business & Information Systems Engineering, 6(8), 239–242. doi:10.100712599-014-0334-4 Latorre-Biel, J. I., Faulín, J., Juan, A. A., & Jiménez-Macías, E. (2018). Petri net model of a smart factory in the frame of Industry 4.0. IFAC-PapersOnLine, 51(2), 266–271. doi:10.1016/j.ifacol.2018.03.046 Liao, Y., Deschamps, F., Loures, E., & Ramos, L. F. P. (2017). Past, present and future of Industry 4.0 - a systematic literature review and research agenda proposal. International Journal of Production Research, 55(12), 3609–3629. doi:10.1080/00207543.2017.1308576 Limba, T., Stankevičius, A., & Andrulevičius, A. (2019). Industry 4.0 and national security: The phenomenon of disruptive technology. Entrepreneurship and Sustainability Issues, 6(3), 1528–1535. doi:10.9770/jesi.2019.6.3(33) Lin, K., Shyu, J., & Ding, K. (2017). A cross-strait comparison of innovation policy under industry 4.0 and sustainability development transition. Sustainability, 9(5), 786. doi:10.3390u9050786 Lin, K. Y. (2018). User experience-based product design for smart production to empower industry 4.0 in the glass recycling circular economy. Computers & Industrial Engineering, 125, 729–738. doi:10.1016/j. cie.2018.06.023

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Littig, B., & Griessler, E. (2005). Social sustainability. A catchword between political pragmatism and social theory. International Journal of Sustainable Development, 8(1-2), 65–78. doi:10.1504/IJSD.2005.007375 Lugert, A., Batz, A., & Winkler, H. (2018). Empirical assessment of the future adequacy of value stream mapping in manufacturing industries. Journal of Manufacturing Technology Management, 29(5), 886–906. doi:10.1108/JMTM-11-2017-0236 Luthra, S., & Mangla, S. K. (2018). Evaluating challenges to Industry 4.0 initiatives for supply chain sustainability in emerging economies. Process Safety and Environmental Protection, 117, 168–179. doi:10.1016/j.psep.2018.04.018 Manavalan, E., & Jayakrishna, K. (2019). A review of Internet of Things (IoT) embedded sustainable supply chain for industry 4.0 requirements. Computers & Industrial Engineering, 127, 925–953. doi:10.1016/j.cie.2018.11.030 Maresova, P., Soukal, I., Svobodova, L., Hedvicakova, M., Javanmardi, E., Selamat, A., & Krejcar, O. (2018). Consequences of Industry 4.0 in business and economics. Economies, 6(3), 46. doi:10.3390/ economies6030046 Mattsson, S., Fast-Berglund, Å., Li, D., & Thorvald, P. (in press). Forming a cognitive automation strategy for Operator 4.0 in complex assembly. Computers & Industrial Engineering. Maynard, A. D. (2015). Navigating the fourth industrial revolution. Nature Nanotechnology, 10(1), 1005–1006. doi:10.1038/nnano.2015.286 McWilliams, A., Parhankangas, A., Coupet, J., Welch, E., & Barnum, D. T. (2016). Strategic Decision Making for the Triple Bottom Line. Business Strategy and the Environment, 25(3), 193–204. doi:10.1002/ bse.1867 Müller, J., Dotzauer, V., & Voigt, K. I. (2017). Industry 4.0 and its impact on reshoring decisions of German manufacturing enterprises. In Supply Management Research (pp. 165-179). Springer Gabler. Müller, J., & Voigt, K. I. (2016). Industrie 4.0 für kleine und mittlere Unternehmen. Productivity Management, 3, 28–30. Müller, J. M. (2019a). Business model innovation in small-and medium-sized enterprises: Strategies for industry 4.0 providers and users. Journal of Manufacturing Technology Management. doi:10.1108/ JMTM-01-2018-0008 Müller, J. M. (2019b). Antecedents to Digital Platform Usage in Industry 4.0 by Established Manufacturers. Sustainability, 11(4), 1121. doi:10.3390u11041121 Müller, J. M., Buliga, O., & Voigt, K.-I. (2018b). Fortune favors the prepared. How SMEs approach business model innovation in Industry 4.0. Technological Forecasting and Social Change, 132, 2–17. doi:10.1016/j.techfore.2017.12.019 Müller, J. M., & Däschle, S. (2018). Business Model Innovation of Industry 4.0 Solution Providers Towards Customer Process Innovation. Processes, 6(12), 260. doi:10.3390/pr6120260

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Müller, J. M., Kiel, D., & Voigt, K. I. (2018a). What drives the implementation of industry 4.0? The role of opportunities and challenges in the context of sustainability. Sustainability, 10(1), 247. doi:10.3390u10010247 Müller, J. M., & Voigt, K. I. (2018). Sustainable industrial value creation in SMEs: A comparison between industry 4.0 and Made in China 2025. International Journal of Precision Engineering and Manufacturing-Green Technology, 5(5), 659–670. doi:10.100740684-018-0056-z Murmura, F., & Bravi, L. (2018). Additive manufacturing in the wood-furniture sector: Sustainability of the technology, benefits and limitations of adoption. Journal of Manufacturing Technology Management, 29(2), 350–371. Nagy, J., Oláh, J., Erdei, E., Máté, D., & Popp, J. (2018). The Role and Impact of Industry 4.0 and the Internet of Things on the Business Strategy of the Value Chain—The Case of Hungary. Sustainability, 10(10), 3491. doi:10.3390u10103491 Nascimento, D. L. M., Alencastro, V., Quelhas, O. L. G., Caiado, R. G. G., Garza-Reyes, J. A., Lona, L. R., & Tortorella, G. (2018). Exploring Industry 4.0 technologies to enable circular economy practices in a manufacturing context: A business model proposal. Journal of Manufacturing Technology Management, 30(3), 607–627. doi:10.1108/JMTM-03-2018-0071 Norman, W., MacDonald, C., & Arnold, D. G. (2004). Getting to the Bottom of “Triple Bottom Line”. Business Ethics Quarterly, 14(2), 243–262. doi:10.5840/beq200414211 Okorie, O., Salonitis, K., Charnley, F., Moreno, M., Turner, C., & Tiwari, A. (2018). Digitisation and the circular economy: A review of current research and future trends. Energies, 11(11), 3009. doi:10.3390/ en11113009 Paravizo, E., Chaim, O. C., Braatz, D., Muschard, B., & Rozenfeld, H. (2018). Exploring gamification to support manufacturing education on industry 4.0 as an enabler for innovation and sustainability. In Procedia Manufacturing (Vol. 21, pp. 438–445). Amsterdam, Netherlands: Elsevier. Piccarozzi, M., Aquilani, B., & Gatti, C. (2018). Industry 4.0 in management studies: A systematic literature review. Sustainability, 10(10), 3821. doi:10.3390u10103821 Prause, G., & Atari, S. (2017). On sustainable production networks for Industry 4.0. Entrepreneurship and Sustainability Issues, 4(4), 421–431. doi:10.9770/jesi.2017.4.4(2) Rajput, S., & Singh, S. P. (2019). Connecting circular economy and industry 4.0. International Journal of Information Management, 49, 98–113. doi:10.1016/j.ijinfomgt.2019.03.002 Rauch, E., Dallasega, P., & Matt, D. T. (2017). Distributed manufacturing network models of smart and agile mini-factories. International Journal of Agile Systems and Management, 10(3-4), 185–205. doi:10.1504/IJASM.2017.088534 Salah, B., Abidi, M. H., Mian, S. H., Krid, M., Alkhalefah, H., & Abdo, A. (2019). Virtual Reality-Based Engineering Education to Enhance Manufacturing Sustainability in Industry 4.0. Sustainability, 11(5), 1477. doi:10.3390u11051477

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Savastano, M., Amendola, C., Bellini, F., & D’Ascenzo, F. (2019). Contextual Impacts on Industrial Processes Brought by the Digital Transformation of Manufacturing: A Systematic Review. Sustainability, 11(3), 891. doi:10.3390u11030891 Scharl, S., & Praktiknjo, A. (2019). The Role of a Digital Industry 4.0 in a Renewable Energy System. International Journal of Energy Research, 43(8), 3891–3904. doi:10.1002/er.4462 Schmidt, M.-C., Veile, J. W., Müller, J. M., & Voigt, K.-I. (2019). Kick-start for connectivity – How to implement digital platforms successfully. Paper presented at the International Society for Professional Innovation Management (ISPIM) Innovation Conference, Florence, Italy. Sénéchal, O. (2018). Performance indicators nomenclatures for decision making in sustainable conditions based maintenance. IFAC-PapersOnLine, 51(11), 1137–1142. doi:10.1016/j.ifacol.2018.08.438 Sjödin, D. R., Parida, V., Leksell, M., & Petrovic, A. (2018). Smart Factory Implementation and Process Innovation: A Preliminary Maturity Model for Leveraging Digitalization in Manufacturing Moving to smart factories presents specific challenges that can be addressed through a structured approach focused on people, processes, and technologies. Research Technology Management, 61(5), 22–31. doi:10.1080 /08956308.2018.1471277 Stachová, K., Papula, J., Stacho, Z., & Kohnová, L. (2019). External partnerships in employee education and development as the key to facing industry 4.0 challengesn. Sustainability, 11(2), 345. doi:10.3390u11020345 Stark, R., Grosser, H., Beckmann-Dobrev, B., & Kind, S. (2014). Advanced technologies in life cycle engineering. Procedia CIRP, 22, 3–14. doi:10.1016/j.procir.2014.07.118 Stock, T., Obenaus, M., Kunz, S., & Kohl, H. (2018). Industry 4.0 as enabler for a sustainable development: A qualitative assessment of its ecological and social potential. Process Safety and Environmental Protection, 118, 254–267. doi:10.1016/j.psep.2018.06.026 Stock, T., & Seliger, G. (2016). Opportunities of sustainable manufacturing in industry 4.0. In Procedia CIRP (Vol. 40, pp. 536–541). Amsterdam, Netherlands: Elsevier. Strandhagen, J. O., Vallandingham, L. R., Fragapane, G., Strandhagen, J. W., Stangeland, A. B. H., & Sharma, N. (2017). Logistics 4.0 and emerging sustainable business models. Advances in Manufacturing, 5(4), 359–369. doi:10.100740436-017-0198-1 Tsai, W. H., & Lu, Y. H. (2018). A framework of production planning and control with carbon tax under industry 4.0. Sustainability, 10(9), 3221. doi:10.3390u10093221 Tseng, M. L., Chiu, A. S., Chien, C. F., & Tan, R. R. (2019). Pathways and barriers to circularity in food systems. Resources, Conservation and Recycling, 143, 236–237. doi:10.1016/j.resconrec.2019.01.015 Tseng, M. L., Tan, R. R., Chiu, A. S., Chien, C. F., & Kuo, T. C. (2018). Circular economy meets industry 4.0: Can big data drive industrial symbiosis? Resources, Conservation and Recycling, 131, 146–147. doi:10.1016/j.resconrec.2017.12.028 Varela, L., Araújo, A., Ávila, P., Castro, H., & Putnik, G. (2019). Evaluation of the Relation between Lean Manufacturing, Industry 4.0, and Sustainability. Sustainability, 11(5), 1439. doi:10.3390u11051439

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Veile, J. W., Kiel, D., Müller, J. M., & Voigt, K.-I. (2019). Ecosystem 4.0: A Supply Chain Perspective on Business Model Innovation. Paper presented at the International Society for Professional Innovation Management (ISPIM) Innovation Conference, Florence, Italy. Waibel, M. W., Oosthuizen, G. A., & du Toit, D. W. (2018). Investigating current smart production innovations in the machine building industry on sustainability aspects. In Procedia Manufacturing (Vol. 21, pp. 774–781). Amsterdam, Netherlands: Elsevier. Waibel, M. W., Steenkamp, L. P., Moloko, N., & Oosthuizen, G. A. (2017). Investigating the effects of smart production systems on sustainability elements. In Procedia Manufacturing (Vol. 8, pp. 731–737). Amsterdam, Netherlands: Elsevier. World Commission on Environment and Development. (1987). Our Common Future. Oxford University Press. Yang, S., MR, A., Kaminski, J., & Pepin, H. (2018). Opportunities for industry 4.0 to support remanufacturing. Applied Sciences, 8(7), 1177. doi:10.3390/app8071177 Yazdi, G. P., Azizi, A., & Hashemipour, M. (2018). An empirical investigation of the relationship between overall equipment efficiency (OEE) and manufacturing sustainability in Industry 4.0 with time study approach. Sustainability, 10(9), 3031. doi:10.3390u10093031

ADDITIONAL READING Beier, G., Niehoff, S., Ziems, T., & Xue, B. (2017). Sustainability aspects of a digitalized industry– A comparative study from China and Germany. International Journal of Precision Engineering and Manufacturing-Green Technology, 4(2), 227–234. doi:10.100740684-017-0028-8 Bonekamp, L., & Sure, M. (2015). Consequences of Industry 4.0 on Human Labour and Work Organisation. Journal of Business and Media Psychology, 6(1), 33–40. Herrmann, C., Schmidt, C., Kurle, D., Blume, S., & Thiede, S. (2016). Sustainability in manufacturing and factories of the future. International Journal of Precision Engineering in Manufacturing-Green Technology, 1(4), 283–292. doi:10.100740684-014-0034-z Müller, J. M., Maier, L., Veile, J., & Voigt, K. I. (2017). Cooperation strategies among SMEs for implementing industry 4.0. In Proceedings of the Hamburg International Conference of Logistics (HICL) (pp. 301-318). Berlin, Germany: epubli. Müller, J. M., Veile, J., & Voigt, K. I. (2018). Supplier Integration in Industry 4.0–Requirements and Strategies. In Proceedings of the Hamburg International Conference of Logistics (HICL) (pp. 23-36). Berlin, Germany: epubli. Müller, J. M., & Voigt, K. I. (2018). The Impact of Industry 4.0 on Supply Chains in Engineer-toOrder Industries-An Exploratory Case Study. IFAC-PapersOnLine, 51(11), 122–127. doi:10.1016/j. ifacol.2018.08.245

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Peukert, B., Benecke, S., Clavell, J., Neugebauer, S., Nissen, N. F., & Uhlmann, E. (2015). Addressing Sustainability and Flexibility in Manufacturing Via Smart Modular Machine Tool Frames to Support Sustainable Value Creation. Procedia CIRP (Vol. 29, pp. 514–519). Amsterdam, Netherlands: Elsevier. Shrouf, F., & Miragliotta, G. (2015). Energy Management Based on Internet of Things: Practices and Framework for Adoption in Production Management. Journal of Cleaner Production, 100, 235–246. doi:10.1016/j.jclepro.2015.03.055 Veile, J. V., Kiel, D., Müller, J. M., & Voigt, K.-I. (2019). (in press). Lessons learned from Industry 4.0 implementation in the German manufacturing industry. [available online.]. Journal of Manufacturing Technology Management, ahead-of-print(ahead-of-print). doi:10.1108/JMTM-08-2018-0270 Veile, J. W., Schmidt, M.-C., Müller, J. M., & Voigt, K.-I. (2019). Buyer-supplier relationships in Industry 4.0 – A comparison across industries. Paper presented at the European Operations Management Association (EurOMA) conference, Helsinki, Finland.

KEY TERMS AND DEFINITIONS Circular Economy: Reduction of resource consumption, wastage, and energy consumption through more efficient end-to-end processes along the entire product lifecycle. Cyber-Physical Systems: Allow a fusion of the real and virtual worlds through sensor, data transmission, and data evaluation technologies. Industry 4.0: The term relates to a predicted fourth industrial revolution through horizontal and vertical interconnection in real-time based on digital technologies. Internet of Things: Extending the internet through internet-ready products and production facilities (“things”) with their own IP-addresses. Small and Medium-Sized Enterprises: Within the European Union, enterprises with up to 250 employees and 50 million Euros of annual turnover. Sustainability: Ensuring economic viability while achieving ecological and social welfare. Triple Bottom Line: The combination of economic, ecological, and social aspects and their interdependency.

This research was previously published in Customer Satisfaction and Sustainability Initiatives in the Fourth Industrial Revolution; pages 1-20, copyright year 2020 by Business Science Reference (an imprint of IGI Global).

144

2019

2019

2019

2019

2019

2019

2019

Development of a risk framework for Industry 4.0 in the context of sustainability for established manufacturers

An integrated architecture for implementing extended producer responsibility in the context of Industry 4.0

Fundamentals of smart manufacturing: A multi-thread perspective

Industry 4.0 and national security: The phenomenon of disruptive technology

A review of Internet of Things (IoT) embedded sustainable supply chain for industry 4.0 requirements

Exploring Industry 4.0 technologies to enable circular economy practices in a manufacturing context: A business model proposal

Connecting circular economy and industry 4.0

Virtual reality-based engineering education to enhance manufacturing sustainability in industry 4.0

Birkel, H.S., Veile, J.W., Müller, J.M., Hartmann, E., Voigt, K.-I.

Gu, F., Guo, J., Hall, P., Gu, X.

Kusiak, A.

Limba, T., Stankevičius, A., Andrulevičius, A.

Manavalan, E., Jayakrishna, K

Nascimento, D.L.M., Alencastro, V., Quelhas, O.L.G., Caiado, R.G.G., GarzaReyes, J.A., Lona, L.R., Tortorella, G.

Rajput, S., Singh, S.P.

Salah, B., Abidi, M.H., Mian, S.H., Krid, M., Alkhalefah, H., Abdo, A. 2019

2019

Optimization of municipal waste collection routing: Impact of industry 4.0 technologies on environmental awareness and sustainability

Bányai, T., Tamás, P., Illés, B., Stankevičiūtė, Ž., Bányai, Á.

Year

Title

Authors

Sustainability

International Journal of Information Management

Journal of Manufacturing Technology Management

Computers and Industrial Engineering

Entrepreneur-ship and Sustainability Issues

Annual Reviews in Control

International Journal of Production Research

Sustainability

International Journal of Environmental Research and Public Health

Journal

Table 3. List of articles reviewed in this chapter (n = 64)

11 (5), art. No. 1477

49, pp. 98-113

30 (3), pp. 607627

127, pp. 925-953

6 (3), pp. 15281535

57 (5), pp. 14581477

11 (2), art. No. 384

16 (4), art. No. 634

Volume/Issue/ Pages

VR-based experiments with students

Online survey

Social

Social/ Ecological

continues on follogin page

50 participants

161 question-naires

Ecological

Social/ Ecological

Literature review (analysing number of publications on IoT, SSCM and Industry 4.0) for identifying the research gap, building on it, a conceptual framework model has been formulated Semi-structured interviews with managers, researchers and professors and literature review

Social

Theoretical insights obtained by document analysis, classification, critical analysis, abstraction methods

Ecological

Ecological/ Social

Ecological/ Social

Aspect

Ecological

Publications from the past ten years

1 refrigerator plant

14 interviews

227 journal articles

Sample

Theoretical (not empiricalbased)

Case study

Semi-structured expert interviews

Systemic literature review

Methodology

 Industry 4.0 in the Context of the Triple Bottom Line of Sustainability

APPENDIX

145

146 2019

2019

2018

2018

2018

2018

The Role of a Digital Industry 4.0 in a Renewable Energy System

External partnerships in employee education and development as the key to facing industry 4.0 challenges

Pathways and barriers to circularity in food systems

Evaluation of the relation between lean manufacturing, industry 4.0, and sustainability

Industry 4.0 and supply chain sustainability: framework and future research directions

Industry 4.0 and sustainability implications: A scenario-based analysis of the impacts and challenges

Exploring organizational sustainability of Industry 4.0 under the triple bottom line: The case of a manufacturing company

Exploring how usage-focused business models enable circular economy through digital technologies

Manufacturing in the fourth industrial revolution: A positive prospect in Sustainable Manufacturing

Scharl, S., Praktiknjo, A.

Stachová, K., Papula, J., Stacho, Z., Kohnová, L.

Tseng, M.-L., Chiu, A.S.F., Chien, C.-F., Tan, R.R.

Varela, L., Araújo, A., Ávila, P., Castro, H., Putnik, G.

Bag, S., Telukdarie, A., Pretorius, J.H.C., Gupta, S

Bonilla, S.H., Silva, H.R.O., da Silva, M.T., Gonçalves, R.F., Sacomano, J.B.

Braccini, A.M., Margherita, E.G.

Bressanelli, G., Adrodegari, F., Perona, M., Saccani, N.

Carvalho, N., Chaim, O., Cazarini, E., Gerolamo, M.

2018

2019

2019

2019

Year

Contextual impacts on industrial processes brought by the digital transformation of manufacturing: A systematic review

Title

Savastano, M., Amendola, C., Bellini, B., D’Ascenzo, F.

Authors

Table 3. Continued

Procedia Manufacturing

Sustainability

Sustainability

Sustainability

Benchmarking

Sustainability

Resources, Conservation and Recycling,

Sustainability

International Journal of Energy Research

Sustainability

Journal

21, pp. 671-678

10 (3), art. No. 639

11 (1), art. No. 36

10 (10), art. No 3740

Article in Press

11 (5), art. No. 1439

pp. 236-237

11 (2), art. No. 345

11 (3), art. No. 891

Volume/Issue/ Pages

Theoretical (not empiricalbased)

Case Study

Case Study

Literature Review and theoretical analysis of scenarios

Systemic literature review

Questionnaire based survey

Theoretical (not empiricalbased)

Quantitative survey

Semi- structured interviews with industry managers and energy researchers

Literature review

Methodology

Ecological

Ecological

Social/ Ecological

Ecological

Social

Ecological/ Social

Ecological/ Social

Social

Ecological/ Social

Social

Aspect

continues on follogin page

1 company (ALPHA)

1 company

4 scenarios

53 papers

252 question-naires

1482 organi-sations

156 primary publications

Sample

 Industry 4.0 in the Context of the Triple Bottom Line of Sustainability

2018

2018

2018

2018

2018 2018

2018

2018

2018

Insertion of sustainability performance indicators in an industry 4.0 virtual learning environment

Exergetic Model as a Guideline for Implementing the Smart-factory Paradigm in Small Medium Enterprises: The Brovedani Case

When titans meet – Can industry 4.0 revolutionise the environmentally-sustainable manufacturing wave? The role of critical success factors

Pharma Industry 4.0: Literature review and research opportunities in sustainable pharmaceutical supply chains

Impact of Sustainability on the supply chain 4.0 performance

Maintenance for Sustainability in the Industry 4.0 context: a Scoping Literature Review

The paradigms of Industry 4.0 and circular economy as enabling drivers for the competitiveness of businesses and territories: The case of an Italian ceramic tiles manufacturing company

IoT Heterogeneous Mesh Network Deployment for Human-in-the-Loop Challenges Towards a Social and Sustainable Industry 4.0

Improving a production site from a social point of view: An IoT infrastructure to monitor workers condition

Chaim, O., Muschard, B., Cazarini, E., Rozenfeld, H.

Dassisti, M., Siragusa, N., Semeraro, C.

de Sousa Jabbour, A.B.L., Jabbour, C.J.C., Foropon, C., Filho, M.G.

Ding, B.

Dossou, P.-E.

Franciosi, C., Iung, B., Miranda, S., Riemma, S.

Garcia-Muiña, F.E., González-Sánchez, R., Ferrari, A.M., Settembre-Blundo, D.

Garrido-Hidalgo, C., Hortelano, D., RodaSanchez, L., Olivares, T., Ruiz, M.C., Lopez, V.

Gregori, F., Papetti, A., Pandolfi, M., Peruzzini, M., Germani, M.

Year

Title

Authors

Table 3. Continued

Procedia CIRP

IEEE Access

Social Sciences

IFAC- PapersOnLine

Procedia Manufacturing

Process Safety and Environmental Protection

Technological Forecasting and Social Change

Procedia CIRP

Procedia Manufacturing

Journal

72, pp. 886-891

6, pp. 2841728437

7 (12), art. No. 255

51 (11), pp. 903-908

17, pp. 452-459

119, pp. 115-130

132, pp. 18-25

67, pp. 534-539

21, pp. 446-453

Volume/Issue/ Pages

Case study

Experiments

Case study

Literature review

Case study

Literature review

Theoretical (not empiricalbased)

Systemic literature review

Case study

Methodology

Social

Social

Social

Ecological

Social

Social

Ecological

Ecological

Social/ Ecological

Aspect

continues on follogin page

1 company

1 ceramic tiles manu-facturing company in Italy

68 papers

1 company

33 articles

339 papers

1 company

Sample

 Industry 4.0 in the Context of the Triple Bottom Line of Sustainability

147

148 2018

2018

2018

2018

2018

2018

2018

2018

2018

Sustainability impact of digitization in logistics

A road map for applied data sciences supporting sustainability in advanced manufacturing: The information quality dimensions

Sustainable robust layout using Big Data approach: A key towards industry 4.0

Petri Net Model of a Smart Factory in the Frame of Industry 4.0

User experience-based product design for smart production to empower industry 4.0 in the glass recycling circular economy

Industry 4.0 and the circular economy: a proposed research agenda and original roadmap for sustainable operations

Empirical assessment of the future adequacy of value stream mapping in manufacturing industries

Evaluating challenges to Industry 4.0 initiatives for supply chain sustainability in emerging economies

Kayikci, Y.

Kenett, R.S., Zonnenshain, A., Fortuna, G.

Kumar, R., Singh, S.P., Lamba, K.

Latorre-Biel, J.-I., Faulín, J., Juan, A.A., Jiménez-Macías, E.

Lin, K.-Y.

Lopes de Sousa Jabbour, A.B., Jabbour, C.J.C., Godinho Filho, M., Roubaud, D.

Lugert, A., Batz, A., Winkler, H.

Luthra, S., Mangla, S.K.

Year

Sustainable Industry 4.0 framework: A systematic literature review identifying the current trends and future perspectives

Title

Kamble, S.S., Gunasekaran, A., Gawankar, S.A.

Authors

Table 3. Continued

Process Safety and Environmental Protection

Journal of Manufacturing Technology Management

Annals of Operations Research

Computers and Industrial Engineering

IFAC-PapersOnLine

Journal of Cleaner Production

Procedia Manufacturing

Procedia Manufacturing

Process Safety and Environmental Protection

Journal

117, pp. 168-179

29 (5), pp. 886906

270 (1-2), pp. 273-286

125, pp. 729-738

51 (2), pp. 266271

204, pp. 643-659

21, pp. 141-148

21, pp. 782-789

117, pp. 408-425

Volume/Issue/ Pages

Questionnaire based survey

Empirical survey

Theoretical (not empiricalbased)

Literature review + empirical study with a glass recycling company in Taiwan

Application case

(Mathe-matical) model development

Theoretical (not empiricalbased)

Case study, Qualitative expert interviews and secondary data

Systemic literature review

Methodology

Social/ Ecological

Ecological

Ecological

Social

Social

Social/ Ecological

Social/ Ecological

Ecological/ Social

Social/ Ecological

Aspect

continues on follogin page

96 responses

170 participants

11 papers (circular economy), 35 studies (product design), 9 resources to compare the level of literature)

85 papers

Sample

 Industry 4.0 in the Context of the Triple Bottom Line of Sustainability

2018

2018

2018

2018

2018

2018

2018

2018

Consequences of industry 4.0 in business and economics

Forming a cognitive automation strategy for Operator 4.0 in complex assembly

What drives the implementation of Indusrry 4.0? The role of opportunities and challenges in the context of sustainability

Sustainable Industrial Value Creation in SMEs: A Comparison between Industry 4.0 and Made in China 2025

Additive manufacturing in the wood-furniture sector: Sustainability of the technology, benefits and limitations of adoption

The role and impact of industry 4.0 and the internet of things on the business strategy of the value chain-the case of hungary

Digitisation and the circular economy: A review of current research and future trends

Exploring gamification to support manufacturing education on industry 4.0 as an enabler for innovation and sustainability

Maresova, P., Soukal, I., Svobodova, L., Hedvicakova, M., Javanmardi, E., Selamat, A., Krejcar, O.

Mattsson, S., FastBerglund, Å., Li, D., Thorvald, P.

Müller, J.M., Kiel, D., Voigt, K.-I.

Müller, J.M., Voigt, K.-I.

Murmura, F., Bravi, L.

Nagy, J., Oláh, J., Erdei, E., Máté, D., Popp, J.

Okorie, O., Salonitis, K., Charnley, F., Moreno, M., Turner, C., Tiwari, A.

Paravizo, E., Chaim, O.C., Braatz, D., Muschard, B., Rozenfeld, H.

Year

Title

Authors

Table 3. Continued

Procedia Manufacturing

Energies

Sustainability

21, pp. 438-445

11 (11), art. No. 3009

10 (10), art. No. 3491

29 (2), pp. 350371

5 (5), pp. 659670

International Journal of Precision Engineering and Manufacturing- Green Technology Journal of Manufacturing Technology Management

10 (1) art. No. 247

Article in Press

6 (3), art. No. 46

Volume/Issue/ Pages

Sustainability

Computers and Industrial Engineering

Economies

Journal

Systematic literature review

Systematic literature review

Questionnaire based survey/ expert interviews

Questionnaire based survey

Questionnaire based survey (for SMEs in China and Germany)

Quantitative study (partial least square structural equation modelling)

Theoretical (not empiricalbased)

Literature Review

Methodology

Ecological/ Social

Social

Ecological

Ecological

Ecological/ Social

Ecological/ Social

Social

Social

Aspect

continues on follogin page

51 articles

174 articles

43 question-naires/ 4 expert interviews

234 Italian companies

329 SMEs (222 Germany, 107 China)

746 manu-facturing companies

67 papers

Sample

 Industry 4.0 in the Context of the Triple Bottom Line of Sustainability

149

150 2018

2018

2018

2018

2018

2018

2018

2018

Smart Factory Implementation and Process Innovation: A Preliminary Maturity Model for Leveraging Digitalization in Manufacturing Moving to smart factories presents specific challenges that can be addressed through a structured approach focused on people, processes, and technologies.

Industry 4.0 as enabler for a sustainable development: A qualitative assessment of its ecological and social potential

A framework of production planning and control with carbon tax under industry 4.0

Circular economy meets industry 4.0: Can big data drive industrial symbiosis?

Investigating current smart production innovations in the machine building industry on sustainability aspects

Opportunities for industry 4.0 to support remanufacturing

An empirical investigation of the relationship between overall equipment efficiency (OEE) and manufacturing sustainability in industry 4.0 with time study approach

Sjödin, D.R., Parida, V., Leksell, M., Petrovic, A.

Stock, T., Obenaus, M., Kunz, S., Kohl, H.

Tsai, W.-H., Lu, Y.-H.

Tseng, M.-L., Tan, R.R., Chiu, A.S.F., Chien, C.-F., Kuo, T.C.

Waibel, M.W., Oosthuizen, G.A., Du Toit, D.W.

Yang, S., Raghavendra, M.R.A., Kaminski, J., Pepin, H.

Yazdi, P.G., Azizi, A., Hashemipour, M.

Year

Performance indicators nomenclatures for decision making in sustainable conditions based maintenance

Title

Sénéchal, O.

Authors

Table 3. Continued

Sustainability

Applied Sciences

Procedia Manufacturing

Resources, Conservation and Recycling

Sustainability

Process Safety and Environmental Protection

Research Technology Management

IFAC-PapersOnLine

Journal

10 (9), art. No. 3031

8 (7), art. No. 1177

21, pp. 774-781

131, pp 146-147

10 (9), art. No. 3221

118, pp 254-267

61 (5), pp.22-31

51 (11), pp. 1137-1142

Volume/Issue/ Pages

Time study-based methodology

Case Study

Interviews

Literature review (analysing the number of publications on different keywords)

Mathematical programming model

Literature review and case study with expert interviews

Case Study

Application case

Methodology

2 cases

Ecological

Ecological

Ecological/ Social

Ecological/ Social

Ecological/ Social

Ecological/ Social

Social/ Ecological

Ecological/ Social

Aspect

continues on follogin page

12 companies

5 companies

Sample

 Industry 4.0 in the Context of the Triple Bottom Line of Sustainability

2017

2017

2017

2017

2017

2017

2016

2015

Sustainable industrial value creation: Benefits and challenges of industry 4.0

A cross-strait comparison of innovation policy under industry 4.0 and sustainability development transition

An Industry 4.0 Research Agenda for Sustainable Business Models

On sustainable production networks for industry 4.0

Distributed manufacturing network models of smart and agile mini-factories

Logistics 4.0 and emerging sustainable business models

Investigating the Effects of Smart Production Systems on Sustainability Elements

Opportunities of Sustainable Manufacturing in Industry 4.0

From automated home to sustainable, healthy and manufacturing home: a new story enabled by the Internetof-Things and Industry 4.0

Advanced technologies in life cycle engineering

Kiel, D., Müller, J.M., Arnold, C., Voigt, K.-I.

Lin, K.C., Shyu, J.Z., Ding, K.

Man, J.C.D., Strandhagen, J.O.

Prause, G., Atari, S.

Rauch, E., Dallasega, P., Matt, D.T.

Strandhagen, J.O., Vallandingham, L.R., Fragapane, G., Strandhagen, J.W., Stangeland, A.B.H., Sharma, N.

Waibel, M.W., Steenkamp, L.P., Moloko, N., Oosthuizen, G.A.

Stock, T., Seliger, G.

Branger, J., Pang, Z.

Stark, R., Grosser, H., Beckmann-Dobrev, B., Kind, S. 2014

2017

2017

Digital Manufacturing Systems: A Framework to Improve Social Sustainability of a Production Site

Gregori, F., Papetti, A., Pandolfi, M., Peruzzini, M., Germani, M.

Year

Title

Authors

Table 3. Continued

Procedia CIRP

Journal of Management Analytics

Procedia CIRP

Procedia Manufacturing

Advances in Manufacturing

22 (1), pp. 3-14

2 (4), pp 314-332

40, pp. 536-541

8, pp. 731-737

5 (4), pp. 359369

10 (3-4), pp 185-205

4 (4), pp 421-431

Entrepreneurship and Sustainability Issues International Journal of Agile Systems and Management

63, pp. 721-726

9 (5), art. No. 786

21 (8) art. no. 1740015

63, pp. 436-442

Volume/Issue/ Pages

Procedia CIRP

Sustainability

International Journal of Innovation Management

Procedia CIRP

Journal

Theoretical (not empiricalbased)

Theoretical (not empiricalbased)

Theoretical (not empiricalbased)

Theoretical (not empiricalbased)

Theoretical (not empiricalbased)

Case Study

2 companies

Ecological

Social/ Ecological

Social/ Ecological

Ecological/ Social

Ecological

Social/ Ecological

Social/Eco-logical

Case study+ semi- structured expert interviews+ quantitative analysis of internal business process data

Social

Social/ Ecological

Social

Aspect

Ecological

1 company

107 policy tools from China, 103 policy tools from Taiwan

46 manu-facturing companies

1 production line

Sample

Theoretical (not empiricalbased)

Literature review of policies

Multiple case study with expert interviews

Case study

Methodology

 Industry 4.0 in the Context of the Triple Bottom Line of Sustainability

151

152

Chapter 9

Industry 4.0 and the Internet of Things (IoT) Zelal Gültekin Kutlu İnonu Universty, Turkey

ABSTRACT In this study, the periodical differences of industrial revolutions, which is one of the effects of technological developments in the industrial field, and the last stage of it are mentioned. With the latest industrial revolution called Industry 4.0, machines work in harmony with technology at every stage of industrial areas. This period, known as Industry 4.0 or the fourth industrial revolution, refers to the system in which the latest production technologies, automation systems, and the technologies that make up this system exchange data with each other. In addition to the information technologies and automation systems used in Industry 3.0, industrial production has gained a whole new dimension with the use of the internet. With internet networks, machines, operators, and robots now work in harmony. At this point, the concept of internet of objects becomes important. Therefore, another focus of the study is the concept of internet of objects. There are some assumptions about the uses, benefits, and future status of the internet of things.

INTRODUCTION Industry 4.0 refers to the use of the latest developments in today’s technologies in the industrial areas. Before examining this period, also called 4. industrial revolution, it is useful to examine the process of industrial revolutions to the present day.In the first industrial revolution that started in the 1700s, the production of steam machine and the use of weaving machines increased. During this period, small workshops turned into large factories where machines were used. This transformation is the first development of machinery to replace manpower(Dombrowsi & Wagner, 2014). By the 1800s, the further development of technology led to the second industrial revolution. The use of electricity in production has led to the creation of production lines and the transition to mass production (Tunzelmann, 2003). In the second industrial revolution, the development of transportation networks is of great importance.

DOI: 10.4018/978-1-7998-8548-1.ch009

Copyright © 2021, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

 Industry 4.0 and the Internet of Things (IoT)

Developing transport networks have increased access to remote markets and facilitated the acquisition of new raw materials. In this period, rather than steam power, the use of electricity in the production process facilitated the transition to mass production. Significant advances in digital technologies in the 1900s paved the way for the third industrial revolution. The use of information and communication technologies in the production process has caused manpower to be replaced by machines. Computer technologies and communication technologies are revolutionary not only in production but also in all areas of life. The establishment of an inter-computer network system for military intelligence in the 1960s is considered as the first example of the Internet. In the United States, an intercomputer network system called ARPANET was developed. Subsequent developments, the first IP addresses have been identified. With the introduction of computer systems and the use of the Internet, technological developments have become inevitable. The aim of this study is to examine the latest industrial revolution, industry 4.0, to examine the dimensions reached by the technologies used in industry 4.0, and to reveal the importance of the Internet of Things concept in the 4th Industrial Revolution. According to this study, productivity and profitability can be increased in the industrial field with the internet of objects and other developing technologies. As in the basic philosophy of society 5.0 mentioned in the last part of the study; simple tasks can be done by robots so that individuals can concentrate on more complex tasks requiring higher intelligence. The rest of this research is organized as follows. In the second part, literature review is given. Chapter 3 describes the technologies used in industry 4.0, the definition of the Internet of Things, one of the components of industry 4.0, their uses, the benefits of their applications, and the possible negative consequences. Section 4 aims to introduce a new concept, Society 5.0. Finally, there is a conclusion section.

BACKGROUND The concept of the 4th Industrial Revolution was first used in 2011 at the Hannover Fair in Germany (EBSO, 2015). Industry 4.0 generally consists of three structures: The Internet of Things, The Internet of Services and The Cyber Physical Systems. The concept of Industry 4.0 can be defined as modular structured smart factories, monitoring with cyber-physical systems, making a virtual copy of the physical world and making decentralized decisions (Lee et al., 2015). It is seen that Germany, which introduced the concept of Industry 4.0 to the world, leads the researches published on this subject. As of 2016, half of the 56 studies evaluated included at least one researcher from Germany. Germany is followed by China with 11 studies. These are followed by developed countries such as England, Spain and America. It is seen that the concept of Industry 4.0 has started to find its place in the literature after 2014 and researchers and academic journals attach more importance to this issue (Pamuk & Soysal, 2018). In addition to the academic perspective, we need to examine the use of industry 4.0 technologies in the manufacturing industry and in everyday life. Industry 4.0 is claimed to be beneficial in productivity, turnover growth, employment and investment.By examining its effects on a global scale, digital production technologies in the manufacturing sector; Sensors, control systems, radio frequency technologies. Using these technologies, many sectors such as automotive and textile sector increase production quantity and quality while saving time and labor.

153

 Industry 4.0 and the Internet of Things (IoT)

It is argued that Industry 4.0 will create a serious employment problem. The optimists argue that the technological elements of Industry 4.0 can create new opportunities for individuals and firms. Crises and wars have been overcome through the development of new business areas and increased profitability in production, and despite the Third Industrial Revolution it has always been possible to balance the huge wave of unemployment. During the Third Industrial Revolution, especially in the automotive sector, automation increased and unemployment did not increase. On the contrary, the economic growth that has come with the revolution has led to the emergence of new and innovative business fields, the emergence of new professions (such as the maintenance / repair of robots and machines in automation) and the increase in job opportunities. In the Fourth Industrial Revolution, as long as productivity increases as a result of the use of artificial intelligence and robotics, profitability will increase and employees will not be dismissed (Wisskirchen et al., 2017). In another optimistic view, artificial intelligence can be complementary, not substitute for humans. For example, with the use of unmanned aircraft, some professions, including human pilots, have disappeared and new business opportunities have emerged in the fields of maintenance, repair, remote control, data analysis and cyber security. The US air force needs thirty people per aircraft to take off an unmanned aircraft and eighty people to analyze post-flight data. If this example proliferates in many jobs and sectors, the labor market of 2050 can be shaped with a focus on artificial intelligence-human cooperation instead of artificial intelligence-human competition (Harari, 2018: 43). According to the pessimistic aspect of the discussion, Industry 4.0 will bring some disadvantages in the short term in the labor market. A few million people may face the threat of losing their jobs, especially in sectors requiring low and medium skills. As employees lack adequate training, they may not be able to obtain employment opportunities in other sectors. As a result of the increase in the use of new machines and intelligent information systems, people may become increasingly insignificant. This may lead to social conflicts, such as the fear of unemployment and the widening gap between the rich and the poor (Wisskirchen et al., 2017). In another pessimistic view, technology not only takes people away from their jobs, but also leads to cheaper workers being left to work (Brynjolfsson and amp; McAfee, 2011). Industrial developments in line with consumer needs ensure that everyday life is equipped with smarter systems. In the United States, for example, smart cities have been developed with the use of the Internet of Things. Trash cans are manufactured to provide wifi service. In addition, intelligent waste bins generate electricity from waste (EBSO 2015). A lot of research has been done in the literature on the Internet of Things (IoT) which is one of the components of Industry 4.0. It is not possible to mention each of these studies in terms of content. Sharing the studies according to their subjects will provide sufficient information about the literature. There have been many studies on trust. One of these studies is the “A survey on trust management for Internet of Things” that Zheng Yang, Peng Zhang and Athanasios V. Vasilakos conducted together in 2014. Another research is Michele Nitti, Roberto Girau, and Luigi Atzori’s ‘’Trustworthiness Management in the Social Internet of Things”. In the studies, trust management was mentioned and the perceptions of people about uncertainty and risk, the concept of trust and trust management were examined(Yan at al., 2014; Nitti at all., 2014). IoT applications in the industrial sector have been reviewed. Some industrial IoT projects have been carried out in areas such as agriculture, food processing industry, environmental monitoring, security surveillance. As an emerging technology, the Internet of Things (IoT), the functioning of many existing industrial systems such as transportation systems and production systems and promising solutions to change their roles. Recent research on IoT from an industrial point of view has been examined. They 154

 Industry 4.0 and the Internet of Things (IoT)

introduced the background of IoT and SOA models and then discussed the basic technologies that can be used in IoT. They analyzed research challenges and future trends in IoT (Xu, He and Li, 2014). In security research, they examined Sybil attacks and defense schemes at IoT. In the research, they presented some Sybil defense programs including Social Graphbased Sybil Detection (SGSD), Behavior Classification-Based Sybil Detection (BCSD) and comprehensive comparisons with mobile Sybil detection. Finally, challenging research topics and future trends for Sybil defense in IoT are discussed (Zhang at al. 2014). The concept of Green IoT has emerged to reduce its impact on carbon dioxide (Co2) release. In the studies performed, it proposes a minimal energy consumption algorithm model for the systems proposed to realize Green IoT. Includes numerical results for minimum energy consumption and network life of the system (Huang at al., 2014). The adaptation of the industries to the developing technology has brought many changes. Industrial organizations that need to renew themselves to create new production systems will maintain their leading position in economy and society with these innovations. In the literature, modern production systems are thought to consist of cyber physical and human cognitions, and it is said that the Internet of Things integrates these components (Thramboulidis and Christoulakis, 2016).In addition, studies on E-Health, Home Automation, Smart Environment, Smart Water, Smart Agriculture, Smart Livestock, Smart Energy, Smart Cities, Smart Measurement, Industrial Control, Security and Emergency Situations, Shopping, Logistics have been carried out. While the 4th industrial revolution discussions have been going on, the 5th industrial revolution has started to be discussed. This new era, called Society 5.0, evaluates the social effects of digitalization from economic, ethical and educational aspects and defines the most efficient designs in human-technology interaction with the “super smart society” model. This philosophy aims not only to increase the power of technology with intelligent systems, but also to improve the quality of life and education of the society (Yeditepe University 2019). Since Society 5.0 is a new concept, there are very few studies on this subject in the literature. It was first introduced by Japanese Prime Minister Shinzo Abe (Serpa and Ferreira, 2018). Then, with the introduction book prepared by Keidanren, the Federation of Japanese Economic Organizations, the economic and sociological reforms expected to develop in the light of Society 5.0 philosophy were explained to the masses. It is claimed that it will be possible to solve the negative effects of Industry 4.0 on employment in particular with the Community 5.0 model. It is emphasized that the education of individuals with high level skills can be achieved through education, the creativity aspect of the society should be revealed and the necessity of raising the level of social welfare (Kaidanren, 2018). There are also several articles published. Studies are needed to improve the subject.

INDUSTRY 4.0 TECNOLOGIES This section first introduces the technologies used in Industry 4.0. These; Cyber Physical Systems, Internet of Services, Cloud Technologies, Big Data, Smart Factories, Learning Robots - Artificial Intelligence, Virtual Reality, 3D Printers - Simulations and Cyber Security Components. Afterwards, the concept of internet of objects, their usage areas, the benefits of applications and possible negative aspects are discussed.

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 Industry 4.0 and the Internet of Things (IoT)

This period, known as Industry 4.0 or the fourth industrial revolution, refers to the system in which the latest production technologies, automation systems and the technologies that make up this system exchange data with each other. In addition to the information technologies and automation systems used in Industry 3.0, industrial production has gained a whole new dimension with the use of the Internet. With internet networks, machines, operators and robots now work in harmony. Industry 4.0 consists of three key components, Cyber physical systems, the Internet of Things and Internet of Services, Cloud Technologies, Big Data, Intelligent Factories, Learning Robots and Artificial Intelligence, Virtual Reality, 3D Printers and Simulations and Cyber Security Components (Hermann et al., 2015).

Cyber-Physical Systems (CPS) Cyber-physical systems are frameworks that connect the real world with the virtual world and enable the interaction of smart objects by connecting to a virtual network (Soylu, 2018). In other words, cyber physical systems provide human and machine communication via internet-based data access and data processing systems (Baheti & Gill, 2011). In doing so, it uses a virtual network system in which objects called internet of objects communicate with each other with their assigned address and an environment consisting of simulations of objects in virtual environment (Özsoylu, 2017). Cyber physical systems consisting of embedded systems, sensors and software programs are mostly used in production processes. Coordination, control, efficiency measurement, error minimization, time saving issues of these kinds of systems have important tasks. These systems can work without the need for people.

Services Of Things Businesses provide access to services using internet services, internet data providers, and web-based software. The concept of Internet of Services refers to the use of new technologies in service delivery, referring to the development and modification of services offered to customers in accordance with today’s technological conditions (Bartodziej, 2017). This system, which triggers new business models, logistics plans and creative designs in the delivery of services, has made it easier to differentiate between competitors. The Internet of Services is defined as an infrastructure that uses the Internet to provide and sell universal services such as health, communication and banking for consumers (Cardoso et al., 2008). At the same time, it provides a research field for the consumers on the subject of research, development, designing, production, marketing, sales and distribution. (Cardoso et al., 2009).

Cloud Technologies This system allows the storage of data on a virtual server called the cloud, and each device connected to the cloud can easily access information, data and programs. Huge data stored with cloud technology without the need for memory and hard disks for data storage provides ease of monitoring and control of processes and creating new data (Davutoğlu et al., 2017). Cloud technology is an internet-based structure where resources, software and data can be accessed and shared by computers and other computer-based devices in line with the user’s wishes. Cloud technology provides flexible and varied services that can be used according to the needs and demands of the users rather than a solid and standard structure. 156

 Industry 4.0 and the Internet of Things (IoT)

These services within the scope of cloud technology generally consist of three structures. Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a service (SaaS). Cloud technology has the advantage that the user can use any application without having to deal with any hardware and software purchase and installation, and multiple people can access the same document simultaneously (Sarıtaş and Üner 2013).

Big Data In today’s conditions where competition is intense, businesses think that even the smallest information is of great importance in order to differentiate from their competitors. Therefore, it aims to access consumer information and data processed during the production process (Davutoğlu et al., 2017). It is planned that the data obtained from many different sources such as media sharing, blogs, photos, videos, log files of consumers will be comprehensively evaluated and all data collected from different sources will be standardized. With big data, businesses will strengthen their knowledge and managers will be able to understand the real-time defects, errors and omissions during production. Therfore optimizing processes with big data will help to determine the potential of using resources efficiently and maintaining the expected product quality at much earlier stages. Big data is also of great importance for the enterprises as it will enable enterprises to make their strategic decisions in accordance with their objectives, manage their risks and make innovations when interpreted with accurate analysis methods (Davutoğlu et al., 2017).

Smart Factories The main feature of the so-called smart factories is that people can communicate in harmony with each other by using IoT, CPS, CBM and Big Data systems. Another feature is the ability to provide cyber security when using these systems and to stop the flow in case of a problem in the process and to have automatic problem solving mechanism. Dark factories, also known as smart factories, aim to produce faster, less defective and unmanned production. An example of this is a telephone part manufacturing factory in China. In this factory which works with smart factory system, the number of accustomers has been reduced by 90%, while error rates have been reduced from 25% to 5% (Wang et al., 2016).

Learning Robots And Artificial Intelligence Smart robots can be operated by an operator or with a pre-installed program. Smart robots, which are mostly used in industrial areas, provide very easy production processes (Davutoğlu et al. 2017). It often saves time beyond human power. With the development of artificial intelligence technologies, robots transform the perceived physical quantities into electrical signals and send them to the decision mechanism. As a result, it performs the necessary task and fulfills the task assigned to it. In addition, the fact that the robots differ from human beings in terms of flexible working conditions encourages the use of robots in production (Özsoylu 2017).

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Augmented Reality Augmented reality is the transfer of the real image to the virtual environment through computers, the enrichment of the virtual environment and the real image creates experiences as if it were in the prepared virtual world (Davutoğlu et al., 2017). Augmented reality technologies are used in design, entertainment, education and research (Kurbanoğlu 1996). The use of new technologies in the industry can be mostly used in the design and prototyping stages. Virtual reality can be used as an assistive technology in production and it can also become a direct sector.

3D Printers It is the technology that converts an object designed with the help of a computer-aided program into real objects that can be handled. With these 3D printers, modeling, 3D printing, and surface improvement works can be done. These devices reduce production costs to a great extent and provide rapid prototyping and modeling. The rapid production of the product and the decrease in the stock cost will enable the production with low prices with the decrease of the total cost of the product (Özsoylu 2017; EBSO, 2015).

Cyber Security Systems Initially, the purpose of the internet to disseminate information and benefit over time has deviated from its purpose. Therefore, the security of the internet has caused concern. The widespread use and diversification of the use of the internet has become an important security issue. Cyber security, economic systems, commerce and information systems have become dependent on the internet. Unauthorized access to systems has been a serious problem as it causes information theft. (Ögün and Kaya 2019). Cyber attacks can be carried out in various ways. These; Worm, trojans, zombies and botnets, phishing, spam service blocking attacks (DoS, DDoS), key logger, spyware, network traffic listening (sniffing and monitoring) can be given as examples of these kinds of attacts (Ünver and Canbay 2010). Businesses may have to work with cyber security experts for reasons such as being able to use information technologies efficiently, not interrupting the communication of smart devices and preventing unauthorized access to their data (Akben and Avşar, 2018). In this sense, it is necessary to make big investments by governments and private institutions and take security measures at national and international level. Considering the damage and loss of reputation due to security gaps, it is considered that states and institutions will not avoid cyber security investment costs (Ünver and Canbay 2010).

Internet of Things (IOT) As one of the technologies of Industry 4.0, the concept of internet of developing objects forms the basis of the technological developments of the future. The Internet of objects can be defined as the way that existing objects access the Internet in some way and communicate with other devices. Objects that have a digital network and the Internet communicate with their environment in a physical and social context through a virtual identity. That is, the objects are communicating with each other by using the internet as an intermediary and managing the things themselves. This concept, which was first used by Kevin Ashton in 1999, is defined as the system that enables objects to communicate with each other using internet networks (Öz and Topaloğlu, 2016). Objects must have a virtual identity (IP address) with access 158

 Industry 4.0 and the Internet of Things (IoT)

to a digital network and the Internet. In this way, it will be possible for the objects to use the internet as a means of communication with each other and plus their environment and to manage the works themselves without the need for human factors (Davutoğlu et al., 2017). According to the concept of internet of objects, “objects” can be actively involved in social processes, interact with each other and the environment, perceive the environment in which they are located, exchange information with each other, react automatically to the events happening around them and perform various capabilities with or without human intervention (Sundmaeker et al. 2010).In its 2005 report, the International Telecommunication Union (ITU) argued that, as a result of technological developments, the objects of the world would be connected both sensually and intelligently. Identifying items (labeling objects); sensor and wireless sensor networks (sensing objects); embedded systems (thinking of objects); nanotechnology (reducing objects) in the form of the Internet of objects has been staged. The aim is to comply with existing technologies and to establish universal standards, to be ethical in security and privacy issues (ITU 2015). The Internet of Things (IoT) consists of three main components: Objects themselves: The addition of devices and sensors that can capture or produce data to objects such as coffee machines, refrigerators or ovens in our home. System that binds objects: A network that binds objects capable of capturing or generating data. Computer systems: Computer systems containing software and hardware that process and use data received or sent by objects(Keleş and Keleş 2018). Many researchers working in the fields of science, academics, businesses and government institutions examine the technologies developed with the concept of internet of objects under three headings. The first one is scientific theories, the second one is engineering designs and the last one is user experiences (Feki et al. 2013). Improving prototypes of scientific theories developed to facilitate life has enabled ideas to acquire a physical dimension. In this context, engineering designs are made available to consumers, new designs and ideas are developed with feedback from consumer experiences. Internet technology enables information sharing and communication between individuals. On the other hand, the internet of objects facilitates the communication of objects with each other and objects with sensors (Hsu & Lin, 2016). Near Field Communication (NFC) in short-range wireless technology of approximately 13.56 MHz and 4 cm has features such as processing, digital content exchange and electronic devices with one touch to make life easier for consumers (Madakam et al., 2015). The Internet of Things and NFC technology enable communication between devices without human factors. RFID systems, which can be considered as another assistive technology, enable data to be read with microchips without contact with objects (Bremer, 2015). Cloud Computing technologies aim to store and convert data produced by millions of smart devices into meaningful information. In the future, Cloud Computing is replaced by Sis Bilişim. In order to increase the potential of IoT, it is recommended to use Cloud Computing because it is frequently mentioned and more advanced IT technology (Özdemir, 2018). IOT is used in industrial and daily life. It is used in many stages from manufacturing process to logistics in industrial area. Smart factories created with the use of smart devices show the productivity increase and profitability of iot technology in production. In addition, process management is faster and more follow-up thanks to iot technology in transport, distribution and logistics management. The basic features of IoT are autonomous operation, fast communication, common systems and standards, realtime production and data transfer, and autonomous operation of the machines to make their own decisions about adjusting the operating speed and using energy efficiently. Intelligent production machines can communicate directly with other machines operating in the same production line or with the cloud system over the network. When production-related settings are made in the cloud, intelligent production

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machines adapt themselves to these changes in real time. It requires the development of new communication protocols based on fiber infrastructure for internal communication. It has published specifications to ensure that protocols written for the communication of intelligent machines meet certain standards. With automatic flexible production, the production speed is adjusted according to the incoming demand, thus avoiding unnecessary stock keeping and reducing costs (Ercan and Kutay 2016). Smart cities are the most obvious examples of the use of iot technology in daily life. Infrastructure, building, security, health, energy management, transportation, education and other management areas are built and executed with intelligent systems. Smart city applications are discussed in more detail in iot applications. The problems encountered in IoT applications are data extraction and conversion to information, compliance problem and standards, protection of information confidentiality, network security, limited frequency field and high energy consumption (Ercan and Kutay 2016). The applicability of IoT technology on these issues needs to be improved. Technologies that contribute to the Internet of objects include assistive technologies such as sensors, wireless detection networks, near-field communications (NFC), radio frequency identification (RFID), and cloud systems. The development of these technologies for iot applications is another challenge. In order to better understand the Internet of Things, it is necessary to examine the applications and areas where they are used as examples. Therefore, iot applications will be explained in the next section of the study.

Internet Of Things Applications Today, The Internet of Things, Smart home and smart city applications, scientific studies, IT, construction, energy, agriculture and animal husbandry, transportation, industrial production, trade, public services and security applications are used in the fields. This data is stored in cloud computing systems by creating big data. They are analyzed with the methods of Machine Learning and contribute to the improvements (Görkem and Bozuklu, 2016). In smart home applications; light sources and lighting, control and switch systems, heating, cooling, ventilation systems, shutter-curtain systems, burglar alarm water control, gas leakage, camera monitoring, music and cinema systems, consumption data collection and meter reading, earthquake warning, pool control, fire fighting, garden maintenance control, mobile phone communication systems are used (Steel 2014). Water quality control, bridge stability, fire fighting, air pollution control, waste container and waste control, vehicle parking control, radiation, traffic, noise controls, human density controls are carried out (Libelium, 2013). In modern agriculture, there are applications that use the Internet of Things platform. These applications include soilless agriculture, protection of plant health, studies aimed at increasing the quality and productivity of harvest, continuous measurement and monitoring of air conditioning conditions, forecasting and taking precautions of climate conditions, efficient use of natural resources (Görkem and Bozuklu, 2016). In animal husbandry sector, technological studies are carried out in order to understand animal health, high productivity and production of quality animal products and animal behavior (Kang et al., 2015). In order to improve public services, especially in the field of e-health, applications have been developed to enable patients to benefit from health services without going to hospitals (Görkem and Bozuklu 2016). Again, in the field of education, there are courses held in virtual environments through the use of smart devices and internet access (Altınpulluk 2018). Internet applications of objects objects like smart

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boards, cameras and videos, tablets and e-books, electronic ID cards, smart air conditioning, 3D printers, security systems are used in education today. Other programs are developed for school administration, students and parents. In the energy sector, energy savings are achieved through energy efficiency measures developed especially by utilizing smart home, smart building and smart city applications (Cho et al. 2013; Li 2014). It is also aimed to raise awareness about the use of convertible energy sources with intelligent measurement systems. It is aimed to estimate consumption trends, calculate consumption amounts and produce saving solutions through smart devices (Oprea and Lungu, 2015). With the use of the internet of objects in the industrial field, the smart production period has begun. It is thought that smart robots designed with artificial intelligence technologies will help people in these areas called smart factories and even dark factories can be established without the need for people (Aksoy 2017). With the software installed on the production lines and machines in dark factories, production is required without the need of operator. In this case, the possibility of making mistakes due to human nature will be minimized and productivity will be increased with more rapid and error-free production (Akben and Avşar, 2018). Thanks to the smart factories, the management and production processes in the factories will be realized automatically and will be self-sufficient in the solution of the problems related to the process. Thanks to cyber-physical system technologies and artificial vertical integration, supply chain management, energy capacity, human resource capacity and efficiency management can be managed effectively (Davutoğlu et al. 2017). Internet applications of major objects used in logistics and vehicles: vehicle tracking systems, vibration, impact, monitoring of openings of containers and cold storage for insurance purposes, ensuring the quality of shipment conditions, determination of the location of goods in large areas such as warehouses or ports, storage in flammable goods near explosive containers detection of mismatches, fleet tracking systems for the control of roads for sensitive goods such as medical drugs, jewelery or dangerous commercial goods (Görkem and Bozuklu 2016). A few examples of the use of the Internet of Things can be given; Belkin’s priz WeMo connected home appliances is designed to increase energy efficiency, Smart outlet developed by Philips, Hue smart bulbs, a smart thermostat Nest that can be connected to the Internet, is designed to automatically increase energy efficiency and save money. Thanks to August Smart Lock systems and SmartThings system developed for security, all your lights, locks, sockets, thermostats, cameras and speakers are managed from a single central unit using your smartphone. The health-related Kolibree smart toothbrush is designed to give both children and adults the habit of brushing teeth. The smart feeder developed by Petnet will contact the supplier when your stock is finished. The smart feeder can be controlled with your smartphone and you can monitor whether the pet eats its food. With the device called Healthpatch, patients can be examined before going to the hospital and necessary tests can be requested. Heart rate, breathing rate, skin temperature, such as values ​​can be measured, you can check your body posture, when you fall and send news to health care professionals. Thanks to this communication feature, doctors can intervene before their health problems occur. Even if the problem has already occurred, it helps the doctor determine the right treatment option. The good thing is that the patient doesn’t need to go to the doctor’s office to do all this. Another system developed is similar to Healthpatch and aims to help people with health problems. The central system connects to a smart watch. Family members are notified when they face any health problems. In addition, if the person falls or something else happens, the system can send news to his family or call an ambulance. Passive sensors placed in various parts of the house monitor the activities of the people, remind them when they need to take medication, and warn 161

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when the meal is skipped or the amount of physical movement decreases. Even when the wearer is away from the central system, the Android app continues to follow these tracks. In the automotive sector, the Automatic app keeps track of your car’s status thanks to an adapter installed in the car. In other words, it measures your mileage, counts how many hours you drive, calculates fuel cost and efficiency, reports your location, and states if there are any problems with the contact. DHL in Logistics Internet of Things; vehicle monitoring and maintenance, real-time tracking of packages, environmental sensors in containers, gathering information about employees and tools, vehicles and employees can be useful to improve safety. All these technologies may take a long time to become available, but once these technologies become available, efficiency in the logistics and transport sector can be significantly increased. As a leading IT company, Cisco encourages companies to make their production processes more efficient by using Internet of Things technologies in their factories. The use of remote monitoring and equipment access technologies in production greatly improves productivity, enables quicker resolution of problems, and consequently increases production. A lot of people may laugh at them, but the stakeholders and businessmen who have begun to pay off the investments would show great interest in such innovations. In the construction sector, Oceanit adds nanosensors to the cement, enabling the cement to act as a sensor and to send and respond to mechanical, acoustic and magnetic signals. The company uses oil drilling to illustrate the product. For example, cement pouring around a well is sending information to the workers, which can determine the robustness of the well and assess risks more accurately. This technology can be used in many places from pavements to hydroelectric power plants (Yeni iş fikirleri, not date).

The Benefits And Potential Problems of Internet of Things Applications The use of Internet of Things applications is becoming widespread. It is used in daily life as well as in the sector. Being in such a daily life has led to discussion of the advantages and disadvantages of these applications. This discussion is held by the academic and business community and includes bipolar views. One group argues that these practices are entirely advantageous, while the other group has disadvantages. The use of the Internet of Things technology in the field of health will create significant differences in human life in terms of monitoring the health data of people, monitoring health status and storing the data obtained. (Agrawal and Das, 2011). It will make a significant difference in terms of monitoring the body through microchips that can be eaten and biologically decomposing in the human body, storing health records and observing an emergency in a convenient way (Bandyopadhyay and Sen, 2011).With the use of wearable technologies in healthcare, the margin of error is minimized. Analyzes made through these technologies can provide more accurate results since they eliminate human errors. With the use of the Internet of Things, environmental information such as climatic factors, noise and radiation, natural disasters will be collected, processed and stored, the system will be alerted automatically in an emergency and measures will be taken (Chen, et al., 2014). The internet of objects can work in collaboration with independent networks, and critical information can be transmitted quickly to long distances. In this way, there will be an opportunity to intervene very quickly despite a negative situation (Agrawal & Das, 2011). It will provide useful solutions to the difficulties faced by the transportation systems of the Internet of Things, the instant location, movements and route of the personal or public transportation vehicles used in daily life can be followed easily and the obtained information will be processed and stored. In addition, vehicles equipped with this technology will provide the driver with information such as the instant traffic situation, alternative routes, estimated journey time, and free parking space (Xu, et al., 162

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2014). In the not-too-distant future, automobiles will read traffic signs and lights with the power of the Internet of objects, and use alternative paths by taking into account the traffic situation by processing the data they acquire via sensors, satellites and the Internet (Greengard, 2017). The Internet of Things technology can be used in the cities we live in as in all other areas. “Smart cities tır will begin to form. Cities where resources are used efficiently and intelligently, energy and cost savings, service delivery and quality of life are improved, environmental pollution is reduced and low carbon emissions (Çelikyay, 2013) are used to improve the living standards and quality of people living in the city by using the internet of objects. Accordingly, the traffic situation is monitored and controlled, the quality of the air is monitored, measurements are provided and even the fullness of the waste containers will be monitored by the technologies to be used (Whitmore, et al., 2015). The Internet of Things will increase productivity in production with intelligent factories, learning robots with artificial intelligence, cyber physical systems and cloud technologies. It will enable targets such as zero error, zero inventory cost and time saving to be realized. With distribution channels and intelligent logistics systems, quality services with less cost and maximum customer satisfaction will be realized. The use of Internet of Things applications is becoming widespread. It has started to be used in daily life as in the sector. The increase in the use of these applications has brought about the negative effects it will create along with its benefits. It is thought that it has some negative consequences with the fact that it makes life easier. The possible negative effects of iot applications can be listed as causing environmental problems related to energy consumption in general, on employment, causing security problems and violation of privacy, and not establishing universal standards. The internet of objects refers to smart devices connected to each other through a virtual network. Devices that continuously produce and process data consume enormous energy. Large energy consumption causes environmental problems and consumption of natural resources. This is one of the negative features of the technology. It is not yet possible to develop technologies to meet the energy consumption of intelligent systems. Therefore, energy consumption and its effects on environmental conditions are one of the negative consequences of IOT applications. Security and privacy are the most important problems that need to be addressed. Not only do these devices collect personal information such as names and phone numbers, but what time did you leave work, monitor when you came to your home, and even with whom you had lunch. As a result of the security breaches that are constantly occurring from the shared or private cloud, users are rightly afraid to overload personal data (Keleş and Keleş 2018). IOT applications require extensive security measures. Management of systems subject to cyber attacks is one of the major problems. A problem encountered in iot applications is that universal standards cannot be established. Various application protocols, service discovery protocols, infrastructure protocols and other effective protocols have been established in this field. Nevertheless, it will take time to establish universal standards (AlFuqaha at al.,2015). It is thought that the use of iod applications, especially in the industrial field, will affect employment negatively. Although it is argued that the use of technology will create new employment areas, it can be said that these areas will also have a share with the success of technology in time. as a result, it can be said that iot applications may have negative effects and consequences as well as benefits.

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THE FUTURE AND SOCIETY 5.0 Technology should be perceived by society as a helper, not as a threat, Japonya said Japan’s president Shinzo Abe for the first time at CeBIT. Society 5.0 evaluates the effects of digitalization and robots demographically, economically, ethically and sociologically, and proposes a model of “super intelligent society people ık in which people are most effectively connected to machines and robots. The booklet that includes the introduction of Community 5.0 in 2018 by the Kaidanren Foundation consists of 5 chapters (Keidanren, 2018; Serpa and Ferreira, 2018). In the first part, social transformations in human history; hunter society, agricultural society, industrial society, information society and super smart society. Over time, people interested in hunting became interested in agriculture and led to a settled life. The societies that started to produce with agriculture started to industrial production. In this period, when the machines started to replace manpower, the use of automation technologies and internet in production, together with the invention of the computer and the transition to information technologies, created the information society. Changes in other industrial revolutions have led to development and progress in various subjects, but also to social problems. For this reason, the society 5.0 philosophy aims to develop solutions for the aging world population, to produce solutions for environmental pollution and natural disasters, to make the virtual world and the real world work together, and to make use of the internet of objects in consideration of the interests of the society. The information society emphasized technological progress on industrial issues. To emphasize the transformations created by this progress at the social level, the new era society, which focuses on the background of individuals and their lives, is called 5.0. The basic philosophy of Society 5.0 is to develop economic development and solutions to social problems to adapt to increasing competition. In the second part of the booklet, the necessity of digital transformation and basic digital transformation technologies are mentioned. These technologies include the Internet of Things (IoT), artificial intelligence (AI), robotic systems and Distributed Ledger Technology (DLT). In the third section; It is claimed that community 5.0, digital transformations and the creativity of society will contribute to the solution of problems and the creation of value. The social reform in Society 5.0 includes a community of free, equal and open-minded individuals, where people with individual values are at the forefront. The new value created through innovation will eliminate regional, age, gender and language gaps and ensure the provision of products and services tailored to a variety of individual needs and hidden needs. In the fourth section; It describes the design of a society where everyone can use different talents, find opportunities whenever and wherever they want, where safe, humanity lives in harmony with nature, and where value is created. Every individual, including the elderly and women, can live safely and comfortably, and each person can achieve the desired lifestyle. Increasing productivity through the digitization and reform of business models is encouraged and at the same time a new economy and society will be realized by promoting innovation and globalization. efforts are being made to solve many of the countries’ problems such as falling population, super-aging society and natural disasters, thus ensuring the realization of a rich and vast future. With the expansion of new businesses and services abroad, we can also contribute to the solution of global problems.

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In the fifth section; The society 5.0 has 17 goals that will change the world. Solutions to poverty and hunger, quality education, health services, gender equality, clean water, recycled energy, economic growth and employment, industrial innovation and infrastructure, sustainable cities and communities, solutions to climate problems, responsible consumption and production, water and life in the land. There are five main obstacles that Community 5.0 needs to overcome for its realization. This philosophy, which appears as an utopia, is the obstacles in the legal system, the scientific gaps in the digitalization of objects, the lack of qualified personnel, socio-political prejudices and social resistance.

CONCLUSION Innovations that are constantly evolving throughout human history and technologies developed in parallel with these inventions have led to transformations in every aspect of life. A technology that is intended to be developed exclusively in the field of health is also adapted to the education, industry and entertainment sector. A tiny idea and camera system developed for the follow-up of the coffee machine in an institution where engineers work has revealed the concept of internet of today’s objects. has become a technology. By integrating the idea of a small business facilitation with technological advances, the creation of intelligent objects with the skills of thinking, analyzing and problem solving that a human brain can do is called industry 4.0. In Industry 4.0, objects have digital identities and autonomous features. These systems, which are intended to facilitate human life, create intelligent buildings and smart cities, while simplifying daily life, they also provide solutions to the environmental problems created by the system. The problem of big communication and storage of data networks with the internet of objects is solved by big data and cloud technologies. Today, as we can shop with virtual money, in the future, all processes can be managed with the Internet of Things without the need of objects or with a small number of objects. Technological advances will allow communication with a chip placed on our skin without mobile devices, access without telecommunication technology, perhaps with irradiation technology, training materials without usb, training with usb logic, and production with virtual simulations without machines. With Industry 4.0, objects are loaded with great tasks and are intended to manage themselves. In the so-called Society 5.0, beyond the self-management of objects, a virtual world without objects is expected to be managed. For this reason, in the realization of the society 5.0 philosophy, a super intelligent, creative and imaginative society is aimed. While simple tasks are carried out with robot technologies, the management of more complex processes and the production of new creative technologies must be performed by people. This goal can also be considered as a solution to the employment problem, which is claimed to be created by industry 4.0. In this study, the last Industrial revolution, Industry 4.0, is examined and the dimensions reached by the technologies used in industry 4.0 and the importance of the internet of objects, one of these technologies, in the 4th industrial revolution are emphasized. According to this study, productivity and profitability can be increased in the industrial field with the internet of objects and other developing technologies. As in the basic philosophy of society 5.0 mentioned in the last part of the study; simple tasks can be done with robots so that individuals can concentrate on more complex tasks requiring higher intelligence. It is impossible to escape from change, so it is necessary to survive by adapting to the era of digital transformation.

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Özsoylu, A. (2017). Endüstri 4.0. Çukurova Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 21(1), 41-64. Retrieved from https://dergipark.org.tr/tr/pub/cuiibfd/issue/34826/387693 Pamuk, N., & Soysal, M. (2018). Yeni Sanayi Devrimi Endüstri 4.0 Üzerine Bir İnceleme. Verimlilik Dergisi, (1), 41-66. Retrieved from https://dergipark.org.tr/tr/pub/verimlilik/issue/34982/388198 Sarıtaş, T., & Üner, N. (2013). Eğitimdeki Yenilikçi Teknolojiler: Bulut Teknolojisi. Journal of Research in Education and Teaching, 2(3). Serpa, S., & Ferreira, C. (2018). Society 5.0 and Social Development: Contributions to a Discussion, Journal reference. Management and Organizational Studies, 5(4), 26–31. doi:10.5430/mos.v5n4p26 Soylu, A. (2018). Endüstri 4.0 ve Girişimcilikte Yeni Yaklaşımlar. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 32, 43-57. DOİ: doi:10.30794/pausbed.424955 Sundmaeker, H., Guillemin, P., Friess, P., & Woelfflé, S. (2010). Vision And Challenges For Realising The Internet Of Things. Cluster Of European Research Projects On The Internet Of Things, European Commision, 3(3), 34–36. doi:10.2759/26127 Thramboulidis, K., & Christoulakis, F. (2016). UML4IoT-A UML-Based Approach to Exploit IoT in Cyber-Physical Manufacturing Systems. Computers in Industry, 82, 259–272. doi:10.1016/j.compind.2016.05.010 Tunzelmann, N. V. (2003). Historical Coevolution Of Governance And Technology İn The İndustrial Revolutions. Structural Change and Economic Dynamics, 4(14), 365–384. doi:10.1016/S0954-349X(03)00029-8 Ünver, M., & Canbay, C. (2010). Ulusal ve Uluslararası Boyutlarıyla Siber Güvenlik. Elektrik Mühendisliği, (438), 94-103. Retrieved from http://www.emo.org.tr/ekler/a9a502d6e646c25_ek.pdf?dergi=598 Wang, S., Wan, J., Zhang, D., Li, D., & Zhang, C. (2016). Toward Smart Factory For İndustry 4.0: A Self Organized Multi-Agent System With Big Data Based Feedback And Coordination. Computer Networks, 101, 158–168. doi:10.1016/j.comnet.2015.12.017 Whitmore, A., Agarwal, A., & Da Xu, L. (2015). The Internet Of Things—A Survey Of Topics And Trends. Information Systems Frontiers, 17(2), 261–274. doi:10.100710796-014-9489-2 Wisskirchen, G., Biacabe, B. T., Bormann, U., Muntz, A., Niehaus, G., Soler, G. J., & Von Brauchitsch, B. (2017). Artificial Intelligence and Robotics and their Impact on the Workplace. IBA Global Employment Institute. Retrieved from https://www.ibanet.org/Document/Default.aspx?DocumentUid=c06aa1a3d355-4866-beda9a3a8779ba6e Xu, L., He, W., & Li, S. (2014). Internet of Things for Industries: A Survey. Transactions On Industrial Informatics, IEEE, 2233-2243. Doi:10.1109/TII.2014.2300753 Yan, Z., Zhang, P., & Vasilakos, A. V. (2014). A survey on trust management for Internet of Things. Journal of Network and Computer Applications, 42, 120–134. doi:10.1016/j.jnca.2014.01.014

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Yeditepe Universty. (2019). Endüstri 4.0‘dan Toplum 5.0’a. Retrieved from https://www.yeditepe.edu. tr/tr/haber/yeditepe-universitesi-endustri-40dan-toplum-50a-gecti Yeni İş Fikirleri Nesnelerin İnterneti Nedir? Örnekleri Nelerdir? (n.d.). Retrieved from http://www. yeniisfikirleri.net/nesnelerin-interneti-nedir-ornekleri-nelerdir/ Zhang, K., Liang, X., Lu, R., & Shen, X. (2014). Sybil Attacks and Their Defenses in the Internet of Things. Internet Of Things Journal, IEEE, 372-383. Doi:10.1109/jiot.2014.2344013

This research was previously published in Internet of Things (IoT) Applications for Enterprise Productivity; pages 1-24, copyright year 2020 by Business Science Reference (an imprint of IGI Global).

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A Comprehensive Study on Internet of Things Based on Key Artificial Intelligence Technologies and Industry 4.0 Banu Çalış Uslu Marmara University, Turkey Seniye Ümit Oktay Fırat https://orcid.org/0000-0002-0271-5865 Marmara University, Turkey

ABSTRACT Under uncertainty, understanding and controlling complex environments is only possible with an ability to use distributed computing by the way of information exchange between devices to be able to understand the response of the system to a particular problem. From transformation of raw data in a huge distribution of network into the meaningful information, to use the understood knowledge to make rapid decisions needs to have a network composed of smart devices. Internet of things (IoT) is a novel approach, where these smart devices can communicate with each other by using key technologies of artificial intelligence (AI) in order to make timely autonomous decisions. This emerging technical advancement and realization of horizontal and vertical integration caused the fourth stage of industrialization (Industry 4.0). The objective of this chapter is to give detailed information on both IoT based on key AI technologies and Industry 4.0. It is expected to shed light on new work to be done by providing explanations about the new areas that will emerge with this new technology.

DOI: 10.4018/978-1-7998-8548-1.ch010

Copyright © 2021, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

 A Comprehensive Study on Internet of Things Based on Key AI Technologies and Industry 4.0

INTRODUCTION With the rapid advancement of technology and science, IT technology has paved new ways of research and discoveries on computational intelligence regarding large data set come from things or objects in a distributed environment. These varieties of things or objects is a network of interconnected, integrated and organized objects (Liu et al. 2017) which allows to communication between things to human, human to human, and things to things in order to analyze large amount of data to produce needed information generated by IoT to reach a certain goal (Madakam et al. 2015). In other words, instead of traditional centralized applications, IoT utilize advanced information analytics via smart devices (things or objects) to be able to integrate various components of network connected as a collaborative way to improve productivity, service ability and flexibility of companies. Based upon the analyzed literature, Figure 1 is generated to demonstrate main architecture of IoT covering basically three different levels. The first level of IoT determined as level of Big Data that is an ability to turn data into meaningful value (Value). In this level, decision makers deal with vast amounts of data (Volume) with huge diversity (Variety) to control messiness or trustworthiness of data (Veracity) that need to analyze while it is being generated (Velocity). In the second level of IoT architecture is defined as AI level where each node represents an integrated things or objects which can sensing, processing, reacting and control the environment to understand current situation of the system and based on the learned knowledge providing necessary information to utilize future decisions. In the Figure 1, data exchange between Big Data level and AI level is illustrated by bi-directional arrow. The state of environment may change at any time so each thing or object needs to have an ability to adapt to changes and learn from the experience. In this dynamic environment, intelligent and autonomous system is crucial technology in order to obtain integration is managed, coordination is provided and distributed computing is enabled. To utilize and understand intelligent system, need to focus on AI technology that encompasses wide spectrum of research field like machine learning, deep learning, natural language processing, machine reasoning, visual processing, robotics and neural networks. AI application range seems like unlimited. It is possible to see AI –based applications from assistance system for healthcare to smart customer service systems. In section 2 this components, applications, and issues of AI explained in detail. In third level of IoT, each node is linked into a network that composed of set of things or objects working together to make real-time, accurate and coherent decision when a specific input arrive the system. Rising applications of the IoT caused a sweeping change that will fundamentally reconfigure industry and the Fourth Industrial Revolution is emerged. In many case, these two word are accepted as interchangeable. Mainly this two approaches focus on the ways to constant high quality by making system faster and secure at the same time but industry 4.0 is a primarily government and academic-based movement that aims to make process and products intelligent (Brettel et al., 2014). Although IoT takes place in the business world, it is one of the most important effect that trigger the fourth industrial revolution. When the previous industrial revolutions is examined; after usage of steam and steam engines, world was confronted with first industrial revolution in 18th century. Electrification was the main cause to second industrial revolution in 19th century and usage of computers and robots led to third industrial revolution in 20th century. Today, Connecting Cyber-Physical systems (CPS) to physical and digital systems is the main idea of fourth industrial revolution (Fırat, 2016). IoT provides connection and information transfer between physical entity and its cyber twins so that control and monitoring the outcome of physical entity is 172

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Figure 1. Main Architecture of the Internet of Things

possible. Other technologies related to Industry 4.0 is illustrated in Figure 2. These technologies and relations between the IoT is explained in detail in section 2. AI-based applications employ artificial intelligence techniques into things in order to control system complexity through autonomy. In order to build an autonomous system there is strong need an infrastructure that consists of sensors and actuators that interact autonomously to handle created and replicated data that grown exponentially. Autonomy gives decision makers an ability to cope with vast amounts of data with huge diversity to analyze while it is being generated. Extremely large data sets that need to be analysed computationally refers to the concept of Big Data. AI layer that is used to construct the necessary infrastructure for the coordination, cooperation and integration required to produce meaningful information from data obtained from things or objects in a distributed environment is defined as

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Figure 2. Technologies related to Industry 4.0 (Guerreiro et al, 2018)

information network where data is monitored, controlled and stored. Knowledge network is stated as third layer that provides interoperability of different AI-based application in order to create a meaningful knowledge. When this three-layer structure is examined, IoT can be defined as an industrial adaptation that is powered by automation. It is expected that AI-based IoT applications with automation features will contribute significantly to solving problems such as minimum cost, minimum energy consumption, time saving, less resource and less memory usage. The objectives of the IoT almost coincide with the objectives of Industry 4.0. Therefore, this study also referred to Industry 4.0 and its components. Main objective of this chapter is to deeply analyze IoT technology and its components to find out main challenges and advantages of this novel technology, Also, is to provide detailed literature review of applications of IoT technology to emphasis importance of IoT for Industry 4.0. Besides, it is expected to shed light on new work to be done by providing explanations about the new areas that will emerge with this new technology. The rest of this chapter is organized as follows. In Section 2, the main characteristics of IoT are further discussed in detail. Industry 4.0 and big data are briefly explained. In Section 3, AI techniques are discussed and AI applications of IoT are pointed out. Beneficial outcomes for IoT applications are stated from the business point of view. Then, Main challenges of IoT designs are listed in Section 4. Finally, the conclusions and future work are given in Section 5.

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IOT AND MAIN CHARACTERISTICS Basically, IoT composed of two words that are “Internet” and “Things”. The first word “Internet” represents the network structure of the technology, while the second word represents “objects” that can be integrated into one another in a distributed environment (Atzori et al. 2010). In the tremendous network including public and private business systems and personal computers that are connected in order to exchange data, IoT can be define as the new information technology that enables the interconnection (physical and virtual) between things or humans based on connected and interoperable systems (Huxtable & Schaefer, 2016). Concept of IoT is emerged in a presentation by Kevin Ashton (1999) “The Internet of Things has the potential to change the world, just as the Internet did. Maybe even more so”, however officially it took place in the literature by the report published by International Telecommunication Union (ITU, 2005). Later on, based on this phenomenon, companies started becoming more intelligent in order to increase their efficiency by applying features of IoT technologies in their business processes (Santora et al. 2017). IoT consists of sensors and actuators that interact autonomously to handle created and replicated data that grown exponentially. By 2025, expected interaction for an average person in the world with a connected device is 18 seconds, it means that nearly 4.800 times per day (Reinsel et al., 2017). Generated data from many applications and services are expected to reach 50 billion devices in next year (Sharma et al. 2018). In this environment, autonomous data collection, processing, contextual inference, collaborating with other IoT objects and decision making should be supported by any IoT infrastructure (Sarkar et al., 2015). This infrastructure should have characteristics including “self-configuration, self-optimization, self-protection, and self-healing” (Kephart & Chess, 2003; Kortuem et al. 2010) that satisfy an ability to collaborate and interact in order to create new functionality that individual devices cannot provide (Ornato et al. 2017). These characteristics defines the intelligent devices that employ artificial intelligence techniques into things and communication networks (Arsénio et al., 2014) in order to create autonomous systems that enables to control system complexity through the achievement of self-governance (autonomy) and self-management (autonomicity) (Sterritt & Hinchey, 2005). For this systems, design of effective interaction between both between humans and things and between things is only possible with the better if “intelligent” interfaces and infrastructure (Atzori et al, 2017) in order to get real-time data processing. To satisfy this integration and connection between smart devices there is a need of an architecture that is not as simple as to explain as the word structure and equipped with powerful features on the backplane. There are many approaches in the literature but there isn’t any standard form in order to define IoT architecture. In this study, IoT is identified based on the three main layer that are; Big Data Layer, AI Layer, and Knowledge Network. It is not technically possible to distinguish these three interlocking layers from one another and examine them separately, but it is possible to explain how the characteristics of the IOT are reflected in what layer. In order to do this analysis, based on the literature reviewed, the five main characteristics of IoT are illustrated in Figure 2. ((Tien, 2017; Yadav et al, 2017; Miorandi et al 2012; Patel et al.2009; Banerjee et al.2014; Le-Phuoc et al., 2010); These five characteristics are discussed in following sections based on the related layers 1. Sensing: Sensing is one of the key characteristic of IoT especially work on Big data that represents vast amounts of data (Volume) with huge diversity (Variety) and uncertainty or imprecision (Veracity) that need to analyse while it is being generated (Velocity) in order to produce meaningful decision 175

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Figure 3. Key Characteristics of IoT

(Value). Datasets became so large and complex, it can be obtained from different sources such as Radio Frequency Identification (RFID) sensors, web applications, unstructured data (pictures, documents etc.), radar, navigation sequences, smart watches, Wi-Fi, and telephonic data services etc. (Tien, 2017; Huda et al, 2018; Perry, 2015; Gubbi et al, 2013). In other word, sensing gives an ability to understand around the physical world (Yadav et al, 2017) by using some sensors when an event occurred. This event can be discrete (producing data after an event), continuous (e.g. temperature sensor) (Patel et al.2009), or can be produced by interaction between smart devices (Miorandi et al, 2012). In addition to handle big data, sensing is also important to integrate sensor data together in order to create new knowledge (Banerjee et al., 2014) in the system. Analysis of big data, in the dynamic and complex environment caused the insufficiency of traditional data processing approaches and increase in the use of artificial intelligence techniques. AI techniques have been used for a long time in the process of creating applications in the analysis of non-structural data, classifying or structuring these data, and transforming data into meaningful information. AI techniques gives an ability to users in order to monitoring the systems by smart devices and controlling through Machine to Machine (M2M) communications where data are generated and finally collected (Sadowski et al., 2016). In other words, AI employ intelligent analysis to create value by providing capturing structured interpretations and it is necessary in order to obtain patterns and make timely automated decisions (Gubbi et al., 2013). Some of the new solution approaches that are generated to handle big data are listed below;

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1. 2. 3. 4.

Cloud computing (Cheng et al.,2014) Distributed File Systems (Segatori et al., 2018) Data Mining (C4.5, C5.0,ANNs, DLANNS (Alam et al., 2016) MapReduce (Dean & Ghemawat, 2008).

2. Intelligence: Intelligence is transformation of data into meaningful information in order to get the application of knowledge (Perera et al., 2014) that enables accurate recognition functions, correct thinking and judgment (Ritter et al, 2011) to make or enable context related decisions (Sintef & Norvay, 2014). This characteristic not only to make right decision but also making adaptive and real time decision (Tien, 2017). Sensing, processing, learning and reacting capabilities of AI is used to give an ability to a machine mimics the cognitive functions of a human (Lafrate, 2018). In order to do that, intelligent sensors have been developed such as γ-ray, pressure, biosensor, and X-ray (Li et al., 2015) and various algorithms and computing technologies have been used that make a device intelligent and smart (Yadav et al. (2017) by collecting, providing an intelligence for planning, modelling, and reasoning the context for management and decision making (Perera et al., 2014; Patel et al, 2016). Such as; Expert systems, Machine learning methods based on evolutionary algorithms, Multi Agent Systems, Genetic Algorithms, and Neural networks. 3. Connection: Network accessibility and compatibility are provided through the connectivity. In other words, IoT enable things to be connected that allows the interoperability of IoT devices at any time and in anyplace by using a path/network. (Patel et al, 2016). These characteristics can be accept as foremost requirement of the IoT because the users of the IoT network are devices (De Poorter et al., 2011). Figure 4 summarizes the evolution of connectivity from connecting two computers to interconnected devices. Technologies that are used to satisfy IoT connectivity that make “Internet of Things” applications possible are; Ethernet, WI-FI, Bluetooth, ZigBee, GSM, and GPRS, wireless sensor networks, sensor networks etc. Perera et al. (2014) defined evaluation connectivity from network without the internet to interconnected objects that defines the IoT (see Figure 4)

Figure 4. Evolution of Connectivity (Perera et al., 2014)

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In order to satisfy necessary connection between IoT devices, Khan et al. (2017) defined the connectivity requirements of the IoTs in two groups based on the radio range; 1. Low Power Wide Area Network (LPWAN) connectivity that includes a large number of distributed devices. 2. Low Power Short Range Network (LPSRN) connectivity that includes devices in short range. ıt requires low power and suitable for small networks such as home automation. Today, huge amount of devices with different capabilities are connected in order to support the interaction between the constrained devices. Due to resource constraints such as insufficient wireless bandwidth, power supply or computing capabilities, IoT devices need to standardization of connection in order to have an ability to communicate. Bello and Zeadally (2016) identified the constrained networks as follows; 1. 2. 3. 4. 5. 6.

Short-range wireless network Low-power loss network Delay-tolerant network WSNs: Cellular networks WiMAX

4. Communication: IoT devices can communicate among themselves not only within the same network (intra-domain) but also across heterogeneous networks (inter-domain) (Bello & Zeadally 2013). Communication means that exchange of information between devices in the network that must support devices using different protocol (De Poorter et al., 2011). Common technologies to enable communication between devices are RFID and WiFi. Also Web of Thing (WoT) technology is developed to solve communication problem between IoT devices by using same languages but security and scalability problem is remained unchained (Hakiri et al, 2015). IoT connectivity can be classified into four categories based on interactions that are; 1. Human to Human; Cellular Communication, Web based speaker systems, Blue Tooth Communication etc. (Tien, 2017; Zelenkauskaite et al., 2012, Crockford, 2006) 2. Human to Machine; E-Commerce, World Wide Web, Automatic Teller Machine etc. (Tien, 2017; Shelby et al., 2014; Guinard et al., 2010) 3. Machine to Human; RFID, Radio, Television, Alarm Systems etc. Kushalnagar et al., 2007; Tien 2017; Ishaq et al., 2013) 4. Machine to Machine; Embedded Artificial Intelligence Devices, Drones, Automatic Vehicles etc. (Paganelli et al., 2013; Tien, 2017; Other solutions for the communication of IoT devices are; 1. LinkSmart (Kostelnik et al., 2011) 2. Open IoT (Kim & Lee, 2014) 3. Data Distribution Service (DDS) (OMG, 2015) 178

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Bandyopadhyay& Sen, (2011) defines some of communication issues and parameters need to be taken into consideration as follows; 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.

Deployment Mobility Cost, size, resources, and energy Heterogeneity Communication modality Infrastructure Network topology Coverage Connectivity. Network size Lifetime QoS requirements

4. Interoperability: After the connectivity and communication is utilized, another characteristic must be taken into consideration that is the interoperability of IoT devices in order to make possible cooperation of these devices due to design and communication capabilities of each device. Interoperability basically exchanging information in a way that other IoT devices can use it for improving system performance (Cousin et al., 2015) Vandaele et al. (2015) proposed four main characteristic of interoperability platform; a. Monitor b. Configure c. Automatically install d. Diagnose CPS is one of the important technology that enables the interoperability by satisfying real-time information exchange (Yue et al, 2015). Cousin et al. (2015) provide most common challenges for interoperability as; Integration of multiple data sources, unique ontological point of reference, and P2P (peer to peer) communication. Some existing integration platforms are given in Table 1. Table 1. Some Existing integrating platforms (Vandaele et al., 2015) Platform

Platform installation

Add new technology

Detect new device

Install app

openHAB

Command Line

Config Files + OSGi bundles

Config Files

Config Files

Open Remote

Command Line

Command Line

Manual in GUI

Manual config in GUI

Zodianet

App Store, Sync with web server

Automated

Config via GUI, Partially automated

App Store

HomeOS

Build from source

Manual config in GUI

Our device not detected

App Store

DYAMAND

Start Scrip

Automatic, At Runtime

Automatic Detection

Developer API

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AI on IoT and Technological Advancement on Way of Working With technological advancement, several hugely and positively disruptive changes are taking place and way of working is getting simpler and easier by using automated systems in all aspects of the life. Smart devices equipped with powerful sensing, connectivity, communication, and intelligent data processing capabilities resulted in enhance capabilities of real-time information extraction, data analysis, decision making, and data transmission. Artificial intelligence plays a key role on these technological advancements by giving an ability of cognition to the IoT devices that means increasing intelligence at all stages from the decision of which data to be collected to the analysis to be presented to the user. Three important characteristic features of artificial intelligence are of great importance in increasing the intelligence of the objects, these are perception, cognition and reaction capabilities The use of Artificial Intelligence Technology in the design and application areas of manufacturing systems is gaining more popularity day by day. The intelligence mentioned here is the decision-making mechanism that will find the best solution to a problem. For a system that made up of various functions such as design, planning, monitoring, testing and storage it is necessary to convert these functions into intelligent autonomous systems in order to shortening the time, reducing costs, increasing the competences, and strengthening the control mechanisms. In other words, Artificial Intelligence is intelligent software that enable machine learning and decision-making skills to be gained for solutions to problems. Key Characteristics of AI are (Rzevski,1993); 1. Adaptability: To have the ability to behave in a way that is appropriate and desired in a predefined manner when unforeseen changes in the external environment occur. 2. Self-Maintenance: To have an ability to diagnose its own situation in the face of unexpected changes (errors) and an ability to perform the necessary repairs at operational speed. 3. Communication: To have an ability to connection with other systems it is connected to, and to have an ability to receive or transmit necessary information, control, reporting and calculations. 4. Autonomy: To have a capability (at a certain level) that operates independently of other systems. 5. Learning: To have ability to self-develop based on past experience, other factors, or the in human management when it conducts a particular task. 6. Anticipation: To have an ability to predict changes that may affect the operation it performs. 7. Goal-Seeking: To have an ability to formulate and modify sub-objectives to achieve a defined strategic goal. When the above-described properties are examined, these properties are peculiar to IoT devices. In other words, IoT devices can be defined as artificial intelligence assisted devices. The basic application techniques involved in the development of artificial intelligence technology can be summarized as follows; 1. Knowledge Based Systems: Knowledge-based systems that are defined as If-Then rules in a computer in advance to solve a particular problem. A knowledge-based system may perform close to the logic of a highly informed decision-maker 2. Expert Systems: This area of artificial intelligence is focused on creating high-performance programs in a matter of expertise. (Öz and Baykoç, 2004)

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3. Neural Networks: This method is one of the most widely used technique both for optimizations and classifications. Historical data is used for training in a network that automatically identifies the most appropriate configuration of a hidden network (Mehrotra et al. 1997). 4. Genetic Algorithms: This method uses population genetics to solve complex global optimization problems Today, AI based IoT advancement caused to positively disruptive changes in many areas, some of them are listed in Table 2. Table 2. Some AI-based IoT studies Research Area

Studies

Home monitoring systems for elderly care in their homes and smart hospital applications

Gluhak et al, 2011; Luo et al., 2010; Alemdar & Ersoy, 2010; Pang et al., 2013; Dhariwal & Mehta, 2017; Park et al., (2017)

Describing open collaborative work space using pool of “GenMobile” employees

Venkatesh, 2017; Nothhaft, 2018

Open learning systems that enables active learning for millions of users worldwide

O’Sullivan et al, 2018; Gülbahar, 2017 ; Anshari et al., (2017); Ma, 2017

Using IoT in transportation and rela-time logistics to improve demand response

Conner, 2018; Su et al., 2017; Sreelatha et al., 2018; Pawar & Bhosale, 2017; Atzori et al, 2010; Yue et al, 2010

Supply Chain Controls and Managements

Leng et al., (2018); Accorsi et al., 2017; Majeed & Rupasinghe, 2017; Witkowski, 2017; Pang et al., 2015; Aung & Chang, 2014; Verdouw et al., 2016

Intelligent Vehicle Design and Vehicle Monitoring Systems

Rao, 2017; Wang & Luo, 2017 ; Qin et al, 2013 ; Zhang et al., 2011; Desai & Phadke, 2017; Goyal et al., 2018; Balid et al., 2017; Kuppusamy et al., 2018

Mine safety and energy industries in order to make early warning by using intelligent communications technology

Han, 2017; Qiuping et al, 2011; Jo et al., 2017; Dong et al., 2017; Mohamed et al., 2015; Ma et al., 2016

High-Risk Environment and Risk Management

Montoya et al., 2018 ; Tupa et al., 2017; Ji & Anwen, 2010; Zhang & Yu 2013; Hiromoto et al., 2017; Aung & Chang, 2014

Intelligent manufacturing / Smart Manufacturing applications

Guo & Zhang, 2009; Tao et al; 2014; Chen, 2017; Jeschke et al., 2017; Zheng et al., 2018; Ye et al., 2018

Smart Cities

Rathore et al., 2016; Mohanty et al., 2016; Ejaz et al., 2017; Hui et al., 2017; Montori et al., 2018

As can be seen in Table 2 IoT-based applications that can be define as the connected network between things in order to make enable real-time decision making, are rather widespread in many industries. To be able to satisfy cooperation and coordination among different objects intelligent tools are compulsory in order to get reliable and efficient solutions.

MAIN CHALLENGES OF IOT Based on the literature examined in this study, several issues and parameters should be taken into consideration for the implementation of the IoT architecture; some of them are listed below

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1. Internet: Internet is one of the key parameter for the communication and interoperability 2. Standardization: Different IoT devices with different communication ability caused to lack of interoperability so there is a need a standardize architecture that includes mobility, availability, manageability and scalability for easy and natural information exchange. 3. Scalability: Every day more and more IoT devices will be connected to the network so data processing and data transfer will become more complex. 4. Security and Privacy: Controlling of information security and data privacy protection also another issue for IoT devices. 5. Energy Saving: IoT devices needs to be connected to a power supply so sufficient energy source is needed to sustainable device smartness. 6. Heterogeneous Network: It includes managing heterogeneous applications and devices in a network and it is accepted as a major challenge for many authors. IoT has become one of the most popular technological advancement of business today. Every production or service systems require intelligent solutions in order to get the most economically feasible solutions for their systems. For analysing, interpreting and forming solutions to the information generated by multiple sources in a distributed environment, it is necessary to take into consideration the 6 basic challenges described above.

DISCUSSIONS AND CONCLUSION This chapter provides a comprehensive overview on the introduction of IoT and its’ key characteristics. In addition to given introduction, IoT applications are listed based on each characteristics in section 2. Role of AI on IoT and applications generated based on AI is stated in section 3 and main challenges in order to create an IoT infrastructure are listed in section 4. Main motivation of this study is to provide a good basis for researchers and practitioners who want to learn about the components of the IoT and the general architecture of communication and collaboration protocols. In addition, current IoT applications and fundamental issues are discussed in the interaction of large data analytics, artificial intelligence technology and network infrastructure. Based on this study, following conclusions can be made; 1. Huge amount of devices with different capabilities are connected in order to support the interaction between the constrained devices so there is a strong need for a standard architecture in order to satisfy connection between these IoT devices. 2. In order to monitoring the systems by smart devices and controlling through Machine to Machine (M2M) communications there is a strong need for a standard architecture in order to satisfy communication between these IoT devices. 3. Various algorithms and computing technologies have been used in order to increase level of intelligence however especially concept of deep learning one of the key important issues for IoT devices. 4. Cyber Physical Systems is one of the most important technology that enables the interoperability by satisfying real-time information exchange so there is a strong need for a standard architecture in order to satisfy P2P (peer to peer) communication.

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This research was previously published in Advanced Metaheuristic Methods in Big Data Retrieval and Analytics; pages 1-26, copyright year 2019 by Engineering Science Reference (an imprint of IGI Global).

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Chapter 11

Industry 4.0 and Supply Chain Management: A Methodological Review

Pavitra Dhamija Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa Monica Bedi Panjab University, Chandigarh, India M.L. Gupta PEC University of Technology, Chandigarh, India

ABSTRACT The association of Industry 4.0 and supply chain management assures tremendous growth and developmental opportunities towards manufacturing organizations. The two aspects (Industry 4.0 and supply chain management) are one of the most opted choices for research among academicians and researchers. The study in question accommodates 884 papers from past 10 years, which contributes towards Industry 4.0, supply chain management, cyber-physical systems, digitization, Internet of Things, and Big Data predictive analytics. The statistical tools include BibExcel and Gephi for bibliometric and network analysis. The results are presented in the form of top contributing authors, keywords, and citations. The article also shares a conceptual model based on the review of studies. The findings will help managers or officials to understand the importance of Industry 4.0 and its association with supply chain management. The formed clusters and their associations are providing new areas that require managerial attention. The article ends while discussing the current and future scope of research.

DOI: 10.4018/978-1-7998-8548-1.ch011

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 Industry 4.0 and Supply Chain Management

1. INTRODUCTION Industry 4.0 came into notice in the year 2011 at the Hannover Fair (Germany), and received a lot of concentration from academicians and officials (Dezi et al., 2018). The term fourth industrial revolution is a commonly used twin term for Industry 4.0 (I4). Kagermann (2015) explains it as a contemporary move enabling automation to exchange data in manufacturing organizations. The continuous developments in the area of science and technology are supporting the virtual growth and enhancements across manufacturing set-ups (Belvedere and Grando, 2017). The involvement of few other aspects not limited to internet of things (IoT), big data predictive analytics (BDPA), cloud computing (CC), and cyber physical systems (CPS) makes I4 a complete concept, also known as smart factory (Bag, 2017; Gunasekaran et al., 2017; Papadopoulos et al., 2017; Wamba et al., 2017). In addition, it is realised that I4 and CC, when put together, deliver the most beneficial outcomes with respect to information technology in manufacturing organizations (Fu et al., 2018). The role of CPS is to monitor all physical operations and produce a soft copy of every performed operation (Brettel et al., 2014; Gunasekaran et al, 2018), so that, organizations are able to make rational decisions. The IoT linked with CPS interact and coordinate amongst themselves and with humans in real time zone through online facilities (Wang et al., 2016). This process further smoothens the internal organizational activities carried out through virtual methods (Hermann et al., 2016). The ongoing researches reflects that I4 it is the next level in manufacturing industry with digitization (DGT) as its key driver (Shrouf et al., 2014), followed by certain interruptions; (a) an incredible rise in data, (b) the power of computation, (c) network connectivity, (d) involvement of business analytics and business intelligence, and (e) human robotics (Lee et al., 2015). The implementation of I4 in manufacturing set-ups impacts the overall supply chain management (SCM) (Stock and Seliger, 2016) (refer to Figure 1). The collaborative activities of manufacturers, retailers, customers, and suppliers require transparency. The process of DGT and automation of different processes in SCM has changed the work patterns for record maintenance and delivery of services (Fu et al., 2018). In order to gauge the possible opportunities and expected threats, it is highly essential to understand the existing stage of association between I4 and SCM. Given the above background, the present paper is a modest attempt to review and assess the current situation of I4 and SCM while considering major constituents i.e. DGT, IoT, CPS, BDPA. This study focuses to address the following research questions: RQ1: What is the relationship between I4 and SCM? RQ2: Is there any contribution towards I4 and SCM, when and where? RQ3: What are the existing trends and future directions towards I4 and SCM? The present study uses bibliometric analysis to assess a sum of 884 selected papers. The following sections will present the review of literature, analysis, discussion, limitations, and future directions.

2. REVIEW OF LITERATURE This section discusses the review of literature in two sub-sections i.e. Industry 4.0 and Supply Chain Management.

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Figure 1. Industry 4.0 levers

2.1 Industry 4.0 The incorporation of automation in manufacturing-oriented processes not only enhances quality of the produced product, it makes production process more smooth and effective (Landscheidt et al., 2017). The overall change to manage work in manufacturing organizations is the result of I4 or fourth industrial revolution (Lasi et al., 2014). The advent of I4 has not happened suddenly, rather it took years for economies across the world to reach this level of BDPA and DGT (refer to Figure 2) (Bellandi & Propris, 2017). The present scenario of cutthroat competition and interdependence for the exchange of data is forcing organizations to adopt and follow a digitized framework to meet out the needs of customers and suppliers (Aquilani et al., 2016). According to Kolberg et al. (2016), the existing practices of I4 and the way it is associated with other elements of production, brings a big question of its global acceptance. The genuine consideration of the fact that everything comes with a cost makes it essential to explore the

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price paid by the world to receive benefits of I4. Qin et al. (2016) discusses four basic elements of I4 viz; (a) rational factory in which all data or information is stored for some reason with its independent and autonomous existence, (b) business oriented activity involving an interaction between suppliers, manufacturers, and customers based on real time basis to eliminate aspects like pollution, (c) digitized products that exchange information through sensors and, (d) the end user who requires information in the form that it can be altered or personalised as per their future needs.

2.2 Supply Chain Management The area of SCM in not new to understand. In fact, it is one of the oldest concepts applicable in the manufacturing organizations (Hugos, 2018). Several researchers considered SCM as the key variable in their respective studies (Muralidhar & Sarathy, 2018). Subramanian et al. (2018) explain SCM as the process of transportation and distribution of goods at desired destinations. According to Kasemsap (2017), it is an activity related to the procurement of raw materials and converting them to the final products for the end users. This process is extremely dependent upon the collaborative activities of various mediators and suppliers (Faisal, 2015; Okdinawati et al., 2017). SCM is the strategic activity involving different business-oriented activities with an aim to perform and improve logistics services for the betterment of society at large (Boyonas et al., 2016). Figure 2. Industry 4.0: A journey from first to fourth industrial revolution

It is pertinent to mention that advent of technological advancements at large and I4 in particular; the concept of SCM evidences a revolutionary development. The most important part is to understand SCM from digital context. An organization headquartered at New Jersey, familiar as Supply Chain Wizard, presents the relationship between DGT and SCM (refer to Figure 3) in the form of a platform comprising of six elements viz; (1) digital supply chain involves end to end activity of the supply chain in a

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digitized format with everything recorded, monitored, summarized in digital form, (2) digital factory covers different processes from the stage of manufacturing in factory till its packaging to unveil deviations, and improve offline factory operations, (3) digital warehouse forms cross functional teams with specific reference to warehousing activities, (4) digital transport monitors sales and marketing services on real time basis and tries to relate all operations with GPS systems, (5) digital retail envisages the allocation of available employees, staff positioning along with a estimation of visiting customers and making arrangements accordingly, and (6) digital projects for overall management of different programs while dealing with business partners of the organization. Figure 3. Supply chain management from the perspective of Industry 4.0

The above discussion justifies the existence of I4 and SCM as individual concepts, which initiates a reason to study I4 and SCM as combined topic. Hence, the present study postulates a conceptual model while considering already framed research questions for I4 and SCM (refer to Figure 8). The subsequent section discusses research methodology of the paper.

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3. RESEARCH METHODOLOGY AND DATA STATISTICS The purpose of research methodology is to provide direction to the research and to answer the selected research questions with respect to defined gaps. The existing body of knowledge with respect to I4 and SCM provides a rational piece of work, but none of the considered studies applied advanced tools like bibliometric and network analysis. The research team implemented the aforementioned tools for analysis to derive meaningful inferences in the form of contributing authors, keywords, affiliations etc. The selection of appropriate key words is one of the crucial tasks towards a well-directed review process (Tatham et al., 2017). A proper sequence of steps for a systematic review of literature includes inspection and dissection of documents, gathering observations, grouping of selected documents (articles), drafting of articles, and finally summarising studies. In parallel to above sequence, the present research also follows the same steps to identify the important articles and puts light on the existing areas of research under the umbrella of I4 and SCM for the forthcoming researches.

3.1 Defining Keywords The thorough collection of data is possible only with appropriate selection of keywords. The present study categorizes keywords as primary and secondary. The selected keywords include Industry 4.0, Internet of Things, Digitization, Cyber Physical Systems, Big Data Predictive Analytics, and Supply Chain Management. The different keyword combinations include: 1. 2. 3. 4. 5.

Industry 4.0 AND Supply Chain Management Internet of Things AND Supply Chain Management Digitization AND Supply Chain Management Cyber Physical Systems AND Supply Chain Management Big Data Predictive Analytics AND Supply Chain Management

3.2 Search Results with Selected Keywords The present study uses data extracted from Scopus database. Scopus provides largest collection of different items (70 million, as per the information available on the Scopus website on September 22, 2018) not limited to articles, articles in press, conference papers/proceedings, and books. This database contains data published by reputed publication houses viz; Elsevier, Emerald, Springer, Taylor and Francis. These journals offer articles/research papers from a variety of subject areas (social sciences, information systems, business management, accounting, medicine) (Fahimnia et al. 2013). The search criteria cover ‘title, abstract, and keywords’; except the material printed in the form of books, conference papers/ proceedings, book reviews. In the initial search, research team received 15670 articles (refer to Table 1 for distribution pattern) that possess information related to authors, keywords, affiliations, and year. The results received from initial search undergo various filters. Firstly, the research team selects articles and articles in press, published in journals in English language. After which, a number of 1296 left papers are screened (refer to Table 1). Secondly, with the application of filters for number of years (2009-2018) and English language, the research team receives a figure of 1200 articles. Finally, the last filter excludes articles without DOI and receives 884 articles for investigation (RIS format).

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Table 1. Keyword search results: I4 and SCM

3.3 Initial Data Statistics Research team acknowledges the segregation of published papers in the subject area of I4, IoT, DGT, CPS, and BDPA with reference to top 20 journals (refer to Table 2). Overall, 884 papers are published in these as well as other journals. Table below shares data for last 10 years (2009-2018). It also demonstrates year-wise publication of papers for top 20 journals. The used abbreviations are elaborated in the end (refer to Table 10) of the paper. Table 2. Journal-wise publication: I4 and SCM

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The figures are presented graphically (refer to Figure 4) reflecting change in trends. The publications are highest in 2011, followed by a downfall from 2012 to 2016. The trend again became strong in 2017. The journals that majorly contributed include Journal of Technological Forecasting and Social Change, Journal of Cleaner Production, International Journal of Production Research, and Journal of Industrial Management and Data Systems. Figure 4. Distribution trend of published articles: I4 and SCM

4. DATA ANALYSIS AND INTERPRETATION The study follows citation analysis by using ‘bibliometric analysis’ and ‘network analysis’. The former is carried out by using BibExcel software. The received results are in the form of top-rated authors, affiliations, and keywords. This software can be very well matched with other software and applications for further analysis (Excel, Gephi, and pajek) (Persson et al., 2015). The process of network analysis is a continuation while considering files received after using BibExcel. The extracted files deliver number of citations while using Gephi. The most advantageous aspect is Gephi provides visualizations in different forms. The paper also presents a conceptual analysis.

4.1 Bibliometric Analysis In general, bibliometric analysis uses different available software not limited to Publish or Perish and HistCite, and BibExcel having advantages and disadvantages. Out of all, BibExcel is most versatile to analyze large volumes of data with its further conversion into RIS format for network analysis.

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4.1.1 Author Influence The BibExcel software delivers a list of top 10 influential authors in the field of I4 and SCM. The figures provided in Table 3 represents number of articles published by each respective author mentioned in the list. Over the last 10 years, Aleksy, Bonnard, Law, Cheung, Mourtzis, and Sung produced the highest number of papers for the area of I4 and SCM and its related aspects. Table 3. Top 10 authors: I4 and SCM

4.1.2 Affiliation Statistics With respect to the country-wise affiliation statistics (refer to Table 4), data is extracted by applying BibExcel. It provides top 10 countries along with number of published articles. Table 4. Top 10 countries: I4 and SCM

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The United States, United Kingdom, China, Germany, and Italy followed by the rest grabs the top most positions. The research team concludes that the concepts of I4 and SCM are the buzzing areas for research.

4.1.3 Keyword Statistics The keywords serve a base to create a research idea for particular area. It is nonetheless pertinent to mention that for studies based on systematic literature review, selection of keywords becomes even more crucial. The table below shares the most commonly used keywords for the area of I4 and SCM (refer to Table 5). Table 5. Top 10 keywords: I4 and SCM

Another aspect is the frequent occurrence of keywords (refer to Table 6). If compared, the most commonly used keywords are internet, Industry 4.0, supply, and information. Table 6. Top 10 commonly used words: I4 and SCM

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4.2 Network Analysis With reference to network analysis, the paper uses Gephi for analysis. The most commendable part is its visualizations. It provides an immense edge towards data presentation (Mishra et al., 2016). The BibExcel converts Scopus data into .RIS file format, followed by .NET format, and finally transferred to Gephi to receive results in the form of citation analysis, co-citation analysis, and lastly PageRank analysis. Further, the received figure represents papers as nodes and citations as arcs in between nodes.

4.2.1 Citation Analysis The first section under network analysis is citation analysis. In this step, selected tool calculates number of citations of all contributing authors of journal ranking. It helps to measure the frequency of cited papers (Meredith and Pilkington, 2018). Figure 5. Citation analysis of top 10 authors: I4 and SCM

Table 7 and Figure 5 illustrates the list of top 10 authors based on citation analysis for last 10 years. Ilic (2010) is at the topmost position with 123 citations in the field of I4 and SCM and its related aspects (IoT, DGT, CPS, BDPA). The next in the list is Tanackovic and Badurina (2009) with 112 citations, trailed by Aleksy et al. (2011), Brandenburg et al. (2014), Calderoni et al. (2012) as the influential authors with 99, 92, 85 citations respectively. The mentioned authors share opinions, both similar and dissimilar with respect to I4 and SCM and its relationship with other aspects covering mobility, business integration, CPS, industrial internet of things to list a few.

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Table 7. Top 10 authors based on citation analysis: I4 and SCM

4.2.2 PageRank Analysis Citation analysis helps us to understand the significance of a published paper. It substantiates the existence of different measures based on citations used by other journals of repute. The measuring of inter-correlated citation matrix is a complicated task. Langville et al. (2008) identifies the technique of PageRank analysis. This method facilitates researchers to gauge the prominence of a paper as per its keywords (Hermann et al. 2016; Mishra et al. 2016). The subsequent section explains this technique. PageRank is based on assumption that different authors have cited paper B (T1, …, Tn). The PageRank equation of paper B i.e. PR (B) is as under: PR (B ) =

(1 − d ) N

 PR (T ) PR (Tn )  1  + d  +…+  C (T C (Tn )  1) 

The meaning of different elements of this equation includes d (the damping factor), which denotes a portion of random citations and its value stays between 0 and 1. The next element is C (T1), which means the citation counts of other papers by paper T1. In the aforementioned formula, B denotes the papers under consideration and N represents the total number of papers. Further, if C (T1) = 0, then PR (T1) is divided into other papers in place of C (T1). The value of 0.85 and 0.5 stands acceptable for the concept of damping factor (d). Table 8 provides the PageRank analysis. After comparing Table 3 and Table 8, it is concluded that the authors who topped the list in Table 3 and 8, are no longer visible in Table 9. Instead, Xue et al. (2013) has highest number of citations, followed by Bendavid and Cassivi (2010), McWhorter (2014), and Kerrigan and Graham (2010). The Brandenburg et al. (2014) and Karnouskos (2012) could not make any position in PageRank analysis. The attributable reason for the same can be the change in the area of interest of the authors as Industry 4.0 is researchable while combining it with various other variables in different industries.

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Table 8. Top 10 papers based on page rank analysis: I4 and SCM

4.2.3 Co-citation Analysis The technique of citation analysis helps to understand contemporary existence of a topic. Along with the citation analysis, co-citation analysis assists the connection of keywords, authors, and journals with each other. It produces results with respect to publications contributed by same author in variety of journals. The co-citation analysis extends various benefits i.e. it explains relationship between authors from societal perspective and with respect to journal publications. The two steps required to complete this process are (1) the .NET file of 884 papers is put into the Gephi software, where a random map is generated with no accessible information, (2) the created file is converted into a ‘Force Atlas’ layout to receive an inter-connected figure of co-cited papers. The Gephi software provides a wide variety of layouts with two important parts i.e. nodes and edges. Out of all available layouts, ‘Force Atlas’ is the most familiar procedure. Once the tool is applied, it delivers a sketch in which nodes are connected with each other whereas; the edges resist each other. The resistance cannot be adjusted manually. The nodes, which are closely connected, are at the center of the figure, and the nodes with less association rests at the borders of the figure, also called as outliers. The isolated nodes do not become a part of data clustering. Eventually, after the removal of outliers, a layout with 88 nodes and 521 edges is received (refer to Figure 6). 4.2.3.1 Data Clustering The process of clustering denotes bringing together the elements with alike characteristics. Accordingly, the present paper reflects the clustering of papers of similar domains (refer to Figure 7). The process starts when nodes follow a pattern to recognize the dense linked nodes as compared to less connected nodes in one cluster. The Gephi software considers modularity to measure density. The standard values of modularity index range between −1 and +1 and are used extensively in other researches (Mishra et al. 2016). The formula below represents a modularity index:

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Q=

1 2m

 ki k j   A − ∑  ij 2m  δ(ci ,c j ) ij

Where; Aij means the edges weight between nodes i & j; ki denotes the sum of edges weight attached to node i as ki= ∑ j Aij; ci belongs to the arm of vertex i; δ (u, v) equals to 1 if u = v&0; m = (1/2) ∑ij Aij The above formula identifies nodes for 884 papers. Post that, a set of five clusters with modularity index value of 0.169 is received. This shows strong relationship between clusters along with variation amongst them (Mishra et al. 2016). The existing body of literature provides information that jointly cited papers generally belong to same subject area. Accordingly, the non-jointly cited papers, which share the same cluster, are the ones with strong co-citation relationship. The set of papers in each cluster represents common interest area of the authors. The PageRank analysis for the present research identifies a figure for each cluster (Mishra et al. 2016). The list of top ten papers (contributing authors) from each of five clusters is shared below (refer to Table 9). Figure 6. Force Atlas layout of 884 connected nodes: I4 and SCM

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Figure 7. Structure of five clusters: I4 and SCM

5. DISCUSSION 5.1 Cluster Analysis The analysis predicts that in first cluster, major thrust of authors (refer to Table 9) is on I4, IoT, and security. The discussion revolved around the conceptual aspect of I4. It also discusses about the association between I4, IoT, and security. The contemporary meaning of technology is internet, which is never malicious free. Although, organizations take this aspect into account and promise to have full proof security for every activity-involving internet, but in reality, to maintain a high level of security is not an easy task. The future researches can include this aspect with connection to I4 and SCM. The second cluster is based on IoT, manufacture, and standards. It reveals the foundation stage for IoT, manufacturing business, capabilities, services and applications. The published articles mentioned in this cluster associate themselves with the existing manufacturing activities. The important theme emerged from this cluster is architectural concept. The success of manufacturing processes is extremely dependent on architectural base of every organization and must be considered effectively. In the next cluster i.e. cluster three, papers related to I4 and SCM are presented. This cluster provides a bigger view of I4 while combining it with the SCM. It highlights an interesting association, which needs to be explored further. In addition, the incorporation

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Table 9. Proposed cluster classification: I4 and SCM

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of concepts like smart logistics or logistics 4.0 is highly recommended. CPS is one of the main constituents of I4. The fourth cluster talks about CPS and information management. This combination also serves an important aspect in contemporary era. It explains the importance of CPS and its importance for information management. The research team analyzes the status of this cluster and realizes that the aspect of security is missing in papers that belong to this cluster. As discussed earlier, security forms the heart of I4, without which this concept will become redundant. Lastly, the fifth cluster consists of a very important aspect. It discusses the connection between DGT and decision-making. This cluster explains about different services, which can be enabled in the electronic form and its usage in decision-making. DGT is a small word with big meaning. It involves a lot of juggling in the mind of the policy makers and decision makers to take decisions related to important matters that involve DGT. Today, the world is revolving around technology, and DGT is its core aspect. DGT becomes more crucial if connected with emerging nations. Accordingly, this theme needs to be researched further. Finally, this cluster serves as one of the most essential cluster among all discussed above, followed by clusters one, two, three, and four. The proposed outline for future research with respect to the selected topic is shared below (refer to Figure 8). It paves the way for future researchers. The order of clusters for present paper started with I4, IoT, and security (cluster 1), followed by IoT, manufacture, and standards (cluster 2), I4 and SCM (cluster 3), CPS and information management (cluster 4), and DGT and decision-making (cluster 5).

5.2 Conceptual Analysis While keeping in mind the aforementioned review of studies, the research team identifies a conceptual model (refer to Figure 8) based on different associations. Based on the systematic analysis of 884 studies by applying BibExcel and Gephi as statistical tools, the realized associations include Internet of Things and Supply Chain Management, Digitization and Supply Chain Management, Cyber Physical Systems and Supply Chain Management, and Big Data Predictive Analytics and Supply Chain Management. The mentioned relationships are definitely leading a direction for the advance research in these areas by future researchers. The analysis also confirms that academicians and researchers in this domain are carrying out research. Further, the existence of trends for future studies and research is also possible. With this, the research team answers the framed research questions.

6. CONCLUSION Comprehensively, this paper significantly contributes to the existing body of literature. The concept of this paper revolves around Industry 4.0 and supply chain management. The research team proceeded with the collection of data in the form of articles published in the journal of repute from the last 10 years of time. Only Scopus data is considered. The selection of keywords that forms the base of the data collection includes industry 4.0, supply chain management, cyber physical systems, digitization, internet of things, and big data predictive analytics. The used tools (BibExcel and Gephi) provides extensive insights with respect to Industry 4.0 and supply chain management. It extends a clear picture contribution by researchers with future direction. The research team has wisely dissected the paper into various sections. With respect to contribution, the paper provides significant value addition to the existing body of knowledge. It enables a clear understanding of Industry 4.0 and supply chain management. The research team identifies different clusters with its connection with the stated conceptual model. The results received from the 208

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Figure 8. Conceptual model: I4 and SCM

analysis, when interpreted, delivers meaningful insights in terms of various associations not limited to internet of things with manufacture and standards, cyber physical systems with information management. The conceptual structure based on research questions is a notable contribution by research team. The framed model signifies the positive relationship between internet of things and supply chain management, digitization and supply chain management, cyber physical systems and supply chain management, and big data predictive analytics and supply chain management. The positive association assures the acceptability of stated combinations. It also conveys that organizations are adopting these concepts along with their practical implementation, which is again extremely important. The most important aspect is to understand the requirement of Industry 4.0 and its allied areas to sustain the contemporary competition across the globe. Another worthwhile information to share is that the research team decides to research Industry 4.0 in phases while trying different combinations. The present study is the first phase, followed by few more phases to come. In the next phase, the association of Industry 4.0 with logistics, business

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integration, mobility, virtual reality, industrial internet of things and other related areas of supply chain management will deliver valuable information for the logistic oriented organizations. The study confirms the wide popularity of Industry 4.0 and supply chain management among researchers and practitioners in the last 10 years of time. The next sub-section discusses implications and contributions, followed by limitations of the present study.

6.1 Theoretical Contributions The present study extends a meaningful piece of work exclusively for Industry 4.0 and supply chain management. This study is not only a systematic review of literature, but implements bibliometric and network analysis with two statistical tools. The results in the shape of citation analysis, co-citation analysis, and PageRank evaluation, reveals a significant contribution by author(s) in the selected field of research. The findings deliver five clusters; ‘Industry 4.0, Internet of Things, and Security’, ‘Internet of Things, Manufacture, and Standards’, ‘Industry 4.0 and Supply Chain Management’, ‘Cyber Physical Systems and Information Management’, and ‘Digitization and Decision Making’ providing a strong background for future researches. The formation of clusters also confirms their association with each other. The concept of Industry 4.0 is one of the most burning areas of technology and needs more research by taking into account one or the other parameter (Brettel et al., 2014; Faisal, 2015; Hermann et al., 2016; Okdinawati et al., 2017; Gunasekaran et al, 2018). Accordingly, the research team selected supply chain management as a combined variable. The contribution in the form of various clusters is the novelty of this research. Looking at the sensitivity of the selected topic and the way it is penetrating into our industry, it requires more investigation and attention.

6.2 Managerial Contributions The Industry 4.0 and supply chain management are the core concepts for manufacturing sector or industry. A high number of firms either have adopted or are trying to adopt the concept of digitization. The findings of present study will definitely serve a knowledge base for the managers of concerned organizations. It will help managers or officials to understand the importance of Industry 4.0 and its association with supply chain management. The formed clusters and their associations with each other initiate new areas, which requires managerial attention. If implemented attentively, provided insights can extend significant benefits to the organizations. Next, it is very much essential for managers to understand the existing status of Industry 4.0 and supply chain management in each cluster. The shared recommendations will provide managers with future directions to take decisions. It is also advisable to managers that do not overlook the negative aspects of Industry 4.0 and supply chain management. With the upcoming of Industry 4.0, the process of supply chain management has received extreme benefits, but its acceptance is still a big question to address. The adverse effects of this association require observation.

6.3 Limitations and Future Research Directions of the Study The selection of keywords is at the discretion of the researcher. Although, the selection is exhaustive, yet it can include other keywords depending upon the objective of study. The tools used are justifiable, but other tools are also available which can produce different results. The number of clusters might differ with the selection of different tools or keywords. 210

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Table 10. Abbreviations of journal titles

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Development and Design Methodologies

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Chapter 12

Design of Facility Layout for Industry 4.0 Mirko Ficko https://orcid.org/0000-0003-0903-0655 University of Maribor, Slovenia Lucijano Berus University of Maribor, Slovenia Iztok Palčič University of Maribor, Slovenia Simon Klančnik University of Maribor, Slovenia

ABSTRACT The concept of Industry 4.0 combines a large number of fully or partly autonomous devices and humans into a system, which is, due to synergies, more flexible and effective than a fully automated system. To reach this objective we have to ensure such facility layout that ensures an efficient transport system which takes into account the large number of participants. The most important design goal is to minimise the path travelled by transport devices. In the case of Industry 4.0 we have two altered conditions: the number of devices (machines, workplaces, storages) connected in one system is substantially larger, and autonomous transport devices and humans have different organisational needs regarding the facility layout. This chapter presents a highly efficient method for preparation of layouts that is based on simplified space and the physical appearance of the system. The design of transportation paths will be subjected to finding the optimum layout of devices to lower transportation costs.

DOI: 10.4018/978-1-7998-8548-1.ch012

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 Design of Facility Layout for Industry 4.0

INTRODUCTION The floor, or Facility Layout Problem (FLP) has been studied widely for decades; in general, it is defined as a search for the most efficient arrangement of objects on the plane. The general definition of a floor layout is the determination of relative locations for, and the allocation of, available space among a number of workstations (Azadivar & Wang, 2000; Ripon, Glette, Khan, Hovin, & Torresen, 2013). The Facility Layout Problem is a common variant of the general definition of a problem which addresses the problem of placement of devices in the facility for industrial purposes. Kusiak and Heragu, in this manner, used the term facility to represent a machine, workstation, inspection-station, washing-station, locker-room, rest area, or any manufacturing or support facility (Kusiak & Heragu, 1987). This chapter addresses the problem of design of facilities in the circumstances of Industry 4.0, where an altered approach is needed to this problem; therefore, the abbreviation FLP is meant strictly as a Facility Layout Problem. The motivation for research of facility layout design in Industry4.0 is economical, technical and organisational; there are multiple goals which have to be met in practice. Because of the integration of a large number of (partly) autonomous devices based on IIoT, the FLP evolves a larger problem with new constraints, or better said without traditional constraints. The layout of a facility plays a very important role regarding its effectiveness. Layout defines the production/manufacturing system regarding the operating of devices, organisation of services and transportation of workpieces. In practice, a highly efficient operation relies on short and simple transport paths without bottlenecks. The goal of this paper is to prepare a facility layout which is tailored to the system`s main purpose, and not to a human view of perfect organisation. Motivation for our research was made on a presumption; in the facilities of a new generation, which consists of a large number of heterogeneous devices, it is possible to obtain a better layout with the omission of the predefined general shape of the manufacturing system. This paper discusses the search for a near optimal layout of devices in a facility according to the concepts of Industry 4.0. After the Introduction, the second section of the paper deals with background of FLP, the third section presents the FLP in Industry 4.0. The fourth section presents a highly efficient method of solving of FLP based on simplified space presentation. The fifth section deals with discretised space representation and the search for a layout without any pre-defined shape. The sixth section presents an idea of a system for facility design, which is based on ideas presented in the previous two chapters. A discussion of the findings follows.

BACKGROUND FLP is one of the most important problems in the literature of production management and industrial engineering, attracting the attention of many researchers in the field of Static and Dynamic Layouts (Hosseini-Nasab, Fereidouni, Fatemi Ghomi, & Fakhrzad, 2018). The FLP is concerned primarily with finding an optimum arrangement of a set of facilities in any layout, subject to certain qualitative or quantitative constraints. The FLPs, like most facility design and planning problems, are computationally non-polynomially difficult (Islier, 1998). The definition of FLP can depend on: •

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Space presentation. Modern researches try to solve the problem on the level of an actual layout, together with the design of transport paths, and take many constraints into account (shape and size

 Design of Facility Layout for Industry 4.0

• •

of the available space, minimal distances between neighbouring devices, position of infrastructure, etc.). Cost function definition. Most researches deal with Material Handling Cost (MHC). Static or dynamic material flow.

The FLP is an optimisation problem that tries to make layouts more efficient by taking into account various interactions between facilities and material-handling systems whilst designing layouts (Hammadi et al., 2011; Shayan & Chittilappilly, 2004). Sule and Islier indicated that approximately 30–75% of a product’s cost could be attributed to material handling and transportation (Islier, 1998; Sule, 1994). An effective layout could reduce costs considerably, including material-handling and transportation costs. Kusiak and Heragu used the term “facility” to represent a machine, workstation, inspection-station, washing-station, locker-room, rest area, or any manufacturing or support facility (Sunderesh S. Heragu, 2008). The FLP is concerned primarily with finding an optimum arrangement of a set of facilities in any layout, subject to certain qualitative or quantitative constraints. The FLPs, like most facility design and planning problems, are computationally non-polynomial difficult (Shayan & Chittilappilly, 2004). Various computer techniques have been developed to support FLP optimisation. Decision-making is a very important process present in various stages of product design (Kaljun & Dolsak, 2012) and manufacturing, often supported with intelligent algorithms, whose foundation is human cognition of a particular field (Sancin, Dobravc, & Dolsak, 2010). FLP optimisation is even more important in the production environments served by robots, because the complexity of their tasks requires complex robots, thus increasing their potential for failures (M Anand, Selvaraj, & Kumanan, 2012). Evolutionary Computation and Swarm Intelligence have shown interesting potential in many different fields, one of them being the Design and Organisation of Manufacturing Systems. The optimisation strategies used, based on a population of solutions and often inspired by nature, have led to an increasing number of researchers publishing articles that address several types of problems encountered within the area of Manufacturing Systems. The designs, configurations and operations of manufacturing systems often involve complex decisions, where intelligent methods can play an important role. To cope with this type of problems, intelligent techniques have been used, such as expert systems (Ahmad, Basir, Hassanein, & Azam, 2008), fuzzy logic (Menon, Zwimpfer, Hanne, & Dornberger, 2015), Swarm Intelligence (Ficko et al., 2010), neural networks (Tsuchiya, Bharitkar, & Takefuji, 1996) and genetic algorithms (Pierreval, Caux, Paris, & Viguier, 2003).

DESIGN OF FACILITY LAYOUT FOR INDUSTRY 4.0 FLP and Industry 4.0 This chapter deals with the design of production systems for Industry 4.0, where intelligent automation and human beings are working side by side. The main objective is to construct a system for optimal device layout in highly automated production systems with transport path design, which incorporates humans and automated/autonomous transport means. During recent decades, the overall research goal regarding FLP remained the same, but the complexity of the problems has increased; in the global competitive economy, the importance of optimal use of resources has increased, and the complexity of production systems is increasingly higher. Development of autonomous transport in factories changed 221

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the facility layout problem. Autonomous transport in the concept of Industry 4.0 must work alongside the human workforce. In today’s competitive scenario, every industry strives to become Industry 4.0 (Kumar, Singh, & Lamba, 2018). Industry 4.0 focuses on precise and accurate manufacturing by establishing intelligent processes (Wang, Wan, Li, & Zhang, 2016); therefore, designing a robust layout is essential in this frequently changing environment. The evolution of machine tools` hardware, software and serving subsystems, has brought new highly integrated machines to customers, capable of interconnecting with other machines. The robots have become inexpensive and reliable enough to replace manipulators; machine tool producers have started to include robotic load/unload systems in the machine tools. Transportation systems are evolving into robotic transport systems guided by their own intelligence. In the case of physically large facilities, the Automated Guided Vehicles from the past evolved to Autonomous Guided Vehicles (AGV) which have proved to be efficient and very flexible. Compared to conveyors, AGVs are more flexible in delivering parts to different locations through alternative routes (Ho, 2000). Because of their routing flexibility, they are often applied in manufacturing environments; during small to medium sized type production AGVs have proved to be the most flexible; the number of used AGVs could be varied on demand, and there is the possibility of using different transport routes. Despite the fact that the transport means changed over time, the general shapes of facility layouts stayed the same (Figure 1). If we look at the reasons for typical layouts of a facility (in one row, multiple rows, in a loop, and in branches), we can conclude that these basic shapes often exist because of technical reasons (Satheesh Kumar, 2008). They proved efficient, but in Industry 4.0 they have become obsolete, and are here mostly because of humans. We humans are often simplifying shapes, mainly because of our view of the best organisation, this is also true in the case when laying-out production systems. Generally used shapes for design of FLP include straight lines and rectangular laid paths, as shown in Figure 1. Figure 1. Examples of typical layout shapes

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A system having a simple form can be conceived, built, controlled and maintained more easily. In the case of autonomous guided transportation, this means free movement in the space, with the limitations of the human mind disappearing, and the layout need not be simplified any more. Without these constraints we can build better facilities, better adapted to the problem, with lower operating costs, and fewer bottlenecks (Ficko et al., 2010). Control and transport systems are advanced enough; they do not need layouts built on predefined general shapes. In Figure 2, we can see a layout which looks chaotic at first glance, but can serve its purpose perfectly. In living nature there are a lot of systems which look chaotic, and only in-depth research shows us that they are adapted for a specific purpose. Such layout is difficult to navigate for humans. This type of layout is called an Open-Field Layout Problem (OFLP). It corresponds to situations where facilities can be placed without the restrictions or constraints that would be induced by such arrangements as single row or loop layout (Yang, Peters, & Tu, 2005). The most prominent limitation of designing an Open-Field Layout is the non-overlapping constraints of the model that force the facilities. (Niroomand, Hadi-Vencheh, Sahin, & Vizvari, 2015) Figure 2. Example of unconstrained layout

In general, we can isolate the following changes of FLP in the circumstances of Industry 4.0: • •

•

The production/manufacturing systems are getting larger according to the number of connected/ included devices. The consequence is that the system for FLP has to deal with a larger number of devices. Man-operated systems and autonomous systems differ greatly from each other, mainly because of the different abilities of man and computer. Based on the topography of production systems, artificial systems are not limited to predetermined basic forms of topologies (Figure 1) in order to organise work and navigation. For Industry 4.0, the Open Field Layout can be used without any pre-defined shape. The design of transport paths is needed because of man-machine cooperation. OFLP has to be constrained, not necessarily to the predefined shape, but to the degree of transport path branching.

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Cost Function The most significant indicator of the efficiency of a layout are the Material-Handling Cost (MHC) (Emami & Nookabadi, 2013) which is used in our research. The objective of solving the FLP is to minimise the total Material-Handling Cost of the system. The following notations are used during the development of the objective function: Fij amount of material flow amongst workstations i and j (i, j = 1, 2 ... N). Cij unit Material-Handling Cost between the locations of workstations i and j (i, j = 1, 2 … N). Dij distance between the locations of workstations i, and j, measured as the number of triangular line-segments. C total cost of the material-handling system.

• • • •

The total cost function is defined as: N

N

C    Fij Cij Dij i 1 j 1



(1)

The evaluation function considered in this paper is the minimisation of Material-Handling Cost, which is the criterion most researchers prefer to apply when solving layout problems.

Space Presentation Most research is based on a pre-defined basic facility layout. The researches differ mainly in their definitions of the problem and space. The placements of individual devices in a manufacturing system can be described as: • • •

One machine in one place (Mak, Wong, & Chan, 1998). It can use block or mesh based representation, Figure 5. Discrete space. The space assigned to machines is separated into smaller special parts (Osman, Georgy, & Ibrahim, 2003) (Figure 9). Open-field space. The space is not constrained or is constrained loosely (Yang et al., 2005).

Most research deals with block layout, and less with discrete or open-field presented space layout (Hosseini-Nasab et al., 2018). However, for facility layout in Industry 4.0 the best representation is open field representation, although the discrete space presentation can get similar results with higher resolution (Ficko et al., 2010). For the solving of FLP, space presentation plays the most important role, but discrete space presentation has similar functionality. If the parts of the space during discrete space representation are small enough, space presentation is similar to the open field space presentation in Figure 3. With a wisely selected dimension for space parts, such as in Figure 3c, an accurate enough presentation of layout for the optimisation process can be created with a relatively small number of discrete parts. There are no significant differences between open-field space presentation Figure 3a and discrete space with small enough parts of space (Figure 3c). The advantage of such an approach is flexibility of computation, and the possibility of defining diversely-shaped devices and space. 224

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Figure 3. Comparison of space presentation; a – open field presentation, b – discrete space presentation with larger discrete parts, c - discrete space presentation with smaller discrete parts (Ficko et al., 2010)

Hybrid space representation is based on computational geometry. The rise of Computational Geometry as a branch of Computer Science can be seen in computer graphics, computer-aided engineering (mesh generation), Computer-Aided Design (CAD), Computer-Aided Manufacturing (CAM) and Electronic Design Automation (EDA). Different geometrical problems, which arise from mathematical visualization, can be tackled with the implementation of Computational Geometry. A finite number of straight line elements connected to form a closed chain is defined as a polygon. Each polygon is specified by its edges and vertices. Polygons are employed in order to construct available space, devices and potential obstacles (located on the available space). Different Boolean operations on polygons can be used in order to explore and retrieve certain relationships between spatial attributes represented as polygons. A set of Boolean operations are stated in the Figure below. Sweep line algorithms are used in order to implement Boolean operations on polygons (Bentley & Ottmann, 1979). To perform different Boolean operations polygons are regarded as planar maps, whose bounded faces are labelled P and Q. The intersection computation P ∩ Q is performed with extraction of the faces in the overlay, labelled with P and Q. Union computation P ∪ Q is performed with extraction of the faces in the overlay, labelled with P or Q, and the difference computation P\Q is performed with extraction of the faces in the overlay, that are labelled with P and not with Q. Figure 4. Boolean operations on polygons (Martinez, Ogayar, Jimenez, & Rueda, 2013)

With the stated Boolean operations relations among devices, obstacles and available space can be calculated in a different manner. Also, the Computational Geometry enables better overall problem insight with clearer facility resolution. Hybrid space representation is, in other words, enabled with

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the definition of polygons, representing available space, devices and potential obstacles, and employing certain Boolean operations. With hybrid space representation, the placement, intermediate routes (spaces among devices), overlay, serving point locations of devices, can be computed and presented with a higher degree of accuracy compared to discrete space representation. Current space representation is independent of the mesh parameters needed for discrete parts` definition, and offers an efficient way for solving the FLP problem.

TRIANGULAR MESH BASED SPACE REPRESENTATION A specific layout design heuristic method is used, based on the work of Schmigalla (Schmigalla, 1970), known as the “modified triangle method”. A GA-based system to solve complex problems of layout design, based on the Schmigalla representation of floor space. The triangle method by Schmigalla (Schmigalla, 1970) belongs to the heuristics layout planning methods. Its main characteristic is that floor layout is represented by equilateral triangles, where the vertices represent devices. It is used mostly to find a reasonably good solution for a theoretical layout that is later adjusted to the real requirements to get real a layout which can be used by civil and mechanical engineers for final design. The original Schmigalla procedure (Schmigalla, 1970) is fairly simple, but only for low numbers of workstations. The calculation for more than 10 workstations becomes extremely time-consuming. This is, however, not the biggest problem. The biggest problem is the fact that the degrees of material flow, and especially the calculating of its sums between workstations and the pairs of already placed workstations within the mesh, start to repeat themselves. When selecting a new workstation for placing within the triangular mesh, several possibilities can emerge (several equal, yet highest values for the degrees of material flow). This means that there are more options for building a layout. There can be more than one candidate (workstation) to be selected next, and/or there are several possibilities regarding already emerged pairs where a new candidate can be placed. Even when there are less than 10 workstations, a large number of possible layouts can emerge that make these cases extremely difficult to solve. When the number of workstations increases, the number of possible solutions also goes up. The aim is, of course, to find the best solution from all the possible solutions. The best solution is the one with the lowest Material-Handling Costs. To put it another way, the optimal solution is the one where the individual distances between all those pairs of workstations that have mutual material flows, are equal to one triangular line-segment. There are several presumptions regarding this method (Ficko & Palčič, 2013): •

The sizes of the workstations are omitted; devices are represented by vertices within the triangular mesh. The distances between neighbouring workstations are equal as the triangles are equilateral. The cost per distance travelled when making a journey between workstations is constant.

• •

In order to construct this triangular mesh, the following data are needed: • •

226

The list of workstations. The frequencies and directions of the material flows between workstations in a selected time interval.

 Design of Facility Layout for Industry 4.0

All these data can be constructed within a flow record chart. Both degrees of material flow from one workstation to other workstation (and vice versa) are summarised and written above the diagonal within the flow record chart.

GA-Based System for Design of Triangular Mesh Based Facility Layout Representation This presented system uses Evolutionary Computation for the search for an optimal facility layout. In the first step, three types of information have to be input into the system. The first type relates to the generation of mesh, the second is problem definition (flow record chart), and the third the parameters of the GA. From this point on the automated procedure takes over. The creation of an initial population creates a new generation randomly; the only limitation used is the fact that the randomly-generated organisms have to present a feasible solution. After creating an initial population of organisms (solutions), the value of the cost function is calculated for all of them. The solution with the best value is represented graphically, together with the cost-function value for human interactive control over its evolution (design process). The evolution starts after the first population is created. New generations are created out of the current generation with the help of selection, reproduction, crossover, and mutation operations. Selection is an operation which ensures the creation of new organisms from better-than-average organisms. The selection of a genetic operation is based on tournament selection probability. After the new population is created, the fitness is applied and the stop criterion is checked. The stop criterion is the fixed number of generations, since the optimal value of the cost-function is unknown in advance. In order to solve using GAs, the possible solution has to be coded to the appropriate form. In other words, the solutions must be suitably-coded into organisms. Permutation coding was chosen, since it ensures the simple, natural coding of organisms and, therefore, a fast computational speed. Our solution uses the representation of layout solutions with the mesh based on equilateral triangles. Workstations are set to the mesh’s vertices. The method forms the solution constructively, which can grow in any direction. Therefore, the mesh size has to be defined by solving the problem of optimal layout within a triangular mesh by a GA. It is beneficial to keep the number of rows and columns as low as possible, since the solution converges more quickly and the feasibility of solution is improved. This method of representing solutions enables ‘pre’ forming of the final solution. If the actual available space for the layout has some limitations, such as, e.g. limitations regarding the maximal number of rows, it can already be included in this phase during designing (Ficko & Palčič, 2013). In an extreme case we can force a (one-row) creation layout. Figure 5 presents a representation of the available space using a mesh of equilateral triangles, in rows and columns. An organism is a set of vertex designations (alleles), and the location of the vertex within an organism (locus) which carries the workstation’s designation, as shown in Figure 6. This type of coding is very simple and, therefore, fast for computation. Many genetic operators exist for this type of coding (Gen & Cheng, 1997).The evolutionary operations of selection, reproduction, and the genetic operation of crossover and mutation, were used in this model. In the next step, organisms obtained by selection were the subject of reproduction, crossover or mutation. The probability of reproduction pr, of crossover pc and mutation pm are input parameters set before the evolution starts. For the crossover operation a variation is used of partially-mapped crossover PMX. In view of the operation, PMX is a modification of a one or two-point crossover, but additionally applies a mapping relationship to repair offspring that have duplicate genes. The proposed coding, namely, does not allow duplicate 227

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genes in an organism, because this would present an unfeasible solution with two workstations on the same place. Mutation, on the other hand, introduces new genetic material into the organisms at the level of the genes. The aim of mutation is the conservation of variety within the population, thus avoiding premature convergence (to a suboptimal solution). Figure 5. FLP represented by a mesh of equilateral triangles (Ficko & Palčič, 2013)

Figure 6. Coding of organism(Ficko & Palčič, 2013)

Experiment The system was evaluated intensively on three cases. The first case consisted of 9 workstations, and was proven to be solved efficiently by the Schmigalla method. This case was named POL9 (Ficko & Palčič, 2013). The second case was more complex regarding the flow record chart and number of workstations. It consisted in total of 11 workstations (Prêt, 2012) and we named it PRET11. After this evaluation of these two cases, the system was evaluated with an even bigger problem, which consisted of 25 workstations in total (Ficko et al., 2010). The chosen size of the mesh or the number of nodes used for the placing of workstations, was large enough, as such, that it did not represent any limitation. In the first case (POL9), the size of mesh was, for testing purposes, set intentionally on 9x9. If it were necessary to avoid the space limitation completely, the number of rows and columns could be set as equal to the

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number of workstations. On the other hand, in an extreme case, the layout in one row could be obtained. In this case, the shape of layout was not predefined, and a search was made for the global optimum for the layout within the open space. The search-space is, therefore, limited, although very large. Namely, 9 workstations can be set on the 9x9 mesh in approximately 1017 different ways. At the beginning of the testing, the true potential of this system was clear, as, when searching for an optimal layout for around 10 workstations, the solutions converged within a few seconds. Even in those cases consisting of several ten-workstations, the converged solutions were obtained in few minutes. Good, or even optimal solutions, were obtained on virtually all runs. The in-depth analysis of solutions obtained after several ten-runs produced, for the first case, the same result as the best known solution, and, in the second case, an even better solution than the case author (Prêt, 2012). During the solution analysis, the fact that the same solution can actually be presented in different ways had to be taken into consideration, as it can, namely, be rotated or mirrored. There are six different representations of the same solution. Figure 7 presents the best layout for the case for POL9 on the left, whilst, on the right, the best solution is presented for PRET11. The values of the total transportation cost are written at the bottom. Figure 7. Best solutions for the POL9 and PRET11 cases

Searching for the best layout in the case of 25 workstations took more time. Despite this fact, the solutions converged as expected. There was no value for the good (or optimal) solution, because this case was not solved by the Schmigalla method, as were the former two. 276 runs were made in order to ensure as good a solution as possible. The solution converged very well at every run of the evolution. Figure 8 shows the changes of value of best solution during the evolution. This test proved the capacity of the presented system; it is easy to imagine using this system on cases consisting of many more workstations than our largest tested-case.

Discretised Dpace Representation The MHC of transport between two devices can be determined if their mutual distance Lij is known. The values of fij and cij are static, regardless of the layout. Lij depends on the physical layout and, therefore, changes with respect to the position of devices i and j, and its orientation. In order to determine Lij, it is necessary to use a certain sophisticated method for searching an optimal path through a field full of obstacles.

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Figure 8. Values of fitness function of best solution during the evolution

In general, the system is composed of two subsystems. One of them serves for creating a layout (subsystem for creating a layout), and the second serves for evaluating the layout corresponding to the lengths of those paths between devices (subsystem for evaluation). During the first step, a swarm of layouts is created by the subsystem on a random basis, whereas, in all the following steps, the layouts are made following arithmetic operators. The forming of the layout with the proposed system was divided into these main steps, where steps 2 and 3 repeat until a good solution is found (Ficko et al., 2010): 1. 2. 3. 4.

Acquisition of information needed for designing of the facility. Determining a layout by a subsystem for layout creation. Evaluation of a layout by the subsystem for evaluation. Presentation of the final solution.

The input information contains the geometry of available space and devices, and locations for serving devices. Furthermore, we need transport quantities between the individual devices during a certain time period, and the costs for transport. At first, the subsystem for creating a layout creates the initial group or swarm of particles (layouts). Then these layouts are evaluated within the subsystem for evaluation. The subsystem for evaluation, using sophisticated methods, determines the shortest paths between all mutual devices. These paths are then used in the fitness function (1). In the next step, the layout feasibility is checked. It is checked for overlay of devices and for path existence. Overlay of devices is unfeasible, since it is impossible to place one device on top of another device. The other reason for penalization of layout is the non-existence of a valid path. It could occur that the serving point is enclosed by other devices. Overlay and encirclement of the devices is penalised by an increase in the value of the fitness function (1).

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Figure 9. Unfeasible layouts

The calculation of the fitness function’s value is carried out after the feasibility check and penalization of bad layouts. The layouts proceed to the next iteration, where the best, or the fittest ones, have a greater possibility of contributing a good design to the next iteration. The next iteration is created using evolutionary and genetic operators. The loop is repeated until the exit condition is met. The exit condition can be set by a user specified value for the fitness function, or a certain number of loops. Figure 10. System outline (Ficko et al., 2010)

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Coding and Evaluating The coding of solutions into layouts/particles is carried out on the following principle: A particle is composed of centre positions for all devices in the system, as presented in Figure 11. In the particles, the values of the device’s positions are presented as real numbers, so special coding is unnecessary. In the evaluation subsystem, coordinates are used for creating the actual layout. Figure 11. Schematic representation of particle

Evaluation of Layout For the evaluation of a layout, it is necessary to find the shortest path between two points in a 2D space (Figure 12), which represent the serving points of the device. It uses a technique similar to a breadth-first search (Simmons, 1969). Throughout this article, we will use “node” to refer to the elements of the matrix. The method works on the following principle: In the first step the starting node A is examined, then all the neighbours of A are examined, followed by all the neighbours of all the neighbours of A, and so on, until the desired target node has been reached, or until there are no nodes left to be examined (in this case no path exists). We need to keep a track of all the nodes, to ensure that no node is processed more than once. This is accomplished by linking the field “Status” with all the nodes. The outline of the algorithm is as follows: 1. Initialize all nodes to the ready state (Status=Ready). 2. Put the starting node A in a queue and change its status to the waiting state (Status=Waiting). 3. Repeat steps a and b until the queue is empty: a. Remove the first node N of the queue. Process N and change the status of N to the processed state (Status=Processed). b. Add to the rear of the queue all the neighbours of N that are in the ready state (Status=Ready) and change their status to the waiting state (Status=Waiting). 4. Exit. The shortest path (Figure 12) between two nodes can be found using the breadth-first search, if we keep track of each edge’s origin (i.e. how a particular element in the path is reached), by using the array Origin together with the array Queue. This method is used in the class. The path finder uses the breadth-

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first search technique without the actual implementation of graphs, i.e. there is no class/structure used for graphs, no adjacent lists, no weights assigned to edges, etc. The class uses certain mathematical formulae to access the adjacent nodes of an element (node) in the matrix (path). A breadth-first search is an informal search method that aims to expand and examine all the nodes of a graph or a combination of sequences by systematic search through every solution. In other words, it searches the entire graph or sequence exhaustively without considering the goal until it finds it. It does not use heuristics. From the standpoint of the algorithm, all child nodes obtained by expanding a node are added to a FIFO queue. In typical implementations, nodes that have not yet been examined for their neighbours are placed in the container (such as a queue or linked list) called “open” and, once examined, they are placed in the container as “closed”. A breadth-first search is optimal for unit-step cost. In general, a breadth-first search is not optimal, since it always returns the result with the fewest edges between the start node and the goal node. If the graph is a weighted graph and, therefore, has costs associated with each step, the goal next to the start does not have to be the cheapest goal available. This problem is solved by improving the breadth-first search to a uniform-cost search, which considers the path costs. Nevertheless, if the graph is not weighted and, therefore, all step costs are equal, the breadth-first search will find the nearest and the best solution. Figure 12. Path finder result

Experiment The system was tested on a group of test cases. The first case, (FBB14), was a facility consisting of 14 devices (Ficko, Brezocnik, & Balic, 2004). Other test cases were taken from research made by Heragu and Kusiak (P4) (S. S. Heragu & Kusiak, 1991), Love and Wong (LW5) (F. Love & Y. Wong, 1976), Simmons (S8 and S11) (Simmons, 1969), and Aiello et. al. (AEG20) (Aiello, Enea, & Galante, 2006). All cases except AEG20 were used for search of a good layout in one-row, and are well known and studied cases. We used them for a search of a near-optimal solution of unconstrained layouts. Firstly, we collected all necessary data for designing of a facility. We prepared a matrix of travel frequencies` costs, and information on the shapes and sizes of individual devices. We decided to use

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a larger space than necessary to avoid any unfeasible solutions in the first phase of the optimisation process. In the preliminary runs of the system, a combination of parameters was searched for with an acceptable probability of success. The search for a good solution consists of three phases. After the first few iterations, where individual devices are laid over the whole surface, the devices started to group. In the second phase the search for a near optimal solution begins where the first big improvement was accomplished. During the process, it progressed from bad designs to an optimal or near optimal solution. In the third phase, only minor modifications were made. The nature of the problem provides the opportunity for different layouts to have similar good values for the fitness function. The research results proved the presumption we made at the start. Omission of a predefined general shape of manufacturing system decreased the total transport costs by 50% compared to previous research (Ficko et al., 2004). This way, the layout is tailored for minimum transport costs. In Table 1 computational results are shown for all test cases with different numbers of devices in the layout. A greater number of devices in the layout means computation of an optimisation problem with a greater number of dimensions. Because of that, different numbers of particles are used in the swarm. In the last row, the fitness values are shown for the best particle in the evolution. Table 1. Best solutions Test Case

Number of Devices

Area of Devices

Number of Particles in Swarm

Number of Iterations

Fitness Value

P4

4

554

30

500

128

LW5

5

37

30

500

64

S8

8

256

30

500

269

S11

11

447

40

500

2411

FBB14

14

52

20

500

5550

AEG20

20

4838

60

500

1965670

The results show the ability of the proposed system to obtain very good solutions of a loosely constrained layout. As such, the proposed system can be used as a decision support tool for the human expert. We can see future work in adding more criteria for optimisation, and in using different optimisation methods. Although we obtained very promising results with PSO, which were confirmed by past research (Ficko et al., 2004), we cannot claim we have got an optimal solution. Without doubt, our solutions are good, but there is still a potential for improving the system.

HYBRID SPACE PRESENTATION In the current part, a hybrid space representation has been adopted for calculating mutual distances between devices Lij. The values of fij and cij are static regardless of the layout. Lij depends on the physical layout, changing its value with respect to the position and orientation of devices i and j. In order to

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determine Lij it is necessary to use a certain sophisticated method for searching an optimal path through a field full of obstacles. In general, the system is composed of two subsystems. One of them serves for creating the layout (subsystem for creating a layout), and the second serves for evaluating the layout corresponding to the lengths of those paths between devices (subsystem for evaluation). During the first step, as a swarm of layouts, best solutions of triangular mesh space representation are used, and optionally additional layouts are created by the subsystem on a random basis, whereas, in all the following steps, the layouts are made following arithmetic operators. The forming of the layout with the proposed system was divided into these main steps, where steps 3 and 4 repeat until a good solution is found: 1. 2. 3. 4. 5.

Acquisition of input information needed for designing of a facility. Best GA solutions of triangular mesh based space representation. Determining a layout by subsystem for layout creation. Evaluation of layout by the subsystem for evaluation. Presentation of the final solution.

The input information contains the geometry of available space and devices, and locations for serving devices. Furthermore, we need transport quantities between the individual devices during a certain time period, and the costs for transport. At first, the GA finds optimal arrangements of devices based on triangular mesh based space representation, which are used for the first iteration of swarm optimisation. If in the initialization stage of PSO additional layouts are needed (swarm size is bigger than the number of different s triangular solutions found by GA), the subsystem for creating layouts creates additional layouts on a random basis inside assigned boundaries. Then, all of these layouts are evaluated within the subsystem for evaluation (Figure 16). The subsystem for evaluation, using sophisticated methods, determines the shortest paths between all mutual devices. These paths are then used in the fitness function (1). In the next step, the layout feasibility is checked. It is checked for overlay of devices and for path existence. Overlay of devices is unfeasible, since it is impossible to place one device on top of another device. The other reason for penalization of layout is the non-existence of a valid path. It could occur that the serving point is enclosed by other devices. Overlay and encirclement of the devices is penalised by an increase in the value of fitness function (1). The layouts proceed to the next iteration, where the best, or the fittest ones, have a greater possibility of contributing a good design to the next iteration. The next iteration is created using the PSO heuristic optimization method. The loop (consisting of layout creation and evaluation) is repeated until the exit condition is met. The exit condition can be set by user specified value for the fitness function, or a certain number of loops.

Coding and Evaluating The coding of solutions into layouts/particles is carried out on the following principle: A particle is composed of centre positions for all devices in the system, as presented in Figure 13. In the particles, the values of the devices` positions are presented as real numbers, so special coding is unnecessary. In the evaluation subsystem, coordinates are used for creating the actual layout. The end number of particles represents the seed for random number generation, so the exact paths can be reproduced lateron.

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Figure 13. Schematic representation of a particle

Evaluation of Layout For the evaluation of a layout, it is necessary to find the shortest path between two points in a 2D space, which represent the serving points of the device. In this part, undirected graphs are combined with Voronoi’s (Voronoi, 1908) based meshing procedure and Dijkstra’s shortest path algorithm (Dijkstra, 1959). The meshing procedure discretises a free space (available facility space) and, with undirected graphs, represents a less computationally extensive way of solving the FLP problem. Dijkstra’s shortest path algorithm finds the shortest path from one vertex to another with more efficiency (especially in weighted graphs) compared to a depth-first search (used in the previous discretised space representation example). The computation complexity of the designed algorithm is altered with “strategic” placement of control points defining Voronoi’s diagrams, which influence the path formation and MHC. An undirected graph, G=(V,E,W), is defined as a network of objects V, called vertices or nodes. Vertices are connected together with bidirectional edges E, which are unordered pairs of V. The bidirectional nature of the graph means one can travel in both directions of an edge Ei (from vertex i to vertex j, as well as from vertex j to vertex i). Weight W represents the travelling cost from vertices connected by an edge. Figure 14a offers an example of an undirected graph G=(V,E,W), where the set V is comprised of four nodes V={A,B,C,D}. Edges connecting vertices in the graph G are bidirectional, and are named after the two vertices they connect. In the representative example below, five edges are defined as a set of edges E= {AB,AC,BC,AD,CD}. Every edge has a certain weight W assigned to it; in the case below, the set of weights is defined as W= {WAB,WAC,WBC,WAD,WCD} which represents a set of weights. If one wants to travel from vertex A to vertex D, there are three different routes A®B®C®D, A®C®D and A®D in existence, while the shortest path A®C®D defined by visual inspection, has a route cost equal to 𝛾, which is equal to the sum of weights belonging to the travelled edges. Figure 14b offers an insight on usage of an undirected graph in order to discretise facility layout space. The undirected graph is spread all over the free space, and weights Wij of the discussed graph are calculated on the basis of the Euclidean distance between connected vertices. The vertices of an undirected graph are created in a randomised manner, in order to reproduce the exact undirected graph (especially concerning the vertices located on free space) and, consequently, the same MHC during the optimisation procedure and the same seed number must be used. We continue the discussion of the shortest path search in graph GEi,j. One of the methods which is especially suited for applications where the edges have all nonnegative weights Wi,j>0, is the shortest path search procedure introduced by the Russian mathematician Boris Dijkstra in 1959. Dijkstra’s algorithm finds the shortest paths from the source vertex to all the other vertices in directed and undirected

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graphs. In essence, the algorithm represents a labelling procedure, where, in order to find the shortest path from vertices v®w in connected graph G, the shortest path to the vertices laying one layer before the final vertex w must be discovered (Bellman’s Minimality Principle). Dijkstra’s algorithm is shown in Figure 15. Let us say we want to find the shortest path from vertex 1 to any other vertex. At each step of the computation, each vertex gets labelled with either a Permanent Label (PL) or Temporary Label (TL). The initial step of the algorithm starts by assigning a Permanent Label L1=0 to the source vertex, and all other vertices get Temporary Labels. Then, the algorithm loops between Steps 2 and 2. In Step 2, the idea is to choose minimal index k, which is selected among vertices in aTL. In the case of an empty TL, meaning all the vertices have been visited, the algorithm stops. Step 3 represents the idea that upper bounds will, in general, improve (decrease), and ought to be updated accordingly. The Temporary Label Lj of vertex j will be Lk + lkj if there is an improvement (a shorter path was found leading from source vertex to vertex j), or the old one Lj if there was no improvement (Kreyszig, Kreyszig, & Norminton, 2011). Figure 14. Graphs; a – simple undirected graph, b - undirected graph representation spread over the available space; weights are equal to the Euclidean distance between connected vertices

Undirected graphs are defined by sets of vertices, edges and weights. The basic idea of weight calculation and optimal path computation has been defined by the Euclidean distance measure and Dijkstra’s algorithm, meanwhile the question of optimal space discretization for path design still persists. Voronoi’s diagrams have been employed in the current example in order to partition the available facility space into regions. Vertices and edges (representing the undirected graph) are computed with Voronoi’s diagrams of the meshing procedure, based on pre-specified parameters for defining control points, sometimes also called sites or seed points (different than the seed number defined in the particle). Control points define the locations of edges and vertices, and enable the discretization of available space. This enables representation of possible paths as sequences of travelling Voronoi vertices connected via. edges, located on available facility space. Discretization of space can also be accomplished similarly with triangulation methods. Figure 16 below depicts the workings of special Voronoi’s based meshing procedure where the control points are set to cover the whole available space. Firstly, the devices` locations, available space and possible obstacles must be defined preliminarily. Device overlay is checked

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based on Computational Geometry with polygons and Boolean operations. Devices` overlay is penalised by an increase in the value of fitness function (1). User is set to choose a number of randomly spread (all over the available space) control points. With the use of Voronoi’s meshing procedure, available space is discretised, meaning the position of Voroni’s vertices and edges (basically connected edge pairs), was respectively calculated and assigned. In the next step, the vertices which lie on the devices (inside or at the border), or obstacles, or outside the available space are deleted, with all associated edges. Let us stress that associating special available space shapes (special meaning not rectangular) and obstacles can be done similarly as devices. The last step consists of counting and additionally modifying Voronoi’s vertices and edges (that weren’t deleted), to suit the form of undirected graph representation of vertices and edges, simultaneously, the weights are calculated and assigned to graph edges. After suitable graph representation is computed, serving points are assigned to the nearest graph point, and Dijkstra’s shortest path algorithm is able to find the routes and calculate fitness function values (1). In this step, encirclement of the devices is also penalised by an increase in the value of fitness function (1), in the case of a path not reaching the target vertex. Figure 15. Dijkstra’s algorithm for shortest paths(Kreyszig et al., 2011)

Experiment With strategic definition of the control points a certain FLP system arrangement can be represented with an adequately spread Voronoi’s diagram, which represents the possible paths to the highest extent at low computation cost. The real power of the discussed example arises if the control points are placed carefully. Placement of control points alters the computation time, also, when discretization with a large number of control points is used, the computed shortest paths converge to the real shortest paths, while causing the routes to separate (the so-called branching effect). The system was tested on an FBB14 case (Ficko et al., 2004), which was used for a search of a near-optimal solution of unconstrained layouts.

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Figure 16: Special Voronoi’s based meshing procedure, where the control points are set to cover the whole available space

Firstly, the authors collected all necessary data for designing of a facility, and prepared a matrix of travel frequencies` costs, and information on the shapes and sizes of individual devices. The search for a good solution consists of three phases. After the first few iterations, where individual devices are laid over the whole surface, the devices started to group. In the second phase, the search for a near optimal solution begins, where the first big improvement was accomplished. During the process it progressed from bad designs to an optimal or near optimal solution. In the third phase, only minor modifications were made. In Table 2, computational results are shown for the FBB14 test case (Ficko et al., 2004) with 14 devices in the layout. A greater number of devices in the layout means computation of an optimisation problem with a greater number of dimensions. Different numbers of particles are used in the swarm because of that. In the last column, the fitness values are shown for the best particle in the evolution. Table 2. Best solutions Test Case

Number of Devices

Area of Devices

Number Of Particles In Swarm

Number of Iterations

Fitness Value

FBB14

14

52

100

1000

9070

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The results show the ability of the proposed system to obtain very good solutions of a loosely constrained layout. As such, the proposed system can serve as a decision support tool for the human expert. We can see future work in adding more criteria for optimisation, in using different optimisation methods, and in setting the constraints for Voronoi’s based meshing procedure. Although promising results were obtained with PSO, there is still a potential for improving the system.

CONCLUDING REMARKS A good solution in industrial practice is often not a mathematically optimal solution, but a good technological solution based on a mathematical solution. The most important from the MHC criteria is the total length of the travel path with a time period, but, however, this is not the sole criterion. Often this is the only criterion when searching for an optimal solution and other, mostly technological, criteria are added later within the phase of creating a real layout by humans. The involvement of significant technological criteria within the phase of searching for an optimal solution is unfruitful, since it is often hard to describe the mostly subjective wishes of the system-user. Only the user knows most of the design goals, and creates an altered solution that is based on the optimal solution. At this point, the set of solutions created by intelligent methods presents their strengths. The user can choose from a set of similarly good solutions according to all the technological and environmental limitations. At this point, we see the possibility of proceeding with research that would ensure the creation of good solutions, and provide knowledge of those elements of a good solution that can support humans by the creation of a real layout. In the discussed chapter, the underlying inspiration is represented by nature’s way of composing the specialised systems, that seem highly chaotic to the human way of perceiving order, yet highly efficient and suited for its purpose. Nature’s seemingly random manner is inherited in all examples. In discrete and hybrid space representation examples the randomness is imbedded, as a fairly loosely defined constraint concerning the orientation of devices. In the hybrid space representation example, nature’s randomness can also be observed as a random distribution of control points. Information about locations of serving points, devices, obstacles, available space, and possibly more precise intermediate routes, can be computed with regard to preliminarily set robust triangular and discrete space presentations. We can see future work in adding more criteria for optimisation and in using different optimisation methods. The work presented in this paper offers excellent groundwork for additional research, especially in expanding the system for dynamic modification of operating place size. The size of an operating place is a parameter, the selection of which has an influence on the system’s convergence. The necessary size of an operating place was unknown at the beginning, and a bigger place must be chosen. The convergence of the system is worse because of that. In additional research, a system will be developed for intelligent modification of operating place size during the evolution. There are also several future challenges that will be addressed using the presented GA-system. We will add the real sizes of workstations within our system, and apply the possibility of different transportation costs amongst each pair of workstations. Another idea is to include the types of machines, and find optimal solutions based on placing the same types of workstations together (design of process layout). As we have already mentioned, we are also interested in finding the core solution for possible layouts – the part of the layout that is extremely important for the target function. This core-solution is a base solution of the optimal layout that cannot be changed if we want to keep transportation costs to a minimum.

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ACKNOWLEDGMENT This research was supported by the Slovenian Research Agency [research core funding No. P2-0157].

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ADDITIONAL READING de Berg, M., van Kreveld, M., Overmans, M., & Schwarzkopf, O. “Voronoi Diagrams: The Post Office Problem.” Ch. 7 in Computational Geometry: Algorithms and Applications, 2nd rev. ed. Berlin: Springer-Verlag, pp. 147-163, 2000. doi:10.1007/978-3-662-04245-8_7 Ficko, M., Brezovnik, S., Klancnik, S., Balic, J., Brezocnik, M., & Pahole, I. (2010). Intelligent design of an unconstrained layout for a flexible manufacturing system. Neurocomputing, 73(4-6), 639–647. doi:10.1016/j.neucom.2009.06.019 Ficko, M., & Palcic, I. (2013). Designing a Layout Using the Modified Triangle Method, and Genetic Algorithms. International Journal of Simulation Modelling, 12(4), 237–251. doi:10.2507/IJSIMM12(4)3.244 Gen, M., & Cheng, R. (1997). Genetic algorithms and engineering design. New York: Wiley. Heragu, S. S. (2008). Facilities design (3rd ed.). Boca Raton, FL: CRC Press. Hosseini-Nasab, H., Fereidouni, S., Fatemi Ghomi, S. M. T., & Fakhrzad, M. B. (2018). Classification of facility layout problems: A review study. International Journal of Advanced Manufacturing Technology, 94(1), 957–977. doi:10.100700170-017-0895-8 Kreyszig, E., Kreyszig, H., & Norminton, E. J. (2011). Advanced engineering mathematics (10th ed.). Hoboken, NJ: John Wiley.

This research was previously published in the Handbook of Research on Integrating Industry 4.0 in Business and Manufacturing; pages 101-126, copyright year 2020 by Business Science Reference (an imprint of IGI Global).

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Incorporating Industry 4.0 in Corporate Strategy Anirudh Agrawal Copenhagen Business School, Denmark Sebastian Schaefer TechQuartier, Germany Thomas Funke TechQuartier, Germany

ABSTRACT Knowledge tells us that increase in productivity decreases margins in a competitive market and increases margins in a growing market. Not much is known about Industry 4.0 and its position within the corporate strategy and its possible impact on corporate performance. This chapter discusses the position of Industry 4.0 within the corporate strategy and how it may impact corporate performance. The main points include that corporates have to reflect on their core resources, leadership, and knowledge portfolio to take advantage of the Industry 4.0 platform. The corporates engaging in cost and volume strategies and those engaging in fast new product development strategies may benefit greatly with Industry 4.0. The services of Industry 4.0 may be outsourced but with added risks. Finally, the increased productivity with Industry 4.0 under constraint market growth may lead to the potential risk of market failure and large-scale layoffs.

INTRODUCTION The primary mission of any corporation is to maximize profitability on behalf of its shareholders (Friedman, 2009) and to provide the best value to its stakeholders (Freeman, 1984). The development of technologies such as the big data and data analysis, IoT, artificial intelligence (AI), flexible robotics, 3D printing, augmented reality, 3D holographic scanning smarter sensors, greater miniaturization, cloud computing, customer feedback and Customer management software (CMS), location detection DOI: 10.4018/978-1-7998-8548-1.ch013

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 Incorporating Industry 4.0 in Corporate Strategy

technologies such as radio-frequency identification (RFID), natural language programming, and changing global social-political environments are all driving human productivity toward a new industrial revolution (Herter & Ovtcharova, 2016; Reinhard, Jesper, & Stefan, 2016). These new technologies are consequently increasing the productivity of the corporations and changing the market equilibrium. The more these technologies evolve and are accepted by the industry, consequently, the industrial productivity shall proportionately increase. However, as the productivity of the corporation increases, it may or may not proportionately increase the performance and profitability. Contrary to popular understanding, any impulsive investment in technology may increase the corporate debt and, consequently, decrease the corporate performance. Technology adoption and subsequent productivity change may also distort the market equilibrium, which, will force the competitors to engage in similar cost/volume strategies, lowering the overall margins per unit of production (Porter, 1979) for the whole industry. With these possible consequences in mind, it is important to logically weigh where in the value chain would the Industry 4.0 platform technology increase the value for the corporation and how these technologies would contribute to the competitiveness in the long run. In the background of the evolving Industry 4.0 technological environments, this chapter provides a framework on how the corporations can increase their performance by reflecting on the role of Industry 4.0 on the overall corporate strategy. While most of the literature on Industry 4.0 is on automation (Mrugalska & Wyrwicka, 2017), productivity, benefits, and business models (Man & Strandhagen, 2017), this chapter tries to create a dialogue between academics of the Industry 4.0 and those who study the corporate strategy and corporate performance. A clearer understanding of Industry 4.0 and its position in corporate strategy will help us in understanding better how to develop business models and strategize the “may or buy” decision on Industry 4.0 technology. The chapter is structured in the following way: First, it briefly reviews Industry 4.0. Next, this is followed by an assessment of corporate strategy and strategy landscape. Finally, the chapter discusses the position of Industry 4.0 in corporate strategy landscape and how and where it brings value to corporate performance. The chapter presents eleven propositions that give strategy-level perspectives on Industry 4.0.

LITERATURE REVIEW The motivation of this chapter is to provide a slightly different perspective on Industry 4.0 where the authors contextualize it in the corporate strategy. Most of the scholarships around Industry 4.0 are on the disruptive increase in productivity, automation, new market alpha, and industry digitalization, but the research rarely makes any conversation on corporate strategy. It is in this context that this chapter adds value.

A BRIEF OVERVIEW OF INDUSTRY 4.0 Industrial revolutions have always been accompanied with a disruptive increase in productivity. The increase in productivity has also increased wages and, subsequently, the quality of life of the citizens. The evolution of Industry 4.0 is incremental, yet its effect will have a disruptive impact on corporate performance, productivity and overall markets and industries. One can understand it better when studying all the different stages of industrial revolutions (Figure 1). 246

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Industry 1.0 was the first generation of industrial revolution that started around late 18th century. During this generation, it was the first time that machines, instead of animals, started to support humans in manufacturing and production. The invention of water turbines and steam engines helped the people–machine interface by increasing their productivity substantially. The factories owned the energy-generation systems. The management structures of these manufacturing and production units were highly pyramidal and feudal. The productivity was higher than before, and the degree of human intervention was very high. Industry 2.0 evolved from the first generation after the introduction of electricity in the 1890s. The electrical energy-driven machines further changed the outlook of the industry. It increased industrial productivity. In this era, the productivity was higher and easier, leading more corporations to engage in production and manufacturing activities. With limited market capacity, this led to increased competition. The competition also led to a faster innovation, resulting in the division of labor and the structuring of the study of business and management into a more specialized field. This was also the era, when the European firms were aggressively looking for newer markets especially in third would countries. The theories of scientific management were formulated during this time. This was also the period when business schools started to emerge and management as a field of inquiry started to take structure. Industry 3.0 evolved from the second generation of industrial revolution. It started around the 1970s when information technology (IT) integrated management systems were introduced. IT significantly reduced transaction costs and control costs of managing and running a corporation. IT-based management systems also enabled the decoupling of organizations’ core strength from manufacturing. Post Industry 3.0, the organizational core competency and core value proposition were decoupled from manufacturing and production. For example, Nike, Zara, General Motors, Apple, and Dell are globally renowned for their products. These companies own the brand, research and development (R&D), and the intellectual rights of the products but they do not own the manufacturing. The generation of Industry 3.0 enabled production as a service. This was one major change over Industries 1.0 and 2.0. The organizational design was divided into more specialized functions such as marketing, finance, manufacturing, corporate Entrepreneurship, R&D, and IT. Management research became more scientific and started using more advanced methods for innovation. Consequently, management study was further classified into more specialized fields of study. Industry 4.0 is an incremental innovation over Industry 3.0 but with the integration of AI and IoT, the disruptive impact on productivity will be far greater than the previous generations of revolution. The term was first used among German manufacturing associations and corporations who were the first ones to rapidly adopt the Industry 4.0 technologies. Industry 4.0 platform is a German government-funded think tank that disseminates information on Industry 4.0. According to them, Industry 4.0 represents a further integration of computing, IT, robotics, Internet, big data, blockchain, and decentralized independent AI based machines which can take corrective decisions while manufacturing. In the world of Industry 4.0, people, machines, equipment, logistics systems, and products communicate and cooperate with each other directly. Production and logistics processes are integrated intelligently across company boundaries to make manufacturing more efficient and flexible (Schweichhart, 2016). According to the McKinsey and Company, the Industry 4.0 is the integration of the manufacturing sector with the IoT framework using the latest sensors, integrating production systems with the back-end supply chain and with the front-end market demand functions using analytic powers, and further reducing contractual risks by using blockchain technologies and the latest manufacturing techniques, eliminating human intervention all together (Wee, Kelly, Cattel, & Breunig, 2015). Management structures during Industry 4.0 are

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imagined to be more decentralized and flatter, albeit more specialized. Newer organizational functions such as expertise in data analytics are prominently getting updated into management. Industry 4.0 is an outcome of the integration of a series of innovative technologies of productivity, creativity, and Industry 3.0 into a single platform. The main scope of Industry 4.0 lies in the reorganization of industrial production methods by incorporating internet of things (IoT), Information and Communications Technology (ICT), big data and AI technologies, robotics, and RFIDs and integrating them with customer feedbacks in order to be (a) more green and sustainable in the industrial processes and supply chains, (b) create a new strategic advantage in the market, (c) reduce costs by using autonomous machines, and (d) ensure faster product development. For example, initially the RFIDs inside the warehouse were primarily used for tracking the inventory and optimizing warehouse management. However, now the scope of RFID has expanded. They have been tried, tested, and integrated with production systems. This integration has reduced the dependence on assembly line worker and increased the control and monitoring of assembly line production machines. The RFID attached to the work parts are designed to send the problem to the machine and, accordingly, the machine processes the parts into a finished product. The machined parts are then transported to another workstation by autonomous robots, eliminating the need of a fixed assembly line. Earlier, there was a dedicated staff to program and supervise the machines, fix the assembly line, and set the tools in workstations. However, now all these steps are automated, eliminating human interventions. With RFID and autonomous robots, the product design to manufacturing time has been significantly reduced (Whelan, 2016) . Figure 1. Evolution of Industry 4.0

Source: See also (Reinhard, G., Jesper, V., & Stefan, S., 2016; Wee, D., Kelly, R., Cattel, J., & Breunig, M., 2015).

Most research on Industry 4.0 is focused on the RAMI 4.0 framework (Schweichhart, 2016). The conversation is around how to implement Industry 4.0 business models and how these will increase the productivity; however, there is still no discussion on the strategic impact on the corporations post industry 4.0 implementation. Unlike in the previous generations where human intervention was high, in

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the ideal case, it is assumed that human intervention in the system of production would be completely eliminated. How would the corporations strategize this new trend? What capabilities would the corporations require in order to manage this? How would the market change subsequently? It is presumed that the overall strategic impact on corporates would change disruptively post Industry 4.0. This also calls for a deeper study at the social, corporate, and technology levels of the overall impact of Industry 4.0.

CORPORATE STRATEGY AND CORPORATE PERFORMANCE The Corporate Strategy is a set of guidelines and drivers that direct the decisions and actions of a corporation in order to improve the corporate performance in the short and long run under conditions of uncertainty (Porter, 1980a; Tallman, 1991). There are many corporate strategies such as market strategy, cost strategy, volume strategy, new product strategy, wage arbitrage, new market strategy, and non-market strategy all of which are undertaken to increase a corporations’ performance. The corporation engages with all these strategies at multiple levels to enhance the corporate performance. The role of a strategy is not limited to just increasing the revenues or decreasing the input cost. It also includes creating brand value and brand recall—New Market Entry, Credit Rating, ability to attract government subsidy, ability to win government projects such as Lock-Heed Martin or EADS, and greater market access. Thus, the corporate performance is not narrowly dependent on profitability but also includes wider measures such as profitability, survivability, credit rating, longevity, ability to attract public funding, brand value, and increasing market access and market penetration (Rao, 1994). Corporate strategies are driven by two critical antecedents (Grøgaard, 2012). First, the portfolio of resources it possesses forms a major driver of corporate strategy (Morgan, Vorhies, & Schlegelmilch, 2006). Corporate resources include tangible resources such as the reputation of the top management team, learning capabilities, patent portfolio, cash reserves, credit rating, market width and penetration, product development /manufacturing flexibility, and market knowledge. Second, the corporate strategy is developed by environmental characteristics, institutional norms, market cycles, and international and national trade treaties (Freeman, 1984; Tallman, 1991). Figure 2 represents a framework of corporate strategy and corporate performance. The role of corporate strategy is to take into account the present resources, understand the external market environment and corporate resources, evaluate the market dynamics, and develop strategies to manage the sustainability of corporate performance over short- and long-term in the changing market – industrial environment. As corporations adopt Industry 4.0, how would corporate strategy change and how would corporates strategize in a dynamic competitive environment?

POSITION OF INDUSTRY 4.0 WITH RESPECT TO CORPORATE STRATEGY AND CORPORATE PERFORMANCE The ability of any corporation to improvise upon its capabilities and strategize tend to help it survive longer (Ruiz-Ortega & García-Villaverde, 2008). Successful corporations such as IBM, GE, and BMW have shown remarkable ability to learn, survive, and grow. On the other hand, corporations (such as Nokia and Kodak), which were market leaders at some point in time but lacked the ability to iterate and strategize based on the changing market and technological environments ended in eventual failure (Binns, 249

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Harreld, & Tushman, 2014). While blindly absorbing a new technology may increase the risks of investment failure, failing to timely adopt will also lead to complete irrelevance in the market. Given the high impetus on the promise of Industry 4.0, many corporations are making substantial capital investments to strategize the future. The risk of irrelevance because of the lack of timely adoption outweighs the initial capital expenditure (Sosna, Trevinyo-Rodríguez, & Velamuri, 2010). Consequently, it is important for the corporations to reflect on their capabilities, their ability to learn and subsequently, prioritize actions accordingly. In this section, the chapter presents how the implementation and absorption of Industry 4.0 are dependent on the corporations’s capabilities. Further, the chapter shows how Industry 4.0 supports the corporate strategy and how it positively improves the corporate performance. Figure 2. Antecedents, Actions, and Outcomes of Corporate Strategy (Strategy Landscape) (Authors’ Source)

Impact of Corporate Resources on Industry 4.0 As has been observed in the previous industrial revolutions, each stage of the revolution brought new organizational designs and the requirement of a new type of a skill set. The complexities of Industry 4.0 would require new organizational capabilities and resources. Organizational resources and capabilities of production are closely interlinked in order to create a long and lasting shareholder value. The capabilities of production have to be holistically aligned with the corporate resources and corporate strategy in order to create sustainable competitive corporate performance (Arthurs & Busenitz, 2006). Before integrating Industry 4.0, corporations have to reflect upon their existing capabilities, market environment, and corporate strategy. Corporation’s capability to learn emerging technologies and map changing industrial and market landscape would greatly help in the successful absorption of Industry 4.0 technology.

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Proposition 1: Successful Implementation and Absorption of Industry 4.0 Is Highly Dependent on the Dynamic Capabilities of the Corporation In a highly competitive market, corporations would engage aggressively to seek leading positions. Industry 4.0 platform of technologies is a certain enabler in that direction. One of the reasons why Silicon Valley scores highly on creating and adopting technologies is that they have an easier access to highly trained human resources. One needs highly valued and qualified skilled human resources to drive, produce, and maintain the Industry 4.0 systems (Gatewood, Shaver, & Gartner, 1995). Corporations that are able to secure trained human resources would be better positioned in the competitive landscape. Thus, the successful implementation and absorption of Industry 4.0 platform of technologies are highly dependent on the human resources. The industry and governments need to work together to create human resources flexible for Industry 4.0 standards and requirements. Proposition 2: Successful Implementation and Absorption of Industry 4.0 Is Highly Dependent on the Human Resources of the Corporation The Upper Echelon Theory clearly says that the success of an organization is highly dependent on the cognitive and leadership capabilities of the top management teams (Carmeli, Schaubroeck, & Tishler, 2011; Mueller & Barker Iii, 1997). The more acceptable and adaptable the top management teams are toward implementing emerging technological advances, the probability to succeed in accepting the change would greatly increase. Thus, the successful implementation of Industry 4.0 platform of technologies is not only highly dependent on the corporate strategy and evolving market dynamics but also on the cognitive and leadership capabilities of the top management. For example, Kodak was once a market leader but soon found that its leaders could not adopt the existing business towards the changing technological landscape. Subsequently, the board had to reconfigure the top management team from outside the group (Gavetti, Henderson, & Giorgi, 2003). The mélange of new culture brought by the new leadership and the existing work culture among the incumbent managers created tensions within the organization. Eventually, Kodak lost its market share and position. Proposition 3: Successful Implementation and Absorption of Industry 4.0 Is Highly Dependent on the Cognitive and Leadership Ability of the Top Management Team The market width and depth greatly influence the corporations’ ability to learn from multiple (strong and weak networks) sources and hedge market risks. The weak ties framework suggests that corporations which capitalize their discerning knowledge sources faster tend to learn and adapt faster (Granovetter, 1983; Liu & Duff, 1972). They also tend to manage uncertainty and risks faster. The weak ties help corporations to identify the opportunities, resources, and expectations much earlier. In order to forge weak ties, they must have diversified portfolios and ability to forge asymmetric complementary ties at different levels. Both market depth within a given service/product point and market width across various domains may help them to identify opportunities early. Similarly, the breath and debt of market access help the them to manage the market risks and uncertainty better. Using similar logic, Industry 4.0 requires knowledge and resources from various domains including IoT, robotics, IT, manufacturing and blockchain. Corporations, which have the ability to identify the emerging technologies and opportunities early from the various discerning market sources, tend to learn faster and compete better. The Samsung 251

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Group is a highly diversified group operating in traditional logistics business as well as cutting-edge mobile technology business. Until 2008, Nokia (focused mobile technology firm) was a market leader and Samsung was a distant fourth. However, by 2012, Samsung was able to take over the market share of Nokia. Among the many reasons cited, diversified market and product strategy helped Samsung the most to outcompete Nokia. Proposition 4: Successful Implementation and Absorption of Industry 4.0 Is Highly Dependent on the Market Width and Market Depth of the Corporation While conducting a cost-benefit analysis, if the cost of incorporating the Industry 4.0 technologies turns out to be more than the potential increase in overall benefits to the corporation, then, the corporation may engage with industry 4.0 service providers. Hence, it is best for the corporations to observe, learn, and outsource their requirements than to develop in-house. If the asset specificity of Industry 4.0 technology is very high such that the capital expenditure and market risk outweighs the potential benefits, then the corporation should engage with Industry 4.0 service providers instead of buying and installing them within their own factory premises (Hendrikse & Veerman, 2001; Susarla, Barua, & Whinston, 2009). The cost and organizational dynamics of outsourcing the Industry 4.0 capabilities may have an entirely different effect on the corporate strategy (Englander, 1988; Grover & Malhotra, 2003). The risks of outsourcing the capabilities to industry 4.0 services providers may also be very high. While, the in-house development of industry 4.0 capabilities may significantly increase the costs, but, buying the industry 4.0 services may significantly weaken proprietary capabilities. Proposition 5: Successful Implementation and Absorption of Industry 4.0 Are Highly Dependent on the Asset Specificity, Frequency of Use, and Market Risk

Impact of Industry 4.0 on Corporate Performance The product differentiation strategy of any corporation is a strategy where it sells diversified range of products. For example, Mercedes sells different range and quality of cars. Subsequently, the diversified range of products increases the production and the marketing costs for the corporation. The biggest difference in Industry 4.0 generation and its predecessors is that it provides a higher degree of control, customization, and decentralized collaboration during production. The larger impact of these capabilities would be customer-centric products at lower costs (Wee, D., Kelly, R., Cattel, J., & Breunig, M., 2015). Corporations would be able to produce high quality, highly customized products to customers at a lower cost. Using Industry 4.0 capabilities, Corporations like Mercedes would be able to produce multiple ranges of models at a far lower production costs. The lower cost of production for Corporations would directly impact its financial bottom-line and increase its capabilities to provide a diversified range of products, therefore positively impacting corporate performance (Porter, 1980b, 1980c, 1985). Adoption of Industry 4.0 platform of technologies clearly provides a strong competitive edge to corporations engaging in product differentiation strategies and diversified product portfolios. Therefore, the chapter concludes that the Industry 4.0 platform of technologies greatly enhances the corporate performance.

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Proposition 6: Industry 4.0 Will Increase the Corporate Performance if It Is Coupled With the Product Differentiation Strategy of the Corporation If a corporation’s initial core strategy is cost leadership strategy, which involves selling a large number of products at lower prices, in that case, it would make strategic sense for it to invest in building resources that increase the output at a reduced price (Porter, 1980b, 1980c, 1985). The increases in production efficiency and volume strategy have a very high connection with increased corporate performance. Industry 4.0 capabilities are positioned to greatly reduce production costs. Thus, it makes strategic dexterity for the corporations to invest in Industry 4.0 capabilities. Corporations engaging in volume strategy must engage in cost-benefit analysis while making decisions on Industry 4.0 platform of technologies. Proposition 7: Industry 4.0 Will Increase the Corporate Performance If It Is Coupled With the Cost Leadership Strategy of the Corporation New product development requires design thinking, customer feedback, market study, and customer profiling. It also requires strong, flexible, and rapid prototyping capabilities such as 3D printing, cadcam design, and flexible machining capabilities. These are some of the capabilities that Industry 4.0 introduces to the current production systems (Qin, Liu, & Grosvenor, 2016). Industry 4.0 significantly enhances the ability to launch a product by integrating the design to prototype any product or service through customer requirements, market research, and, consequently, reducing the product development life-cycle time and cost considerably (Qin, J., Liu, Y., & Grosvenor, R., 2016). Data has shown that when the corporations launch new products at a higher rate they tend to have stronger performance over competitors (Pujari, Wright, & Peattie, 2003). Increased product development will positively impact the corporate performance (Rubera, Chandrasekaran, & Ordanini, 2016). Hence, accordingly, the corporate performance shall increase if Industry 4.0 is strategically integrated with the new product development strategy of the corporation. Proposition 8: Industry 4.0 Will Increase the Corporate Performance if It Is Coupled With the New Product Development Strategy of the Corporation The implementation of Industry 4.0 is positioned to massively increase the productivity and reduce the overall costs of the corporation (Wee, D., Kelly, R., Cattel, J., & Breunig, M, 2015). As the Corporation engages in more aggressive actions, its competitors would also engage in similar actions. Higher productivity in a competitive market will put pressure on the overall margins. Higher pressure on margins will make everyone worse off. Consequently, corporations would be forced to aggressively engage in actions and strategies to explore and exploit newer markets (March, 1991; Rodan, 2005). The new market exploration and exploitation strategy of the corporation will have a positive impact on its performance. Hence, in the long run, if a corporation chooses to aggressively engage with the Industry 4.0 platform of technologies, it will also have to invest and couple Industry 4.0 with the new market development strategies. Failing to do so may increase the risks of underutilization of production systems or may increase the inventory costs.

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Proposition 9: Industries 4.0 Will Increase the Corporate Performance if It Is Coupled With the New Market Development Strategy of the Corporate The prominent non-markets strategies of any Corporation include corporate social responsibility (CSR) expenditure, support to public projects and goods, social value creation through community engagement, investment in industry associations, strategic lobbying with the government, and strategically managing different social and political groups (de Figueiredo, 2010; Sandel, 2012). These strategic investments are executed to create trust with the public and the government. Industry 4.0 is there to increase the productivity at the employee and organizational levels. It does not share any common characteristic with the non-market strategies of the corporation. The non-market strategies have no commonalities with the Industry 4.0 technology. Consequently, incumbent corporations must not replace their focus on nonmarket strategies with an increased focus on Industry 4.0. Rather, the current media narrative against automation and digitalization, the corporations digitalizing must invest more on non-market strategies to manage their public relations. Proposition 10: Industry 4.0 Will Have No Impact on the Corporate Performance if It Is Coupled With the Non-Market Strategies of the Corporation Industry 4.0 will drastically increase labor productivity. It will also decrease the cost of organizational control. Consequently, the increased productivity may lead to a decreased requirement of manpower. The decreased requirement of the workforce may lead to an increased possibility of large-scale layoffs. The layoffs may create stress in the markets. Increased productivity will also lead to commodification, reduced margins, and higher stress in the markets. Consequently, this will lead to social, political, and economic tensions in the business-social environment, which will result in adverse consequences for the corporation (Flanagan & O’Shaughnessy, 2005). The negative spillovers because of increased productivity will also lead to reduced fund allocations and reduced tax incentives from the government. It can also lead to an increased governmental scrutiny of the enterprise’s activities. On the other hand, engaging the government and public about Industry 4.0 and its greater impact on market equilibrium may lessen these fears. Autonomous vehicles are one example of Industry 4.0 (Beede, Powers, & Ingram, 2017). It is a disruptive innovation but it also threatens millions of jobs (drivers and transporters) that are dependents on vehicles. The recent tests by Google and Uber have not only attracted interest from tech enthusiasts but also raised concerns about the future of jobs. Proposition 11: Industry 4.0 Implementation May Have a Negative Impact on the Corporate Performance if It Drastically Increases the Probability of Employee Layoffs or Market Competition Using the 11 propositions developed in the chapter, the authors propose a diagram describing the position of Industry 4.0 within the corporate strategy landscape (Figure 3). The current available scholarship on Industry 4.0 is limited to conceptual definitions, adaptation in the industry, and operationalization within the manufacturing plant. The eleven propositions presented here, therefore, provide a perspective on how the corporate strategy and thereby corporate performance might get influenced while adopting Industry 4.0 enabling technologies. It also adds value to the current literature on Industry 4.0 by bringing in the strong corporate strategy perspective. The authors also hope to create a dialogue between Industry 4.0 solution providers/creators, corporations and policymakers. 254

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Figure 3. Position of Industry 4.0 within corporate strategy landscape Source: (Authors’ Source)

CONCLUSION AND FUTURE OUTLOOK This chapter looks not only at the changing technologies disrupting the current industrial sector but also at the corporate strategy and where and how Industry 4.0 adds value to the corporate performance. Corporations that learn and adapt faster tend to survive longer. They are generally more profitable while those, which are slow, tend to lose out to the competition. Industry 4.0 is an upcoming platform of technologies that is revolutionizing the rate of productivity per employee. It also has the promise to dramatically reduce the cost of controlling and compliance incurred by corporations. Therefore, the future outlook of Industry 4.0 looks promising. However, governments and the civil society have yet to take into account the socio-political-economic spillovers. For instance, if the productivity of existing employees increases substantially with Industry 4.0, it would also require for the market demand to grow equally. In case of limited growth into the market demand, there will be a very high risk of an economic crash, market failure, and possible large-scale unemployment as was witnessed during the Great Depression in the 1930s and in 2007–2008. In order to manage uncertainty that might arise due to higher productivity and, therefore, unplanned layoffs, policymakers should study Industry 4.0 from different positions. They must study the consequential spillovers on the socio-economic equilibrium, labor productivity, market equilibrium, and the plan for any major market failure. They might also consider reorganizing schooling and training curriculums to help people secure employment.

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This study is primarily at the organizational level and discusses the position of Industry 4.0 within the broader corporate strategy. However, the study found that there is very little critical literature on the in-depth research on the growth of Industry 4.0 in the long run from a public policy perspective. Industrial revolution 1.0 and onward have greatly changed the productivity of corporations and consequently enhanced their profitability. The strategic resources of the corporations changed dramatically post Industry 3.0. In this era, the intellectual resources of the firm were decoupled from manufacturing. Industry 4.0 will now further change the outlook of intellectual resources, manufacturing and supply chains. Industry 4.0 would further reduce human intervention and decrease costs of controlling and monitoring. Given the lowering of wages, the rising unemployment in northern countries, and rising wages in southern countries, multinational companies are already restructuring their supply chains and manufacturing strategies. However, it is still to be seen how these restructuring would impact the labor markets in North and South. Presently, China is the production engine of the world, which exports goods to the rest of the world at very competitive prices. Will Industry 4.0 change this trend? Similarly, as Industry 4.0 gains further acceptance, the labor force of the new industrial age would be highly productive and will have higher wages compared to the present worker, given our knowledge of the previous industrial revolutions. How will the rise in wages impact the current economic equilibrium at the country and international trade levels? As industries adopt Industry 4.0 technology the uncertainties over employment, trade secrets, and core strength continue to persist. The research calls for further studies on Industry 4.0 and its impact on outsourcing. In order for the corporation to successfully absorb Industry 4.0 technology, they also need a highly skilled workforce. The research calls policymakers to relook at the technical education to incorporate the requirements of the new industrial revolution.

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KEY TERMS AND DEFINITIONS Corporate Competitiveness: Corporate competitiveness is the ability of the business to provide products and services as or more effectively and efficiently than relevant competitors. Corporate Performance: Corporate performance is how effectively and efficiently a given business organization is achieving the objectives written in the strategy document of the business. Corporate Productivity: Productivity is a measure used in many contexts. In this chapter, productivity is related to business performance. It is the ratio of output to input used in the production process. Corporate Strategy: Corporate strategy is a set of statements that guides the corporation’s decisions and actions to ensure its competitive position in the given industry and market. Industry 4.0: Industry 4.0 is also known as “Smart Factory.” It is a platform for increasing the productivity by systematically using multiple innovations in data science, automation, IoT, big data, robotics, modular manufacturing, and decentralized decision making by machines. Institutional Factors: Institutional factors are social structures (rules, culture, routines, norms, and conditions) formed by the government, markets, and society that influence the organizational behavior and functioning. Labor Market: Labor market refers to the supply and demand for labor in which employees provide the supply and employers the demand. It is a major component of any economy and is intricately tied in with markets for capital, goods, and services. Labor Productivity: Labor productivity is equal to the ration between a measure of output volume (gross value added) and a measure of input use (total number of hours worked). Market Forces: Market forces are the factors that influence the price and availability of goods and services in a market economy. Market forces push prices up when supply declines and demand rises, and drive them down when supply grows and demand contracts. Market Uncertainty: Market uncertainty is the situation where the current state of knowledge is such that (a) the order and nature of things are unknown, (b) the consequences, extent, or magnitude of circumstances, conditions, or events is unpredictable, and (c) credible probabilities to possible outcomes cannot be assigned.

This research was previously published in Analyzing the Impacts of Industry 4.0 in Modern Business Environments; pages 161-176, copyright year 2018 by Business Science Reference (an imprint of IGI Global).

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Understanding Digital Congruence in Industry 4.0 Berna Ulusoy Hochschule Fresenius University of Applied Sciences, Germany

ABSTRACT Industry 4.0 is a type of revolution that brings profound changes around the world. Industry 4.0 has both broadened the scope of digital transformation and raised its importance to organizations. The interconnection of digital and physical processes is fundamentally increasing. In this respect, digital transformation becomes a major driving force of Industry 4.0 for organizations. While Industry 4.0 presents key opportunities to boost competitiveness and promotes digital change, development of digital capabilities is significant for organizations to be better prepared to implement these advances. Thus, according to some authors, digital capabilities refer to digital congruence. Digital congruence relates to culture, people, structure, and tasks in organizations. It is therefore considered that explaining the link between digital congruence and Industry 4.0 will provide a unique insight into the research agenda of Industry 4.0.

INTRODUCTION Today, the digital change which is happening at an exponential pace, is dramatically affecting us. According to Klaus Schwab, we are at the beginning of a revolution that is fundamentally changing the way we live, work and relate to one another. This revolution, Industry 4.0 is characterized by a range of new technologies that are spreading around the world by impacting all disciplines, economies and industries. Rapid technological advances with new ideas make Industry 4.0 unique and these profound advances highlight the potential to connect billions more people to digital networks (Schwab, 2016). Transactions are being digitized, data is being generated and analysed in new ways, people and activities are connected (Iansiti & Lakhani, 2014). While the role of digital technology is changing, digital transformation provides opportunities for value creation and capture. The strategic implications of these changes are indispensably critical for organizations (Digital Transformation of Industries: Digital Enterprise, 2016). With the changes being so profound, adopting and implementing Industry 4.0 present DOI: 10.4018/978-1-7998-8548-1.ch014

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 Understanding Digital Congruence in Industry 4.0

a special challenge for organizations throughout the world. German Trade and Invest (GTAI) defines Industry 4.0 as the technological evolution from embedded systems to cyber-physical systems, an approach that connects embedded production technologies and smart production processes (MacDougall, 2014). Incredible progress of digital technologies becomes important and transformational for organizations. The transformation that has been brought about by digital technology is extremely beneficial because technology provides more choices (Brynjolffsson & McAfee, 2016). Industry 4.0 encompasses digital transformation of organizations around the globe. In other words, Industry 4.0 has both widened the scope of digital transformation and increased its significance for organizations. Industry 4.0 combines digital and physical technologies-artificial intelligence, Internet of Things, additive manufacturing, robotics, cloud computing and others, to foster more adaptive and interconnected organizations (Hagel, Brown, & Lui, 2013). Digital transformation can be defined as adopting business practices that help organizations to sustain their competitiveness. It is about how organizations adapt to digital trends as well as adapting to how customers, employees and competitors use digital technologies (Michelman, 2018). The ways organizations use digital technologies to drive their businesses forward are crucial to successful transformation. Thereby, using technology better than their competitors do, is important in building a digital advantage for organizations. While digital technologies are tools for organizations to transform their business processes, excelling in different dimensions play an important role in transforming digitally. Sustaining the momentum of digital transformation is crucial to the long-term success of organizations. In order to make the change possible, building new skills is necessary for organizations. Digital transformation demands integrating technology and business processes (Westerman, Bonnet, & McAfee, 2014). Moreover, the focus of digital transformation is both about strategy and new ways of thinking. Transforming for the digital age requires organizations to improve their strategic mindset and having a broader scope of business strategy (Rogers, 2016). In order to remain competitive, transformation efforts of organizations should be well designed. Acquiring the right capabilities plays a crucial role for organizations to better prepare for their digital future. MIT Sloan and Deloitte recently examined how organizations can prepare for and survive the digital future. In their study, emphasis is given to development of digital capabilities, in which organizations’ activities, people, culture and structure are compatible with a set of organizational goals. These digital capabilities refer to digital congruence. In the competitive landscape, organizations should consider a new concept which is called digital congruence, -culture, people, structure and tasks- aligned with each other and organization strategy and challenges of digital environment (Kane G. C., Palmer, Phillips, Kiron, & Buckley, 2016). The Congruence Model developed by Nadler and Tushman in the 1980s suggests, organizations to be systems that are made up of components or parts that interact with each other. The model is based on how well components fit together; that is the congruence among the components (Nadler, Tushman, & Hatvany, 1982). The Congruence Model is a powerful tool in order to analyse how well the key components of an organization interact. This means, organizational success depends on how the four key components -tasks, people, structure and culture- work well together. For instance, with a bureaucratic organizational culture, decision-making approaches will be problematic even if the organization follows the latest digital trends. The Congruence Model provides a framework for analysing organizational components in today’s digital age. By adopting the four components as digital congruence, culture, people, structure and tasks as seen in Figure 1; organizations may able to compete the digital transformation driving Industry 4.0. Organizations will be able to benefit from the digital congruence thanks to the visibility which Industry 4.0 has given them. To remain competitive and successful in today’s digital world, organizations should embrace the digital transformation Industry 4.0 261

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offers. As digital transformation plays a crucial role within Industry 4.0, to understand the components of digital congruence is equally important. Industry 4.0 continues to be at the top of the political and economic agenda of many countries in today’s world. Industry 4.0 is a system of digital advancement which represents challenges and opportunities for organizations. With Industry 4.0, new forms of competition emerge, and the role of digital congruence becomes critical. Mastering Industry 4.0 requires a deep understanding of digital congruence; thus, it is a crucial avenue for research. In this study, it is aimed to examine this new concept “digital congruence” in detail in order to provide an understanding of it. The impact of digital congruence on organizations will be reviewed by expanding the breadth of the concept. Key challenges and the opportunities for Industry 4.0 deployments will be covered by integrating knowledge from various case studies. Figure 1. Digital Congruence Components.

Adapted from Aligning the Organization For Its Digital Future. MIT Sloan Management Review and Deloitte University Press.

CULTURE Organizational culture refers to a system of shared meaning held by employees that distinguishes the organization from other organizations. The role of culture is important as it shows how employees perceive the characteristics of an organization. Culture has a boundary-defining role: It sets organizations apart from others. It inspires a sense of characteristic attributes or expected behaviours for employees. It represents common sets of norms for employees (Robbins & Judge, 2017). Culture influences employees’ attitudes and behaviour by being a control mechanism (Weber & Dacin, 2011). Culture has significant influence on organizations. In this respect, Industry 4.0 has also implications on organizational culture while organizations adopt new processes that are key to their success in the digital age.

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It is important for organizations to master the cultural changes necessary for digital transformation. The benefits of fostering a digital culture are substantial. Without a digital culture, it is obviously difficult for organizations to execute digital transformation. According to 2015 Digital Business Global Executive Study and research project by MIT Sloan and Deloitte, the ability to digitally transform the business requires a broader perspective of a clear digital strategy supported by leaders who promote a culture that is able to transform. Besides, what is unique to digital transformation is that risk taking becomes a part of digital culture (Kane G. C., Palmer, Phillips, Kiron, & Buckley, 2015). Strategy, leadership and risk taking are different factors that influence digital culture. All these factors have one thing in common: They support digital transformation. Thus, digital culture separates an organization from others and drives digital transformation. A recent study emphasizes the importance of a true digital mindset along with the right digital culture. It is stated that without the right digital culture, the best talent wouldn’t want to stay in the organization. Digital culture depends on a digital environment which is highly collaborative with supportive leadership (Geissbauer, Vedso, & Schrauf, 2016). In addition, a culture leading to the implementation of a strong digital strategy is critical for organizations. In order to succeed in building such a digital culture, there are specific areas that have great impact. The first one is the active management support. Digital culture requires support from upper management as well as support for a digital flow between management and employees. Furthermore, a vision of organization’s digital opportunities is critically important for leaders. The second area is about the interaction and collaboration in organizations. Digital culture helps to eliminate hierarchy between departments, functions and reporting lines. Instead, self-organized, cross-functional teams are created, and greater level of interaction is promoted in organizations (Bughin, Digital Success Requires a Digital Culture, 2017). Another important area is about risk taking. Digital culture encourages employees to take risks and to learn from failure. This type of culture fosters rapid decision making due to flatter hierarchy. Fast pace of work requires speed in a digital environment which is supported by digital culture (Hemerling, Kilmann, Danoesastro, Stutts, & Ahern, 2018). Overall, digital culture is one of the key elements of digital transformation. It is a reality that organizations are willing to invest in Industry 4.0 to enable digital transformation. An important part of this transformation is the cultural change in organizations. Adopting a digital culture is a key determinant of organizations’ ability to digitally transform.

People (Employees) Mastering digital transformation is a multi-level process and more than a technology transfer. While Industry 4.0 is evolving, it is an undeniable fact that the nature of work and employment has dramatically changed. Industry 4.0 represents a new kind of challenge for organizations as well as their employees. With the changing employee roles due to digital transformation and the emergence of new opportunities in Industry 4.0, it is to be expected that recruiting processes and career development become crucial. Some authors categorized the features that allow organizations to benefit from their employees into four, as being; skills, mindset and behaviour, talent sources and career development. As the need for skilled employees increases, it will be extremely important to develop new ways of attracting new talent as well as the adequate training programs for digital capabilities. This means, it becomes a necessity for organizations to provide their employees with resources and opportunities to develop their digital skills and capabilities. In order to determine these resources and opportunities, it is significant to understand how digital transformation affects the goals of an organizational strategy. By evaluating this circumstance, 263

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an organization can determine the necessary skillsets for its employees (Pillsbury, Geissbauer, Schrauf, & Lübben, 2018). Organizations success with Industry 4.0 is closely related to having employees with digital skills. It is a challenge for organizations to train existing employees and recruit new ones who have the necessary digital skills. The talented employees who are able to adapt and use the existing digital technologies are called digital talent. It is hard to find such digital talent for organizations that aim to successfully execute digital transformation. In other words, a digital talent gap exists due to the need for digital skills and digital roles in organizations. There are various strategies for organizations to more effectively recruit, develop and retrain digital talent. It is extremely important that talent strategies are aligned with organization’s goals. One of those strategies is to integrate digital talent into core business. In order to do that, many organizations create digital accelerator programs. The objective of these programs is to improve digital capabilities. Another strategy for organizations is to train existing employees. It very advantageous for organizations to train internal talent so that they develop new skills to adopt a digital mindset as well as digital tools. A third strategy is using bottom-up initiatives in order to find digital talent. Those initiatives include brainstorming sessions, suggestion boxes and competitions to determine potential employees and create ideas for digital solutions. If employees realize the importance of digital transformation and how they will benefit from the transformation process, then they will be willing to be involved in. Using these initiatives will help organizations to discover the digital talent they have (Dahlander & Wallin, 2018). Furthermore, organizations which understand the ways of dealing with Industry 4.0 technologies, need employees who require process and IT systems know-how in order to establish the link between the digital and physical world. Thorough understanding of overall processes, systems and data is a requirement for new business models and operational improvements in organizations. Thereby, data and process experts who can work at the interfaces between functions and systems, have become the talent organizations need (McKinsey&Company, 2015). Digital talent is one of the key components of digital transformation. For organizations, best practices of employment processes to more effectively recruit, develop and retrain digital talent gain importance. One of the factors that impacts organization’s success is the quality of employees. Thus, skilled digital talent becomes critical in adopting and embedding digital transformation processes.

STRUCTURE How organizations should be structured and the new skills required, are the prominent questions in the digital age (HBR, 2015). Smart, connected products are demanding organizations to revise and rethink, not only their strategies but also organizations’ structures. Lawrence and Lorch address that every organization structure must integrate two basic elements, which are differentiation and integration. Dissimilar tasks, such as sales and engineering need to be differentiated or organized into distinct units. In addition, the activities of those separate units need to be integrated to coordinate and align them, in a timely manner (Lawrence & Lorsch, 1967). Smart and connected products of Industry 4.0 have a major effect on differentiation as well as integration. As a result, organizations’ structures are changing gradually, and classic organizational structures are replaced by the new ones. New functional units and new forms of collaboration and integration between teams are emerging in organizations. These new kinds of units include unified data organizations, development-operations groups and customer success management units (Porter & Heppelmann, 2015). Therefore, organizations proceed to digitize in different dimensions. 264

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For the most part, organizations are going through different forms of business transformations in order to adapt digital transformation in Industry 4.0. In terms of redesigning internal operating models, organizations take various tasks from business units and set them in new shared-service organizations. Product-oriented business units are reorganized by removing sales and marketing functions and placing them in industry or location aligned units. Tasks are also outsourced and moved to offshore units. In terms of control and alignment mechanisms, the decisions that are made at different organizational levels become in sync with the strategies of the organization. These mechanisms are modified for flatter, more collaborative work environments rather than formal organizational hierarchies for command and control. As a result of less hierarchy, collaboration gains importance which can be considered as a big organizational change. An example of this organizational change is the agile method which refers to alternative methods of working. Agile methodology involves multidisciplinary teams, continuous level of collaboration, short set periods to complete specific work, new roles like “scrum master” and “product owner”. This methodology is different in concept from traditional ways of working and requires organizations to become more collaborative (Scantlebury, Ross, & Bauriedel, 2016). Besides becoming collaborative, organizations are forming their strategies around digital transformation. In order to take the advantage of digital transformation, differentiating strategies to support digital transformation gains importance. What drives digital transformation is a clear and coherent digital strategy. The scope and objectives of the digital strategy are significant. Organizations may consider their future visions and work backwards from their visions in order to develop more advanced digital strategies. Thus, the traditional strategy development process is reversed instead of planning organization’s next steps according to its present abilities. Digital strategies are developed with a focus on transforming the organization (Kane G. C., Palmer, Phillips, Kiron, & Buckley, 2015). Overall, one of the ways to successful digitization is related with the digital strategy. Thus, capturing the full potential of Industry 4.0 requires a deep understanding of collaboration and a clear strategy. In order to benefit from Industry 4.0, organizations need to transform, which requires change in organizational structures. Besides, organizational strategy plays an important role in building organizational structure that sets organizations apart.

TASKS Along with Industry 4.0, the way traditional workplaces function, has significantly changed. With the signs of the change, the emerging workplace suggests new perspectives. The key contours of this change are the workforce and the nature of tasks. Industry 4.0 has a significant impact on the workforce and demands new skills. As the skill requirements change, it becomes harder for organizations to find the right talent. The extent to which organizations benefit from Industry 4.0 will depend on their success in managing newly skill talent pools (Lorenz, Küpper, Rüssmann, Heidemann, & Bause, 2016). In the traditional way, employees are hired for well-defined jobs by organizations. Ultimately, the nature of the tasks wouldn’t change to a great degree, if employees shift a new role in the same organization. That is beginning to change as Industry 4.0 and digitalization is transforming jobs by changing the nature of the traditional tasks. Indeed, there has been the growth of contingent work which means various forms of employment related to the completion of a specific task in a relatively short amount of time. Contingent work covers employees in a variety of employment relationships including independent contractors who are self265

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employed (Barley & Kunda, Gurus, Hired Guns and Warm Bodies: Initerant Experts In a Knowledge Economy, 2006). The newest additions to this group of contingent employees are the ones who work in the so called “gig economy”. Employees in the gig economy participate in spot labour markets, except that “gig employees” typically find their jobs through online platforms and may never meet their employer. Gig employees can be highly skilled or somewhat low skilled. Due to the increase in the number of contingent employees, project-based forms of organizing are becoming ubiquitous across organizations. Started mostly in construction, consulting, aerospace and defence sectors, project work is turning out to be a substantial form of organizing in high-tech industries as well as other economic sectors (Barley, Bechky, & Milliken, The Changing Nature of Work: Careers, Identities, and Work Lives In the 21th Century, 2017). As a result, work in different functions is transforming to more project-based roles and activities. Digitalization enables those project-based jobs to have much shorter deadlines as well. Furthermore, due to the use of digital technologies, information can be transferred with ease and worldwide. Organizations can develop knowledge in-house, as well as acquire it through highly skilled freelancers that offer their knowledge capabilities through online platforms. Those platforms encourage freelance work and collaborate employees and organizations. Although the collaboration with contingent employees may be completely online, their outcome and performances need to be integrated within the organization (Lanzolla, Lorenz, Miron-Spektor, Schilling, & Solinas, 2018). As a matter of fact, organizations have started to become agile in the new landscape as the content of the existing tasks and skill requirements change. Indeed, with the changing skill requirements in Industry 4.0 age, recruitment processes have evolved, as well. Finding the right talent has become a challenge for organizations. Replacing long and complicated recruitment processes by freelance talent, has become a way to overcome this challenge. Tasks in organizations are customized and modularized to compete in the digital age (Kane G. C., Palmer, Phillips, Kiron, & Buckley, 2016). Moreover, online platforms are increasingly connecting employees to different career opportunities. While a growing number of work force use these platforms, their potential is increasing rapidly. Using job rating systems to get information on tasks and performance, online working platforms provides an effective way to measure abilities. These dynamics have consequences for the employees, organizations and the economy. Especially, online platforms play an important role in the digital economy. According to a McKinsey research, online platforms are not only cause people to enter the workforce again in flexible employment processes but also advance the matching of jobs and employees within and across organizations (Bughin, Lund, & Remes, Rethinking Work In The Digital Age, 2016). Therefore, nature of the tasks that employees perform, work force and skill requirements have radically changed across digital transformation process. The impact of these changes on organizations will cause them to establish new ways of working to implement Industry 4.0.

CASE STUDIES Key challenges and the opportunities for Industry 4.0 deployments will now be introduced by giving examples from various case studies. The organizations in case studies are succeeding in their digital transformation efforts toward Industry 4.0. They are from various industries. On the one hand, they use technology, on the other hand they develop digital capabilities in their digital transformation processes in order to implement Industry 4.0. These organizations invest in digital opportunities which is a key to 266

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success in Industry 4.0. The case studies include information from the websites of organizations as well as the business white paper requested from the organization.

Bosch and Industry 4.0 The digital transformation is affecting markets and competitors alike. It is changing our lives. Industry 4.0 and the accompanying digital transformation of processes allow organizations to act at even greater speeds and, above all, with even greater customer orientation: Prototypes are easy to test, customer feedback is available promptly, and manufacturability findings are acquired without effort. This shortens product life cycles, while raising demand for customized products. Consequently, individual manufacturing processes become more adaptable. To meet these requirements, organizations developed flexible and adaptable manufacturing processes, by data exchange between machines and their environment. Thus, the physical world of production comes closer to the virtual world of information technology. By this way, organizations become more flexible in the digital world. Bosch regards this digital transformation as an opportunity to shape the future. The Bosch Group’s strategic objective is to create solutions for a connected life. The connectivity of physical things and new services allows organizations to create new business models that are key drivers for the digital transformation of businesses. More than simply connecting things, with the Internet of Things and digital transformation, there are intelligent automated systems reaching into every corner of the world. The Internet of Things is not new, but it radically changes businesses and disrupt industries. It is very clear that the IoT is not just about technology, it is much more than that. It is the catalyst for digital transformation that creates business models. Overall, Internet of Things is both the driver behind and target of Bosch’s business activities. Bosch has a unique perspective. At Bosch, the focus is always to make sure the technologies they build are human and humane in nature. In Industry 4.0, people are the key players. Enabled through technology, work is getting more efficient, but machines will continue to play the subordinate role. Employees’ work is facilitated to a greater degree than by software-based systems (Innovations, 2018). Therefore, Bosch is not only a leading user of Industry 4.0 but also a leading provider. In consistent with this strategy, Bosch has its own Industry 4.0 solutions. In order to support these solutions, Bosch is focusing on its activities on Bosch Connected Industry. With Industry 4.0, manufacturing organizations require increased efficiency through transparency and traceability. Intelligent Industry 4.0 software solutions and services provide consistent transparency and efficiency in processes. Bosch Connected Industry combine these solutions in an extensive portfolio. The objective of these solutions is to make employees work, production and logistics easier. Industry 4.0 software solutions and services are supplied to their own Bosch plants around the world and to their customers. The software solutions are tested inside their plants. With many plants and warehouses all over the world, Bosch has many years of manufacturing expertise. This experience is integrated into their advanced product development as well as practical solution development (www.bosch.com, 2018).

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Daimler and Industry 4.0 Daimler AG sees Industry 4.0 as the digital transformation of the entire value chain. Physical and digital processes are connected to an increasing extent. Daimler is also connecting the entire automotive value chain including design, production and sales and service. While Industry 4.0 is evolving, the ultimate goal of Daimler is the “Smart Production”. Daimler sees that digital transformation will provide a route to advanced changes in the industry. These changes will influence processes of developing, planning and producing their vehicles. In Daimler, smart network of human, machine and industrial processes is seen as the future of production. The view of Daimler for the smart production is aligned with a great degree of flexibility. Respond time in production to market changes and customer demands is quicker due to the smart production. Other characterizations of smart production are being efficient and fast. Fully flexible production of vehicles is optimized and quality of control of processes are improved. In Full-Flex plant, several vehicle models can be flexibly produced on the same production band. The plant is continually digitized according to Industry 4.0 and the production networks around the globe are connected to the plant as well. Material flow and quality control processes are digitally interconnected. Besides, green production is given great importance. Employees are supported by different devices in processes. The workplaces are designed as being ergonomically optimized. By this way, employees work at ergonomically optimized workstations. Human-machine cooperation enables to relieve employees by making partial automation feasible. Overall, an employee-centric workplace is created, paying attention to employees’ individual requirements. Work organization, logistics and quality control processes are supported digitally. Digital transformation centers in different areas. One of them is 360-degree-networking from the supplier to the customer. Another one is the digital value chain from development and design to production and customers. For research and development as well as production, 360-degree networking is used so that communication across all units is quick and transparent. Thus, by 360-degree networking, smart production can integrate the real world into a digital one. Processes and systems are mapped in real time along with the factory. Transforming to a fully networked production is the priority of Mercedes-Benz. For instance, production processes are visualized and optimized by digital tools such as “Virtual Reality”. Furthermore, to advance the digital transformation, Daimler has successfully implemented a digital organizational strategy. With this strategy, Daimler aims to shape the digital transformation. Moreover, it gives its employees the chance to shape the digital transformation by involving them in the digital transformation process. At the cultural level, Daimler adapts its culture to the challenges that digital transformation brings. Connected collaboration is promoted between employees. With digital transformation efforts, Daimler proceeds with the digital change by letting employees experience technologies and supporting employee collaboration. Daimler wants to be the leader of their industry in terms of digital transformation. Accordingly, they support their employees in every aspect to “think digitally”. Employees are prepared for the latest technologies. In order to do that, training with a digital transformation focus is given to employees. Digital transformation is seen as an opportunity. Therefore, online learning platforms and working with robots, 3D printing and Augmented Reality are part of the training programs. With digital transformation and automation, trainings provide meaningful learning experiences for employees. Moreover, a culture of diversity in the workplace is built by the help of these trainings in Daimler. The objective of Daimler is to harmonize their internal mindset with external structure in order to react to the dynamic business environment properly. To support this approach, Daimler focuses on an 268

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organizational structure that enables fast and agile operation. With the help of Industry 4.0, the organization wants to successfully transform from a leading automaker to a leading provider of mobility. Their success is aligned with their consistent continuation of strategy (www.daimler.com, 2018).

BASF and Industry 4.0 Digital transformation presents great opportunities for BASF. By adopting digital technologies and data, BASF improve the efficiency and effectiveness of its processes. One of BASF’s core strengths is “Verbund” integration. The focus of Verbund is the physical integration of production, market platforms and technologies along with BASF’s resources and expertise. The organization functions in most of the countries around the world with its six Verbund sites and more than 300 available production sites. BASF benefits from physical, technological, market-related and digital Verbund advantages. Moreover, digital transformation helps BASF to use its existing resources efficiently, with “Power Plant 4.0”. It is an initiative to improve efficiency in organization’s activities. Thus, the initiative is a driver of digital transformation in BASF. An example of digital solutions, which contribute to the efforts of improving solutions and products of BASF, is smart manufacturing. The “Augmented Reality” application supports employees at plants in their daily tasks. Via specially equipped mobile devices, like tablets and smartphones, necessary information is easily accessed through the application. By this way, employees are supported in their workplaces. BASF experiences important benefits of Augmented Reality in the area of simulation, communication and digital learning use cases associated with manufacturing and industrial processes. A primary goal of BASF is to make augmented reality available for use in its plants worldwide. Predictive maintenance is another application which aims to optimize the coordination of maintenance and production processes. It predicts the optimal time for maintenance measures. Available live data is modelled and evaluated by a software. Then, the data is used for maintenance work and forecasting in production processes. Another area driven by digital transformation is research and development in BASF. High-performance computers are used for simulation and modelling. BASF researchers have the chance to advance totally new approaches by the help of the computer, named Quriosity. It is BASF’s super computer and offers approximately 10 times the overall computing power than before. With Quriosity, variations of more parameters along with sophisticated models can be done. It can perform immense number of calculations. With a high degree of computing power and input data, chemical processes are simulated. This increases BASF’s efficiency and paves the way for new discoveries. Moreover, Quriosity is connected to Verbund, which has a significant role in production. Quriosity is a useful tool which supports various processes including research and development, production, logistics and new digital business models. In addition, digital transformation gives researchers at BASF, a great number of opportunities to implement their unique and creative ideas. Digital technologies are integrated into the workflow of research and development departments. Direct access to knowledge-based systems allows analytical approaches to be used for problem solving. Thus, digital transformation offers a lot of potential in research and researchers in BASF benefit from it. For instance, working with the data on catalysts enables the researchers to have detailed information regarding the prediction of catalysts’ performance and lifetime. Another example is related with crop protection. BASF uses digital transformation to provide solutions for crop protection. It is significant to optimize agricultural production in order to improve the quality of life and meet the needs of future generations.

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BASF is also using digital transformation in its logistics. Autonomous and automatically driven vehicles are used in their plants. As means of digital logistics, these vehicles enable to save significant amount of time in delivery. Besides, augmented reality applications support information flow in the plants. Information is shared via specific tablets on site. Employees are supported by digital technology trainings. BASF also use smart energy network. The aim is to boost efficiency with this special software. Information on production and sales is analysed via the software and used to forecast energy requirements. At BASF, employees are the key to digital transformation. The digital transformation approach is one of the drivers of organization’s success in Industry 4.0 (www.basf.com, 2018).

FUTURE RESEARCH DIRECTIONS Industry 4.0 has a very high potential to impact the organizations in different ways by providing opportunities for improving business processes and operational effectiveness. If organizations want to be aware of the advantages in these areas, the need to build the necessary digital capabilities. This is a challenging task that requires many changes throughout the organization. Not only the processes, but also the strategy and the capabilities will need to change. Organizations should act in a timely manner in order to adapt to the digital environment (McKinsey&Company, 2015). In order to succeed in Industry 4.0, organizations should start their digital transformation efforts immediately. The success of organizations in Industry 4.0 depends on digital transformation, so organizations should invest in their digital capabilities. Adapting the components of digital congruence has a significant importance for the digital future of organizations.

CONCLUSION In this chapter, digital transformation efforts of organizations in order to benefit from Industry 4.0 are reviewed. Industry 4.0 and the accompanying digital transformation are dramatically changing the world we live in. Industry 4.0 and digital transformation create a world by connecting people, organizations and systems. An interconnected environment is created in which opportunities and challenges exist. In order to benefit from the opportunities of Industry 4.0, organizations need to adapt to digital environment. Digital changes occur at a great speed in this environment. The expectations for Industry 4.0 require organizations to embrace these digital changes which relate to all areas of their organizations. Digital and physical processes are becoming increasingly connected and capabilities of organizations play an important role in this process. The main objective of this chapter is to provide an understanding of digital congruence in Industry 4.0. Digital capabilities of organizations are named as “digital congruence”. The term, digital congruence refers to culture, people, structure and tasks of an organization. Digital congruence is a new term. The focus of digital congruence is that every component of it, should be aligned with each other as well as organizational strategy. The purpose of this chapter is to analyse how each organizational component relate to digital transformation along with Industry 4.0. A new perspective to digital transformation and Industry 4.0 for organizations is reflected in this chapter. As digital transformation leads to fundamental changes in the world, it effects different processes and structures in organizations. To remain successful and competitive in the digital world, it is important for organizations to understand the impact of Industry 270

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4.0 on the components of digital congruence. A perspective of Industry 4.0 and digital congruence as a strong predictor of success for organizations is reflected in this chapter. Moreover, this chapter discusses the components which are tasks, people, structure and culture, in depth. The chapter aims to build a deep understanding of how organizations adapt the components in order to transform digitally. While Industry 4.0 is seen as an evidence of a fundamental shift, it represents the ways of how digital technologies would be embedded within organizations. Digital technologies help the organizations to transform the way they work including tasks, employees, structure and culture. The ability to orchestrate the components is the key to a successful digital transformation. Digital technologies are shaping and reorganizing the organizations. New ways of working and changing skills of the employees are supported by digital technologies. While the way how organizations work changes, tasks are customized accordingly. They are mostly grouped and divided between teams. New ways of working include project-based roles and freelance work. Organizations become more agile, while content of the existing tasks change. With the changing employee roles, employees need different skill sets in the digital era. Thus, to provide appropriate training programs for employees become significant. By these trainings, employees have the chance to operate and manage Industry 4.0 solutions. Furthermore, new ways of finding new talent is a challenge for organizations. Organizations require employees with relevant skill sets to leverage digital transformation. The lack of such talent can be countered by continuous in-house training to acquire the required digital skills. Organizational culture is an important success factor for organizations as well. While defining a digital strategy is critical, promoting a culture that supports the strategy is equally important. Thus, culture should be in line with organizational strategy. In addition, lack of digital culture is a major problem to digital success. Transforming the organizations digitally is most likely related to developing and sustaining digital culture. While digital transformation is connecting the world, the technological developments are creating new types of businesses. These have an influence on the structure of organizations. The transformation causes a radical change in organizations in terms of speed and flexibility. Agile ways of working in organizations gain importance with new functional units and new forms of collaboration. Integration between teams makes them work on solutions across the organization. That means, rather than formal organizational hierarchies, the teams in organizations focus on collaboration. The changed organizational structure involves enhanced collaboration as a result of less hierarchy. Overall, this chapter presents how digital transformation and implementing Industry 4.0 will impact the digital capabilities of organizations. Responding to change in Industry 4.0 requires keeping track of the latest technological developments. It has become an absolute necessity for organizations in their journey of digital transformation. As the way organizations are structured changes, organizations adopt completely new ways of doing work. Not only organizational structure but also organizational culture, tasks and employees are parts of the change. The organizations which successfully navigate the change, will have many opportunities to succeed in Industry 4.0. Speed, unpredictability, collaboration, change characterize the new environment of digital transformation. The broader implications and positive impacts of digital transformation are significant for organizations. Alignment around structure, tasks, employees and culture will ensure digital transformation adoption to succeed in Industry 4.0.

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REFERENCES Barley, S. R., Bechky, B. A., & Milliken, F. J. (2017). The Changing Nature of Work: Careers, Identities, and Work Lives In the 21th Century. Academy of Management Discoveries, 3(2), 111–115. doi:10.5465/ amd.2017.0034 Barley, S. R., & Kunda, G. (2006). Gurus, Hired Guns and Warm Bodies: Initerant Experts In a Knowledge Economy. Princeton University Press. Brynjolffsson, E., & McAfee, A. (2016). The Second Machine Age: Work, Progress and Prosperity In a Time of Brilliant Technologies. New York: W. W. Norton and Company. Bughin, J. (2017). Digital Success Requires a Digital Culture. McKinsey & Company. Bughin, J., Lund, S., & Remes, J. (2016). Rethinking Work In The Digital Age. The McKinsey Quarterly. Dahlander, L., & Wallin, M. (2018). The Barriers To Recruiting and Employing Digital Talent. Harvard Business Review, 2–5. Digital Transformation of Industries: Digital Enterprise. (2016). World Economic Forum. Geissbauer, R., Vedso, J., & Schrauf, S. (2016). Industry 4.0: Building The Digital Enterprise. PWC. Hagel, J., Brown, J. S., & Lui, M. (2013). From Exponential Technologies To Exponential Innovation. Deloitte Insights. HBR. (2015). Designing A Marketing Organization For The Digital Age. Harvard Business Review and Marketo. Hemerling, J., Kilmann, J., Danoesastro, M., Stutts, L., & Ahern, C. (2018). It’s Not a Digital Transformation Without a Digital Culture. BCG. Iansiti, M., & Lakhani, K. R. (2014). Digital Ubiquity: How Connections, Sensors, and Data are Revolutionizing Business. Harvard Business Review. Innovations, B. S. (2018). How the Internet of Things Drives Digital Transformation and Customer Success. Berlin: IoT Newsletters. Retrieved from https://www.bosch-si.com/media/bosch_si/services/ whitepaper_1/boschsoftwareinnovations_businesswhitepaper.pdf Kane, G. C., Palmer, D., Phillips, A. N., Kiron, D., & Buckley, N. (2015). Strategy, Not Technology, Drives Digital Transformation. MIT Sloan Management Review and Deloitte University Press. Kane, G. C., Palmer, D., Phillips, A. N., Kiron, D., & Buckley, N. (2016). Aligning The Organization For Its Digital Future. MIT Sloan Management Review and Deloitte University Press. Lanzolla, G., Lorenz, A., Miron-Spektor, E., Schilling, M., & Solinas, G. (2018). Digital Transformation: What Is New If Anything? Academy of Management Discoveries, 378–387. Lawrence, P. R., & Lorsch, J. W. (1967). Organization and Environment. Boston: Harvard Business School Press.

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Lorenz, M., Küpper, D., Rüssmann, M., Heidemann, A., & Bause, A. (2016). Time To Accelerate. In The Race Toward Industry 4.0. Boston Consulting Group. MacDougall, W. (2014). Industrie 4.0 Smart Manufacturing For The Future. Germany Trade and Invest. McKinsey & Company. (2015). Industry 4.0: How To Navigate Digitization of the Manufacturing Sector. McKinsey Digital. Michelman, P. (2018). How To Go Digital-Practical Wisdom To Help Drive Your Organization’s Digital Transformation. Cambridge, MA: MIT Sloan Management Review. Nadler, D. A., Tushman, M. L., & Hatvany, N. G. (1982). Managing Organizations: Readings and Cases. Boston: Little Brown. Pillsbury, S., Geissbauer, R., Schrauf, S., & Lübben, E. (2018). Global Digital Operations 2018 Survey: Digital Champions. How Industry Leaders Build Integrated Operations Ecosystems To Deliver End-toend Customer Solutions. PWC. Porter, M. E., & Heppelmann, J. E. (2015). How Smart, Connected Products Are Transforming Companies. Harvard Business Review, 96–114. Robbins, S. P., & Judge, T. A. (2017). Organizational Behavior. Harlow, UK: Pearson Education Limited. Rogers, D. L. (2016). The Digital Transformation Playbook. New York: Columbia University Press. doi:10.7312/roge17544 Scantlebury, S., Ross, J., & Bauriedel, W. (2016). Designing Digital Organizations. Boston: MIT CISR & BCG. Schwab, K. (2016). The Fourth Industrial Revolution. New York: Crown Business. Weber, K., & Dacin, M. T. (2011). The Cultural Construction of Organizational Life. Organization Science, 22(2), 287–298. doi:10.1287/orsc.1100.0632 Westerman, G., Bonnet, D., & McAfee, A. (2014). Leading Digital: Turning Technology Into Business Transformation. Boston: Harvard Business Review Press.

KEY TERMS AND DEFINITIONS Agile Methodology: A type of methodology that involves multidisciplinary teams, continuous level of collaboration, short set periods to complete specific work in organizations. Congruence Model: A framework in order to analyze how well the key components of an organization interact. Digital Congruence: A new concept which is the culture, people, structure, and tasks of an organization aligned with each other and organization strategy and challenges of digital environment. Digital Culture: A type of culture that separates an organization from others and executes digital transformation.

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Digital Talent: The talented employees who are able to adapt and use the existing digital technologies. Digital Transformation: Transformation related to how organizations adapt to digital trends as well as adapting to how customers, employees, and competitors use digital technologies. Industry 4.0: The technological evolution that connects embedded production technologies and smart production processes.

This research was previously published in Business Management and Communication Perspectives in Industry 4.0; pages 1731, copyright year 2020 by Business Science Reference (an imprint of IGI Global).

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Knowledge Transfer and Introduction of Industry 4.0 in SMEs: A Five-Step Methodology to Introduce Industry 4.0 Dominik T. Matt Free University of Bolzano, Italy Erwin Rauch Free University of Bolzano, Italy Michael Riedl Fraunhofer Italia Research, Italy

ABSTRACT Industry 4.0 is for most companies and especially for small and medium sized enterprises (SMEs) one of the major challenges after the wave of lean management. The aim of this chapter is to provide a methodological guidance for the practical use of the Industry 4.0 vision and principles in production system design in the specific context of SMEs. Based on the analysis of literature, a procedure model for the target-oriented introduction of Industry 4.0 principles in SMEs is proposed. A first practical evaluation of the approach is carried out based on two industrial case studies. The experiences made in the industrial cases show that Industry 4.0 is not limited to the application in large enterprises but is very suitable also for SME. This chapter contributes, with its case-study-based methodology, to the existing sparse knowledge on the introduction of Industry 4.0 in SME production systems.

DOI: 10.4018/978-1-7998-8548-1.ch015

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 Knowledge Transfer and Introduction of Industry 4.0 in SMEs

INTRODUCTION Many companies in various industries have reorganised their production in the recent past, following the principles of lean production or even taking advantage of novel production strategies such as agile manufacturing and mass customisation (Brettel, M., Friederichsen, N., Keller, M., & Rosenberg, M., 2014), and thereby increasing flexibility and achieving significant progress in productivity and in readiness for delivery. However, the demands are growing: the trend towards individualisation of products is increasing, efficiency-enhancing serial or even mass-production concepts are increasingly being pushed back, and classic approaches to automation are failing in the demand for a production up to the ‘lot size 1’. Industrial production is faced with a profound change and the question of the quadrature of the circle: Is automation of individuality possible? (Matt D. T., Rauch E., & Fraccaroli D., 2016). A popular term, ‘Industry 4.0’ is currently broadly debated both by researchers and practitioners as a visionary concept that promises to resolve this conundrum. Industry 4.0 technologies also facilitate the fabrication of customised products and, thus, the concept of mass customisation (Matt & Rauch, 2017). However, the majority of small and medium-sized enterprises (SME) are still quite sceptical regarding this vision and many of them doubt the benefits (Matt D. T., Rauch E., & Fraccaroli D., 2016). Industry 4.0 represents a special challenge for businesses in general and for SMEs in particular. The readiness of SME to adapt to Industry 4.0 concepts and their organisational capability to meet this challenge exists only in part. The smaller SMEs are, the higher the risk that they will not be able to benefit from this revolution (Sommer, 2015). SMEs are conscious about the knowledge in adaption deficits and this opens the need for further research and action plans for preparing them for Industry 4.0. This chapter proposes a methodology for an efficient transfer of knowledge in the context of Industry 4.0 and the related cyber-physical production systems, and is derived from a careful literature research and evaluated through two industrial case studies, wherein Industry 4.0-relevant knowhow and concepts were transferred from research into the industrial practice of small and medium-sized enterprises. The case analysis shows that Industry 4.0 is not only accessible for large high-tech-enterprises. However, the implementation must be gradual along a clearly defined strategy. Thus, the purpose of this chapter is to propose a methodological guidance from a focused knowledge transfer to the practical implementation of the Industry 4.0 vision and principles in production system design and in the specific context of small and medium-sized enterprises. It should serve as a starting point for broader and more detailed study regarding research on how to implement Industry 4.0 in industrial enterprises and, especially, in SMEs. The chapter is structured as follows: after the initial introduction in Section 1, the authors analyse the state of the art in the implementation of Industry 4.0 principles and the Smart Factory concept in SMEs. Section 3 describes the research methodology, the objectives of this research and the proposed implementation approach consisting of five successive steps. In Section 4, the authors describe the application of the 5-step implementation approach in two industrial case studies. In Section 5, a critical discussion follows and shows the advantages, but also the current limitations, of the proposed approach. This section addresses future direction for further research. Finally, the chapter ends with a brief summary and conclusion.

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THEORETICAL BACKGROUND This literature review section is organised in three parts. First, the terms ’Industry 4.0’, ‘Smart Factory’ and ‘Cyber-Physical System’ are explained to provide a basic knowledge of these concepts. The second part describes the extension of the Smart Factory concept in small and medium-sized enterprises, while the third part highlights the importance of knowledge transfer in Industry 4.0 competencies and then formulates the objective of this research.

Industry 4.0 and Smart Factory Based on advanced digitalisation within factories, the combination of Internet technologies and futureoriented technologies in the field of ‘smart’ objects (machines/products) is likely to result in a new paradigm shift in industrial production, known as Industry 4.0 (Lasi, H., Fettke, P., Kemper, H. G., Feld, T., & Hoffmann, M., 2014). Following the mechanisation, electrification and computerisation of industrial production, we are now on the verge of the fourth industrial revolution with the arrival of the Internet of Things and Services. The term ‘Industry 4.0’was first introduced at the Hannover trade fair in 2011 (Kagermann, H., Lukas, W. D., & Wahlster, W., 2011) as an expression for the so-called ‘fourth industrial revolution’ and essentially addresses the technological integration of Cyber-Physical Systems (CPS) in the production process which enables (Internet-based) networking with all partners in the value chain (Schröder, 2016). To date, the term has been mainly used in popular science and has not been broadly established in the scientific literature (Brettel, M., Friederichsen, N., Keller, M., & Rosenberg, M., 2014). A review of most recent scientific literature identifies four key components of Industry 4.0: Cyber-Physical Systems, Internet of Things (IoT), Internet of Services (IoS) and Smart Factory (Hermann, M., Pentek, T., & Otto, B., 2015). In the following, these terms are explained to facilitate a better understanding of Industry 4.0 and the related topics. A cyber-physical system (CPS) is a combination of computers and physical systems. CPS is about the intersection, not the union, of the physical and the cyber. It combines engineering models and methods with the models and methods of computer science (Lee, 2015). Connecting physical world objects (made of atoms) with information (packaged as bits) may segue to another revolution, predicted by many, including Neil Gershenfeld, the director of MIT’s Center for Bits and Atoms (Datta, 2015). CPS positively affects manufacturing in the form of Cyber-Physical Production Systems (CPPS) in process automation and control (Monostori, 2014). Cyber-Physical Systems are also enablers for changing business logic and value chains, with the conjunction of cyber-physical systems and the development of new business models, facilitating the creation of disruptive innovations (Rauch, E., Seidenstricker, S., Dallasega, P., & Hämmerl, R., 2016). CPS have to be synchronised one with another and with the external world to share information and trigger actions, first by homogenising and integrating the communication systems through necessary hardware and middleware, and, second, by constructing a centralised backbone network where all valuable information is collected for further big data analysis (Rojas, R., Rauch, E., Vidoni, R., & Matt, D. T., 2017). Thus, the Internet of Things (IoT) plays a major role in the implementation of Industry 4.0. The Internet of Things, also known as the Internet of Objects, refers to the networked interconnection of everyday objects, which are often equipped with ubiquitous intelligence. IoT will increase the ubiquity of the Internet by integrating every object for interaction via embedded systems, leading to a highly distributed network of devices communicating with human beings as well as other devices (Xia, F., Yang, 277

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L. T., Wang, L., & Vinel, A., 2012). Regarding the introduction in the context of Industry 4.0, the term has been extended to the Industrial Internet of Things (IIoT). IIoT includes emerging technologies such as industrial wireless networks (IWNs), big data, and cloud computing, which will bring great opportunities for promoting industrial upgrades and even allow the introduction of the fourth industrial revolution (Wan, J., Tang, S., Shu, Z., Li, D., Wang, S., Imran, M., & Vasilakos, A. V., 2016). IIoT combines physical manufacturing and value creation with information processing, providing physical objects that traditionally lie in industrial environments with cognitive, perception or actuation, communication and autonomy capabilities (Törngren, M., Bensalem, S., Cengarle, M. V., Chen, D.-J., McDermid, J., Passerone, R., Sangiovanni-Vincentelli, A., & Runkler, T., 2014). The Internet of Services (IoS) presents a paradigm in which everything is available as a service on the Internet. It can also be viewed as networked services or systems across the real and virtual worlds over the Internet. In the IoS, services – as encapsulated functional entities containing interaction processes by service providers and customers – are redistributed, virtualised and converged over the Internet to meet the requirements of and create value for customers (Xu, X., Sheng, Q. Z., Zhang, L. J., Fan, Y., & Dustdar, S., 2015). An example of IoS is cloud computing. Cloud computing will play a major role in the future Internet of Services, enabling on-demand provisioning of applications, platforms and computing infrastructures (Moreno-Vozmediano, 2013). With Industry 4.0, companies will cross-link their products, machines, storage, handling and transport systems as well as other operating equipment such as cyber-physical systems (CPS) in a worldwide network of so-called ‘Smart Factories’ (Kagermann, H., Wahlster, W., & Helbig, J., 2013). The term Smart Factory is not consistently defined. Radziwon et al. (2014) identify several interchangeable expressions, such as U-Factory (ubiquitous factory) (Yoon, J. S., Shin, S. J., & Suh, S. H., 2012), Factory-of-Things (Lucke, D., Constantinescu, C., & Westkämper, E., 2008), Real-Time Factory (Zuehlke, 2010), or Intelligent Factory of the Future (Hameed, B., Durr, F., & Rothermel, K., 2011). In the Smart Factory, a completely changed production logic is applied: the products themselves become ‘smart’. Such ‘smart objects’ are not only able to store data and information about their location, their current state and their surroundings (e.g. via radio frequency identification - RFID), but also provide them with real-time decision-making logic, which allows them to independently find the way through their production and logistics environment. They are, thus, clearly identifiable and localisable; they know their history, their current state and their target state, as well as alternative ways to achieve it. In doing so, they must interact with their production and logistics environment. Products need to communicate with machines, and these machines and other resources must, in turn, exchange information and subsequently initiate processes autonomously (Einsiedler, 2013; Distefano, S., Banerjee, N., & Puliafito, A., 2016). Based on the literature review and the definitions of CPS, IoT, IoS and Smart Factory, the authors derive the following definition of Industry 4.0: Industry 4.0 is a collective term for technologies and concepts of value chain organisation. Within the modular structured Smart Factories of Industry 4.0, CPS monitor physical processes, create a virtual copy of the physical world and make decentralised decisions. Over the IoT, CPS communicate and cooperate with each other and humans in real time. Via the IoS, both internal and cross organisational services are offered and utilised by participants of the value chain (Hermann, M., Pentek, T., & Otto, B., 2015). Industry 4.0 and the comprehensive IT integration in production are regarded as the central theme for the intelligent factory of the future (Bauer, W., Schlund, S., Marrenbach, D., & Ganschar, O., 2014). 278

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The theoretical approaches and necessary technologies are now widely available. The competition will take place through the integration into industrial practice, the development of economic applications and the definition of new business models (Gausemeier, 2015). These productivity potentials cannot be achieved by simply by the purchase of emerging Industry 4.0 technologies, but require an individual adaptation to the specific conditions in the company and the connectivity between each of the technologies. For the efficient adaptation and integration of cyber-physical production systems into industrial practice, a broad scientific knowledge base is necessary. This is due, on the one hand, to the complex interrelations involved in the implementation of cyber-physical systems and, on the other, to the high degree of specialisation of companies, particularly in the case of small and medium-sized enterprises (Moore, J., Loughran, A., McCusker, E., Solvang, W. D., Sziebig, G., Yu, H., Ericson, A., Holmqvist, J., Wenngren, J., Pieska, S., Vahasoyrinki, J., & Kaartinen, H., 2016). Industry 4.0 is setting new standards for production systems and machines: they must be smart, efficient and highly adaptive, as the products to be manufactured can constantly change. In other words, production in Industry 4.0 should be more individual, flexible, efficient and faster (Matt D. T., Rauch E., & Fraccaroli D., 2016). This vision of the Smart Factory sounds equally as adventurous for SMEs. But how far are we actually from that?

Smart Factory: An Extension to SMEs This question cannot be answered generally, as companies are very differently prepared for the development of ‘smart networking’. This is shown by the example of a recent study by the Association of German Mechanical and Plant Engineering Companies VDMA (Lichtblau, K., Stich, V., Bertenrath, R., Blum, M., Bleider, M., Millak, A., Schmitt, K., Schmitz, E., & Schröter, M., 2015), according to which, every fifth mechanical and plant engineering company in Germany deals with ‘Industry 4.0’. In fact, only 5.6% of the surveyed companies are among the pioneers of the Industry 4.0 implementation, while 17.9% consider themselves as newcomers in the subject and the overwhelming majority of 76.5% have not yet taken any systematic steps towards Industry 4.0. And this despite it being unquestionably integral to the self-understanding of the German mechanical and plant engineering sector showcase itself as an industry and technology leader in this issue. This is in consideration that, in Germany, the mechanical engineering industry is the industry with the highest number of employees, even before the electrical and the automotive industry, with mainly small and medium-sized enterprises and an average company size of about 173 employees (VDMA, 2014). Radziwona et al. (2014) assert that one of the main handicaps of a Smart Factory vision is the lack of concepts that are applicable in SMEs. Most SMEs have a need for automation solutions in manufacturing to optimise operations. Apart from the related necessary investments, practical experience shows that productivity potentials cannot be realised by the mere purchase of technologies, but often require an individual adaptation to the specific conditions in the company. Experience also shows that, for the efficient adaptation and integration of cyber-physical production systems into industrial practice, a broad scientific knowledge base is necessary. This is due in part to the complex interrelations involved in the implementation of cyber-physical production systems, but also to the high degree of specialisation of companies, especially in the case of small and medium-sized enterprises (Riedl, M., Garcia, D., Rauch, E., & Matt, D. T., 2016). Moreover, many elements of the Smart Factory have not yet left the research laboratories. There is also no uniform concept for the Smart Factory that can be applied to any industrial operation (Hermann, M., Pentek, T., & Otto, B., 2015). Each company must analyse its situation and its individual needs and 279

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then choose those approaches that promise the best prospects for achieving the goals set. In this respect, certain parallels to ‘lean production’ and to the associated integration approach of the so-called holistic production systems can be recognised. A holistic production system is by no means a rigid conception frame with fixed methods and tools, rather it is the result of a company-specific, cooperative development process between executives, experts and employees. Despite a wide variety of variants and design variants in operational practice, almost all holistic production systems are based on similar basic objectives: the improvement of competitiveness by simultaneously increasing flexibility and efficiency by means of the systematic and continuous reduction of waste in all processes along a strictly customer needs-oriented value chain. In order to achieve these goals, work-organisational approaches from the lean-method-toolkit have been the focus of attention so far. Now, Industry 4.0 opens up new technological perspectives. The Smart Factory concept is, therefore, a logical technological step into the future: from data will come knowledge, from knowledge will come benefit. According to Mrugalska et al. (2017), lean production and Industry 4.0 can coexist and support each other. The Smart Factory does not replace lean production, rather it provides new technological tools that enable the lean principles to further develop (Matt D. T., Rauch E., & Fraccaroli D., 2016): physical kanban cards, whiteboards and other lean tools are gradually being replaced with modern technologies of the real-data processing and visualisation, rather like small evolutionary steps. According to Sanders et al. (2016), Industry 4.0 is capable of implementing lean principles – committing to Industry 4.0 makes a factory lean as well as smart. Through integrated information and communication systems, the shortcomings of conventional practices can be overcome to improve productivity and eliminate waste. Industries now have the combined benefits of real-time integration of the entire factory along with the assurance of minimal waste generation (Sanders, A., Elangeswaran, C., & Wulfsberg, J., 2016). New possibilities from information and communication technologies are matching with lean environments. For companies, Industry 4.0 offers an estimated benefit by stabilising lean processes with Industry 4.0 technologies (Wagner, T., Herrmann, C., & Thiede, S., 2017). However, application-oriented research needs to be developed pertaining to the criteria of implementing lean manufacturing (Sanders, A., Elangeswaran, C., & Wulfsberg, J., 2016). This is corresponding directly to the practical experience of the authors in closely following and supporting SMEs in their specific development processes towards the Smart Factory. The two use cases at the end of this chapter can, therefore, be seen as examples for the application of a reference architecture presented by the authors in the following paragraphs.

Knowledge Transfer: Industry 4.0 as a Competence In order to transfer approaches from the Internet of things, mobile robotics or the mobile Internet to the production halls in the form of short-term and medium-term solutions, both knowledge in the field of production technologies, mechatronics and a strong understanding of ICT are prerequisites. In this context, Industry 4.0 must be understood not so much as a product, but rather as a competence. Furthermore, the learning capacity of not just the single worker, but of the whole company is a crucial aspect. Based on this, the question naturally arises as to how these competencies can be transferred to practice. The challenge is to understand the concepts from an information technology-linked view of production and to apply it to one’s own tasks. In order to make this potential available to small and medium-sized enterprises in the context of applied research, appropriate approaches for a transfer of knowledge from applied research into practice are needed (Riedl, M., Garcia, D., Rauch, E., & Matt, D. T., 2016). 280

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Knowledge differs from pure information through the background or context and the individual experience of its intermediary or recipient. Knowledge can, therefore, be understood and interpreted differently (Nonaka & Takeuchi, 1995). From the combination of approaches based on technologies and company organisation, different conceptual models for a holistic transfer of knowledge have been developed in the past (Grimpe & Hussinger, 2008). These can be seen as a paradigm of learning organisations. Companies, with their internal structure, are receptive to new trends and proactively support the learning process of their employees. These companies are in a position to constantly develop and change, as well as to react dynamically to internal and external effects, thus successfully competing. Knowledge in an organisation can be broadly classified into two categories (Groever & Froese, 2016): explicit and tacit knowledge. Explicit knowledge is that which can be measured, captured, examined and easily be passed on to others in a codified format - a formal and systematic language. Tacit knowledge, on the other hand, is highly personal, context-specific and comes from one’s experience. It is hard to measure, capture or examine. That might be too narrow in the light of new cyber-physical systems as self-organising and decisioncapable technical entities (Gronau, 2015). In the era of Industry 4.0 and digitalisation, companies can also manage tacit knowledge. In the future, at least some of the competencies to make decisions will lie with technical actors (Gronau, N., Thim, C., Ullrich, A., Vladova, G., & Weber, E., 2016). Knowledge transfer in organisations can be defined as the process through which one unit (e.g., group, department, or division) is affected by the experience of another (Argote & Ingram, 2000). According to Argote and Ingram (2000), knowledge transfer in organisations manifests itself through changes in the knowledge or performance of the recipient units. Thus, knowledge transfer can be measured by measuring changes in knowledge or changes in performance. Experience in the field of knowledge transfer has demonstrated knowledge transfer to be a phenomenon that inherently involves interdependencies and asymmetries between providers and recipients of knowledge, and difficulties during different knowledge transfer stages (Caloghirou, Y., Kastelli, I. & Tsakanika, A., 2004; Wehn & Montalvo, 2016). Figure 1 shows a simplified version of the 5-step model of transfer of knowledge in companies as described by Gilbert and Cordey-Hayes, (1996) and which is often used in literature. It shows the process or the successive steps for a consistent transfer of knowledge leading to successful technological innovation. Figure 1. Simplified version of the 5-step model for the transfer of knowledge in companies

(Gilbert & Cordey-Hayes, 1996)

The first step corresponds to the basic identification of knowledge in the sense of a knowledge acquisition. As the basis of the knowledge transfer, this can also be understood as the identification of important trends or the identification of potentially useful findings from basic research. On this basis, the communication of knowledge takes place in verbal or written form. In essence, this step serves as a

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first dissemination of knowledge within the company. In order to maintain the knowledge acquired and communicated in the following, concrete application in the company is indispensable. The results of the application of knowledge enable the company to learn and build new competencies. In the case of successful application, this leads to an acceptance of knowledge or new knowledge in the company. This essential step ensures that the new knowledge is compatible with the basic orientation of the company and often takes place at an individual level. The key to a successful transfer of knowledge lies, ultimately, in the assimilation of knowledge. The results from applied knowledge are firmly integrated into the company and its corporate philosophy. This step is crucial to anchoring the knowledge in the core business processes in the long term and is, thus, essentially different from the often only project-based first application of knowledge (Riedl, M., Garcia, D., Rauch, E., & Matt, D. T., 2016). In particular, implicit expert knowledge is only successfully transferred if the recipient is able to work with this knowledge independently of the source. The knowledge is, thus, integrated into the company and its processes so that an independent problem solution is possible. An on-site training methodology and proactive support in the initial application of knowledge (Szulanski, 2000), for example, through coaching with the aim of supporting knowledge dissemination, directly correcting and directly reporting back, can be supported by knowledge-based educators. In principle, different channels are possible for the above-mentioned process of transferring knowledge from research into industry. These can be distinguished with regard to their relative importance (Bekkers & Freitas, 2008). The authors conclude in their experimental investigation that the different weighting of channels of knowledge transfer depends on the following factors: 1. Basic characteristics of specific knowledge (such as implicit / explicit knowledge, systemic knowledge or expected breakthrough). 2. Subject-specific background of knowledge. 3. Individual and organisational characteristics of those involved in the process of knowledge transfer (experience, publications and patents, entrepreneurship and research environment). In particular, the importance seems to be independent of the industrial sector in which knowledge is transferred. Table 1 shows an overview of channels of the knowledge transfer divided into higher-ranking groups, as well as their typical characteristics (according to Bekkers & Freitas, 2008). The authors of the study cited also note that joint research and contract research is well suited to transfer knowledge, which is characterised by many interrelationships. Clearly, each of the different channels of knowledge transfer is obviously suitable for a specific context. Formal technology transfer, for example, through joint research and contract research, and informal technology transfer, for example, through workshops or exchanges of workers, influence each other and are complementary to one another by, for example, informal contacts supporting the quality of a formal technology transfer (Grimpe & Hussinger, 2008). In addition to the distinctive qualities of knowledge itself (Argote & Ingram, 2000; Chen, 2004), the key factors in the successful knowledge transfer are the trust between the two partners (Szulanski, 2000; Szulanski, G., Jensen, R. J., 2004), the organisational structures of knowledge exchange (Szulanski, 1996), the motivation (Bock & Kim, 2002; Kuo & Shih, 2014) and the commitment of the partners (Amesse & Cohendet, 2001). For a successful transfer of knowledge, both appropriate skills for acquiring knowledge on the part of the receiving partner as well as for knowledge dissemination on the other hand are required (Martin & Salomon, 2003; Schulze, A., Brojerdi, G., & von Krogh, G., 2014). 282

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Table 1. Channels of the knowledge transfer divided into superordinate groups, as well as their typical characteristics Superordinate Groups of Knowledge Transfer

Characteristics

Scientific publications, conferences and workshops

• Scientific publications or professional publications and reports. • Participation in conferences and workshops. • Personal contacts (informal).

Important, especially if the knowledge is wellwritten and many interrelationships, especially for IT.

Exchange of labour

• Graduates as employees. • Students as trainees. • University staff as employees in industry. • Employees with simultaneous employment in research and industry. • Temporary exchanges of workers.

Important, especially, when breakthroughs are to be expected and the knowledge is difficult to document or publish.

Joint and contract research

• Collaborative projects in the area of ​​R&D (in the context of funding programs). • Contract research. • Financing of PhD. projects. • Consultancy services by university staff.

Important, especially to transfer published knowledge as well as systemic knowledge and knowledge with many interchanges, especially with large companies and universities as well as with regard to the IT sector.

Contact via alumni or professional associations

• Personal contact through memberships in professional associations. • Personal contact through alumni organisations.

Important, especially for basic research and the area of ​​economics and business models, as well as electrical engineering.

Organisation of specific activities

• Contractual in-business training and training measures. • Spin-offs of the university. • Specific activities through university technology transfer agency. • Shared facilities (for example, laboratories, equipment, offices).

Less important for the transfer of knowledge in the field of mechanical engineering. Especially important to transfer systemic knowledge and knowledge with many interchanges.

Patents and Licensing

• Patents (Patent Office or Patent Databases). • Licensing patents of the university or ‘know-how’ licenses.

Important in the case of a large number of published patents and knowledge with many interchanges.

Source: (According to Bekkers & Freitas, 2008)

On-site training is an appropriate measure for the understanding of important contextual information, especially for knowledge with interchanges (Carlile & Rebentisch, 2003). In addition, the decontextualisation of knowledge (Cummings & Teng, 2003) and the support for the application of knowledge (Schulze, A., Brojerdi, G., & von Krogh, G., 2014), especially on the spot (Szulanski, 2000), support a successful transfer of knowledge to industry. Implicit expert knowledge is often based on many years of experience and forms the basis, which can be passed on in a knowledge transfer. At the same time, however, the ability of the knowledge-imparting partner is also essential to recognise the significance of the relevant knowledge for the specific field of application (Szulanski, G., Jensen, R. J., 2004). The more understanding is available for the application, the more efficiently knowledge can be obtained, for example, using suitable analogies, well-understood examples and appropriate metaphors (Hashweh, 2005). In particular, in the transfer of knowledge between companies, the aim is to identify the area of knowledge which is helpful for the other partner, but, at the same time, to find the compromise of not having to give up its own advantage with regard to specialisation in the market and in the competition. The ability to find the right balance is a key part of the successful transfer of knowledge between companies (Schulze, A., Brojerdi, G., & von Krogh, G., 2014). For this, the cooperation of very heterogeneous partners can be very helpful, e.g. with regard to core competencies or industry sectors. The better the knowledge-imparting

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partner understands the motivation and abilities of the other partner’s knowledge, the better he can select adequate channels and tools for efficient knowledge transfer (Martin & Salomon, 2003). It is important to decouple and abstract the knowledge of the original topic area, while, at the same time, embedding it in sufficient background information, so that a successful application in the new topic area becomes possible. Carlile & Rebentisch (2003) note that, in particular, the process of knowing knowledge with the knowledge-imparting partner can be of decisive importance in many situations. It is necessary to create a common language, a common understanding among all partners of the knowledge transfer (Schulze, A., Brojerdi, G., & von Krogh, G., 2014). Schulze et al. (2014) emphasise, in particular, the possible collaborative character of a knowledge transfer. Also, for the knowledge-imparting partner, effects arise from which it can profit, e.g. through a deepening insight into a new field of application of knowledge. A possible approach of Industry 4.0 knowledge transfer in SMEs is presented by Ganzarain and Errasti (2016). In their stage process model, companies are guided in knowledge transfer and finding new opportunities for diversification and optimisation in areas within Industry 4.0. The stage process model is structured in three main stages: 1. Stage 1: Vision 2. Stage 2: Roadmap 3. Stage 3: Projects In the first stage, internal and external partners are involved to understand what is Industry 4.0. In addition, the company analyses its own capacity and resources in introducing Industry 4.0. At the end of the first stage, a vision for Industry 4.0 should be defined. The second stage is dedicated to developing a roadmap for implementing Industry 4.0 in SMEs. The given approach of Ganzarain and Errasti (2016) is a more strategy-oriented approach and aims to define an Industry 4.0 strategy for SMEs. There can be found elements for knowledge transfer, but the focus lies in defining a vision, strategies and a roadmap for implementation. In the following, the authors present a 5-step methodology that is more focused on the process of knowledge transfer as well as the identification of own competencies in Industry 4.0 and the resulting potential in its introduction.

5-STEP METHODOLOGY TO FACILITATE THE KNOWLEDGE TRANSFER OF INDUSTRY 4.0 PRINCIPLES TO SMES Based on the above described general scientific fundamentals and the long-term experience of the authors in introducing lean production systems and flexible automation in small and medium-sized enterprises (Matt, 2008; 2009; 2013), as well as based on Ganzarain and Errasti (2016), a 5-step approach has been developed which is suitable for Smart Factory concepts in SMEs. The proposed approach is illustrated in Figure 2. While Steps 3, 4 and 5 can be used for every type of industrial company to implement Industry 4.0, the first two steps of the proposed methodology are important for SMEs. The main difficulty of SMEs is to understand what is Industry 4.0 and how these new technologies can improve productivity in their company. Many SMEs don’t have enough qualified staff and, therefore, need the first step to train their people and to generate the awareness that Industry 4.0 is of crucial importance for the future of the company. Further, the second step should help SMEs to organise their ideas and needs regarding 284

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Figure 2. 5-step methodology for the introduction of Smart Factory concepts in SMEs

Industry 4.0. While many of the emerging Industry 4.0 technologies and tools can be applied in large companies, this does not apply for SMEs. In the following, the above shown five steps of the proposed methodology are explained in detail.

Step 1: Information and Awareness In the context of Industry 4.0, Smart Factory, Cyber-Physical-Systems, etc., there is currently a conceptual diversity and a definition jungle in which SMEs are often not able to cope with the issue and, thus, have a certain scepticism. At the same time, there is often a lack of direct reference to one’s own entrepreneurial reality, as well as suitable case studies, which could facilitate the projection of theoretical approaches and laboratory concepts into their own productive environment. Therefore, an introductory seminar will explain the most important terms and concepts of Industry 4.0 and Smart Factory. For example, the often very abstract term ‘cyber-physical system’ (CPS) becomes immediately more comprehensible if there are illustrative examples behind it. Figure 3 shows an example of the application of CPS using an intelligent gripper for wood chips in an industrial heating system. Equipped with suitable sensors for the measurement of moisture, ash content and granularity of the wood chips, this CPS collects measured data. Through the collected data, the system composes - depending on the respective operating state of the burner communicated directly to the gripper (M2M - machine2machine) - the right

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Figure 3. Smart Gripper: Sensors and intelligent control algorithms for optimising the emissions of an industrial heating system (Garcia, D., Riedl, M., Niedermayr, F., Waid, S., & Matt, D. T., 2015)

mixture of different wood chips, thus minimising CO2 emissions (Garcia, D., Riedl, M., Niedermayr, F., Waid, S., & Matt, D. T., 2015; Niedermayr, F., Waid, S., Garcia, D., Riedl, M., & Matt, D. T., 2015). In combination with the introductory seminar, SMEs should have the opportunity to see some best practice examples from other companies. This is possible in three ways: a) integrating examples of implementations in other SMEs, as in Figure 3, b) inviting experts from industrial practice to speak about their best practices or c) visits to best practice companies. In this phase, it is important to generate awareness that the world does not stand still and processes as well as systems are changing. Therefore, companies also have to change according to this new trend of Industry 4.0. In this phase, the first to steps in the knowledge transfer model of Gilbert and CordeyHayes (1996) – see also Figure 1 – are fulfilled.

Step 2: Requirements In a second step, the requirements for the introduction of Industry 4.0 have to be defined in a workshop. In this phase, the participants of the workshop are informed about the principles of Industry 4.0 and should express a ‘wish list’ of expectations regarding Industry 4.0 in their company. This initial collection of desires, user needs and requirements should be used to define the limitations for investigation as well as the constraints for the initiative. Axiomatic Design Theory is used for the categorisation of requirements. Axiomatic Design was developed by Nam P. Suh (Suh, 1990) as a design method whereby a designer starts from the collection of customer requirements or needs, translating them into functional requirements. Based on the functional requirements, possible design solutions are derived in a systemati-

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cal top-down decomposition. In our research, we do not use the whole Axiomatic Design approach, but only the initial phase of collecting requirements in order to categorise the requirements in a reasonable way. In Axiomatic Design theory (Suh, 1990; Thompson, 2013), customer requirements are differentiated into the following categories: •

•

•

Constraints: The determination of a hard limit usually indicates the presence of a constraint, defined as follows. Constraints can be a defined number of minimum or maximum. An example for a constraint is when space in production is limited to a certain number of square metres or that the budget for the introduction of Industry 4.0 is limited to a certain value, due to the financial limits of the firm. Non-Functional Requirements: Non-functional requirements focus on ‘how’ the artefact should be, in order to meet the standards set by the involved stakeholders. Their omission can compromise functional requirements. Non-functional requirements can be distinguished by the use of the form ‘should be’, together with an adjective that aims to influence stakeholders’ awareness In case of a smart manufacturing system for SMEs, a non-functional requirement could be that the production system should be productive with moderate production costs. Functional Requirements: Functional requirements help the designer in the determination of the sub-levels requirements and related design solutions. They should be de independent from each other to reduce complexity of the system design and characterise the functional needs of the artefact.

The moderator in this workshop has to collect, as a first step, all customer needs through a brainstorming round. In the next step, the individual customer needs should be discussed and categorised according to the three types of requirements shown above. This supports the participants to keep in mind what are the limitations, the main expectations and what functional topics need to be worked. Usually, customer attributes have to be rewritten or reformulated, when categorised as constraint, non-functional requirement or functional requirement. Where possible, tangible and measurable targets should be defined in this stage. If requirements are measurable, the greater the acceptance in the management and the workforce, which, in turn, considerably facilitates and accelerates implementation. In addition to business-oriented requirements (such as productivity, inventories, flexibility, delivery service level, etc.), other objectives can also be incorporated, for example, improved ergonomics or ‘demographic-free’ jobs.

Step 3: Self-Assessment Based on the defined requirements in the form of constraints, non-functional requirements and functional requirements, the actual situation should be discussed together to self-assess the actual competence and achievements in Industry 4.0 concepts and principles. Such a self-assessment and competence analysis should be conducted on two levels: a) organisation and management and b) operations. Therefore, groups should be divided in two parts, one working on more management and strategy-oriented topics and the other group working on operational tasks.

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Organisation and Management Assessment The analysis of the organisation consists of an assessment if companies are Industry 4.0 ready and if they only apply Industry 4.0 concepts in their management structure or processes. While much Industry 4.0-related literature gives recommendations about technological changes, very few describe also the organisational challenges in the introduction of Industry 4.0. The following are the main dimensions to be analysed in this group, according to Anderl et al. (2015) and Schuhmacher et al. (2016): 1. Strategy: Vision, strategy, roadmap, business models. 2. Leadership: Management competence, central coordination of Industry 4.0. 3. Governance: Data security management, protection of intellectual property, labour regulations for Industry 4.0. 4. Supply Chain Network: Digital competence of customer and supplier, digitalisation of processes along the supply chain. 5. Culture: Knowledge-sharing, open innovation, awareness of Industry 4.0. 6. People: ICT competencies of people, willingness and openness of employees. 7. Process Digitalisation: ICT tools for digitalisation, mobile devices, real-time communication.

Operations Assessment This can be done most systematically along the value chain of the company. A previous value-stream design is suitable as a method to support this phase and to better understand the material and information flow. Many SMEs are already familiar with this instrument from the lean toolkit and very often already have a current state map of their value stream. Thus, it can be applied without many explanations. In the case of the collective discussion of the value stream (or the value streams), the actual state of competence in Industry 4.0 implementation in operations can be investigated. In addition, initial ideas of potential areas for improvement and application can be identified. The following are the main dimensions to be analysed in operations according to Anderl et al. (2015) and Schuhmacher et al. (2016): 8. Product: Individualisation of products, digitalisation of products, product intelligence, integration of sensors, connectivity of products 9. Production Control: Decentralisation in production planning and control, use of modern tools like Manufacturing Execution Systems (MES), connectivity of systems. 10. Shop Floor Management: Big data analytics for production, use and evaluation of data, monitoring, visualisation of shop floor data. 11. Operator 4.0: Changing role of the operator, competencies and skills of operators, willingness and openness of employees. 12. Technologies: ICT in production and logistics, man-machine interaction, automation, advanced manufacturing processes, predictive maintenance. In both, the organisation and management assessment as well as the operations assessment, the participants should discuss the actual state in the achievement of the above described dimensions and elements. In order to manage a self-assessment or an assisted assessment through external partners or experts, every element in the dimensions has to be evaluated using a rating scale. There are different 288

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possibilities to define such a rating scale. One possibility is to evaluate the elements on a Likert scale reaching from ‘not implemented’ to ‘fully implemented’ (Schumacher, A., Erol, S., & Sihn, W., 2016). Another possibility is to define formulated and clearly defined stages of maturity for each element, with practical examples (Anderl, R., Picard, A., Wang, Y., Fleischer, J., Dosch, S., Klee, B., & Bauer, J., 2016), in order to facilitate an assessment by the group of participants. In the second case, one maturity stage could be closer to the other. Both possibilities are illustrated in Figure 4. Therefore, in our case, we suggest the use of a Likert scale to guarantee a neutral rating. Further, it helps to document in the workshops whether competencies in the above described dimensions and elements are available and, if so, on which of the two following levels: • •

Internal Competence: Competence in own organisational structure and own staff – e.g. experienced specialists in robotics or in vision systems, as well as successfully implemented projects. External Competence: Access to competencies through an experienced and qualified supplier in the supplier network - e.g. a supplier with a high expertise in sensors.

Figure 4. Assessment of Industry 4.0 elements based on a) Likert scale or b) maturity stages

Step 4: Gap and Potential Analysis The previous self-assessment and competence analysis summarises the current situation of a company dealing with the introduction of Industry 4.0. In a next step, a gap analysis should be done to identify

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where Industry 4.0 topics are already implemented and where gaps are significant. According to the requirements defined in Step 2, the single dimensions and elements have to be discussed in order to define the target level of competence to be achieved and the resulting gap between actual level and needed or requested level (see Figure 5). Depending on the defined requirements, some dimensions and elements can be more important than others. For example, for a firm, traceability and identification of the product along the production flow can be highly important, while automation or man-machine interaction are less or not important. In this case, the gap in both examples could be the same, but the expected potential is different. Thus, gap analysis should be followed by the determination of importance or potential through implementation of an Industry 4.0 element. In a similar case described in Schuhmacher et al. (2016), the authors propose a factor for evaluating the potential with a Likert scale reaching from ‘no potential’ (rating = 1) to ‘very high potential’ (rating = 5). The evaluation of the potential could be determined by individual evaluation of every participant in the groups and then calculating the average overall. Based on the evaluation of the gap and the potential in implementing these elements or concepts, a matrix can be designed in order to select those concepts with most potential and to determine also the needed effort and time for implementation. Figure 5 shows the structure of such a matrix. If the elements in Figure 5 show a high potential and only a low gap to achieve it, we speak about so called ‘quick wins’ and, thus, Industry 4.0 concepts with a high priority in implementation combined with a short-term implementation. If the elements show a lower potential and only a low gap, then these are called ‘low hanging fruits’ and can be categorised as short-term concepts of lower priority. Elements with a high potential combined with a high effort to overcome a high gap are called ‘must have’ and need a well-defined medium-term planning of time and resources. In the fourth case, a combination of low potential and a high gap, we speak of ‘money pits’ and the company should try to avoid them and not invest in the implementation of these Industry 4.0 concepts.

Step 5: Implementation Plan Based on the results from Figure 5, the areas of actions are selected and detailed in a further workshop, involving further employees and skilled workers, where work packages are derived and then, subsequently, organised in an implementation plan. In the implementation plan, the teams, deadlines and responsibilities are defined and documented. This implementation plan is then used as roadmap for a step-by-step conversion and monitoring of the progress in each work package. Before concluding the phase of work package definition, the project team should check whether all constraints, and non-functional as well as functional requirements, are represented and respected in the work packages. If there are any inconsistencies, the project team can react and adapt the implementation plan before the kick-off of the operative implementation phase. In the case of extensive and important work packages, it could be reasonable to start with smaller pilot projects. The implementation of pilot projects creates first touchable results and, therefore, acceptance of employees. Through such pilot projects, the last three steps in the knowledge transfer model of Gilbert and Cordey-Hayes (1996) – see also Figure 1 – can be fulfilled (Application, Acceptance, Assimilation).

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Figure 5. Strategies for selection of Industry 4.0 topics to implement - matrix with gap on x-axis and potential on the y-axis

CASE STUDIES The above described approach has been tested in parts within two case studies. In Case Study A, the procedure model was applied in the context of a large group of participants from several different industries, all of them small or medium-sized enterprises located in the North of Italy. Case Study B reports the application of the approach in one single North Italian SME.

Case Study A The methodology described in the previous section was carried out in collaboration with several companies from Northern Italy within the framework of a six-month series of events. A total of 40 participants from the industry participated in the event and scientific support was provided by an interdisciplinary team of scientific staff. The companies involved had a very heterogeneous industrial background. Figure 6 shows the applied steps of the Five-Step Methodology, while Table 2 shows the key data of the case study.

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Figure 6. Applied steps of the Five -Step Methodology for Knowledge Transfer and introduction of Industry 4.0 Concepts in SMEs in Case Study A with a set of 40 North Italian SME.

Table 2. Key data of the case study Key Data Participants

40 participants with a majority of engineering background and several years of industrial experience.

Industries

• Automation technology. • Automotive. • Battery technology. • Domotics and building automation. • Industrial gases. • Metal processing. • Utilities in the field of energy and telecommunications. • measuring systems. • Agrochemistry. • Medical technology.

Size and Turnover of Companies

SMEs with 50 to 250 employees, as well as revenues between €5 and €50 million.

Step 1: As the initial step of Case Study A, an introductory seminar with speakers, bringing together scientific and practical knowledge, was held for the participants. To prepare the Self-Assessment in Step 3, the participants had to fill in a template asking for specific requirements for their company as an exercise after the seminar (Step 2). For the Self-Assessment, the group of participants was divided into two working groups. In these working groups, the participants completed the subsequent steps of Self-Assessment (Step 3) and Gap & Potential Analysis (Step 4) with scientific assistance. At this point, the main results of the working groups are summarised, since these can be regarded as the most important component and result of the intended knowledge transfer. The workshops in this case study covered only the operational analysis and, thus, topics on different levels of operations in the company from the supply chain to the operational level, through planning and monitoring tasks at the process level up to individual production modules and workstations. The following list gives an overview of the treated use cases: • • • • • •

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Process monitoring and remote maintenance. Smart maintenance. Transparent order management and prioritisation of orders. Self-diagnosis and self-optimisation in production processes. Virtual production paper: dynamic work instructions. Smart workstation: assistance in assembly and logistics processes.

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Table 3 shows an example of some of the project ideas elaborated in the working groups and provides an overview of the thematic scope and the first steps of applying the acquired knowledge by the participants with corresponding Gap & Potential assessment (Step 4). Table 3. Exemplar excerpt from the project ideas worked out during the workshops, including a short qualitative assessment Selection of Worked Out Project Ideas (Qualitative Assessment) Digitalisation on the Shopfloor: Assistance functionalities for employees during assembly processes. *Idea: Smart workstations to increase quality in assembly.

Gap: medium-high Potential: high Time-line: medium-term

Relevant and mobile Information: Lean, Industry 4.0 and problem management *Idea: Provide relevant information and work instructions dynamically on mobile devices

Gap: low Potential: high Time-line: short-term

From Rocket Science to Shopfloor Reality: Lean, Industry 4.0 and prioritisation in order management *Idea: Electronic tagging in internal processes (analogous to supermarket)

Gap: low Potential: medium-high Time-line: short-term

Communication and Information Management in Industry 4.0: Lean, Industry 4.0 and holistic information management *Idea: smart sensors and information in real time

Gap: medium Potential: medium-high Readiness: medium-term

Intelligent Products to stay competitive: Innovative products through integration of Internet-of-Things approaches and additional services *Idea: Support of life cycle management through networked products

Gap: medium Potential: medium-high Time-line: medium-term

Improve Machine & Process Control with better data & connection: Optimisation of process control *Idea: Efficient response to digital traceability issues

Gap: medium-high Potential: high Time-line: medium-term

In a concluding round of feedback, the participants positively evaluated both the approach of the transfer of knowledge as well as the results. The following selection of individual comments from the participants gives an impression: I found the contact with other companies and the overview of the current status in other companies particularly helpful. The discussion in the working groups and the exchange taking place were a great added value for the whole event. The focus of the seminar series and workshops on CPPS supports the development of the first pilot applications in the company. The exchange of ideas and openness in the discussions and working groups are seen as a great benefit. Looking beyond the boundaries of one’s own business can help identify important trends and identify the necessary knowledge that a company should adopt in the future. Although the workshops are seen as great added value in the individual companies, some participants judged the time for the effective elaboration of the project ideas as too short.

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Case Study A addressed the first four stages of the five-stage model with a special focus on operations. However, this did not happen at the company level, but rather as part of an awareness raising of the company’s own knowledge bearers, which can subsequently bring this new knowledge into the company. This is the reason why the last step of the 5-step methodology presented in this chapter was not executed as part of the case study carried out with this group of companies. Organised on project level, the scientific monitoring had to stop after Step 4’ nevertheless, participating companies stated their interest to proceed with implementation plans, but, unfortunately, on their own. Confidentiality regarding participating competitors was mentioned as the main reason for these decisions. As described by Gilbert and Cordey-Hayes (1996), the acceptance of new knowledge takes place after the successful application, especially at the individual level. In particular, the accompanying workshop corresponds to the approach described by Szulanski (2000) for effective on-site training and proactive support in the first application by the scientific coaches. The channels used in the described method for the transfer of knowledge and, in particular, the contents to be transferred with regard to the topic Industry 4.0, correspond to the respective characteristics according to Bekkers and Freitas (2008); see also Table 1. At the same time, however, it becomes clear that the described methodology can only be a first step for the transfer of knowledge into the company. Further scientific support in the application or integration into the company or its processes is very important for the sustainability of the results. According to Grimpe and Hussinger (2008), this would seem to be a suitable complement to the already implemented steps to raise awareness and to anchor the new knowledge within the company.

Case Study B The second case study reports the application of the approach in one single medium-sized company. It is taken from a (still ongoing) project with an Italian medium-sized manufacturer of sanitary articles. Figure 7 shows the applied steps of the Five-Steps Methodology in case study B. Figure 7. Applied steps of the Five-Step Methodology for Knowledge Transfer and introduction of Industry 4.0 Concepts in SMEs in Case Study B with an SME from the manufacturing sector.

The company has always strategically positioned itself as a ‘flexibility leader’ and describes itself as an ‘industrially organized craft enterprise’. All articles are produced just-in-time on customer orders, 40% of which are custom-made. The company has always strived to achieve the highest degree of flexibility in terms of variants and volumes without compromising productivity. As a result, a holistic production system was introduced as early as 2001 according to the principles of lean production and, from then on, continuously improved. In 2009, product identification and tracking using RFID (Radio Frequency

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Identification) was introduced. RFID technology is an important building block or even the prerequisite for entry into many Industry 4.0 applications. Products and components can be clearly identified and tracked by radio. Firstly, the RFID identification was used primarily for tracking the production order, for automatically labelling the packaged products, for monitoring inventory levels, and for checking the completeness of shipments. However, this is only a fraction of the possibilities offered by this technology in the context of the Smart Factory. The now cramped space conditions at the old production site have prompted the company management to build a new ‘greenfield’ plant, which is to be ready for the middle of 2018. In the course of this factory-planning project, the opportunities are now also being taken of planning the new factory according to the principles of Industry 4.0. Therefore, a project team consisting of internal and external specialists and executives was compiled and the Smart Factory 2018 project was launched. The 5-step approach described above was applied to identify and implement Industry 4.0 technologies and solutions. Corresponding to Step 1 of the presented methodology, an introductory training in the principles of Industry 4.0 and in requirements analysis, the value chain of the company ‘from ramp to ramp’ was carried out. In comparison to Case Study A, this initial step was tailored to the demands and specific already existing knowledge of the company, coming, in particular, from past individual experiences (see above). Therefore, experts with scientific and practical background sensitised the participants in a series of seminars. Two working groups of company employees defined the individual requirements in an iterative process – the organisational and management oriented requirements on the one hand and the operational perspective on the other (Step 2). As result of Step 3, these working groups identified the following topics of interest for future development: 1) order processing (information flow from customer order to delivery note and invoice); 2) goods receipt and warehouse; 3) component manufacturing; 4) picking and material supply, assembly; 5) packaging and dispatch. As the first approach to Step 4 (and subsequently Step 5) of the methodology, the gap and potential of possible solutions were analysed on the basis of a macro value stream plan. At the time of writing this chapter, these phases are still work in process; therefore, final results will be reported in future publications of this case study. Nevertheless, the potential of the methodology in general was perceived by the participants, e.g. 79 ideas and applications for Industry 4.0 concepts were recently developed and validated in this project. As part of the ongoing Step 5 of the methodology, these ideas were grouped together into a total of eight work packages, where they were further concretised. For each of the eight defined work packages, a separate project team was set up, which first carried out concept elaboration with investment planning and an accurate profitability analysis for the measures within the work package. In the meantime, the planning phase for the new and smart greenfield factory is nearly concluded and the results are very promising. The research team will also continue to assist the company in the implementation phase in order to collect more information and data that might deliver valuable inputs for the further research work in this field.

DISCUSSION AND OUTLOOK FOR FUTURE RESEARCH However, the described application of the developed methodology for knowledge transfer and introduction of Industry 4.0 principles in the case study has different boundary conditions, which must be taken into account when the described results are transferred. In principle, of course, findings obtained from the 295

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consideration of only one case study are not easily transferable to other circumstances. In this context, the discussed case studies have a quite contrasting character. While Case Study A is implementing the presented methodology on a higher, more general level, while also integrating some aspects of open innovation approaches, Case Study B is focusing more on the specific conditions and individual experience of one SME. Nevertheless, the first analysis of results seems to prove the compatibility of the presented methodological approach to both situations. In addition, the development of the methodology along with the implementation and evaluation of the case study were carried out by the same research team. Therefore, the described methodology can be used and analysed in other case studies. For further discussion of the approach, it is important to record a larger set of results. This also applies to the specific background of the company involved in the case study and its employees. In the further tests of the methodical approach of the transfer of knowledge, it will be of crucial importance to test this in various industrial contexts. At the same time, it is not unlikely that there are significant regional differences in the academic, industrial and political frameworks that influence the successful transfer of knowledge from the industry to cyber-physical production systems. Therefore, the initial starting point of an SME must be kept in consideration and the fact that this topic can be addressed on various levels: the contents have to be adapted to these specific requirements of the company. This is true not only for the initial situation, but also for mid- and long-term changes, as the contents of the Smart Factory will change in future, hand-in-hand with continuous technological advances. This suggests that the capability to learn and adapt will be crucial for companies on different levels in the future (as it was in the past). A potential methodological approach for this challenge was presented by the authors and partly validated in practice during the presented use cases. In order to overcome the given limitations and further develop the methodology, the authors will carry out, over the next four years, the collaborative research project SME 4.0 – Industry 4.0 for SMEs, financed by the European Commission within the H2020-MSCA-RISE-2016 program (grant n° 734713 – www.sme40.eu), which involves international research and industry partners from Europe, Asia and the USA. The aim of this research project is to analyse, in the first two years, the specific requirements of SMEs in the introduction of Industry 4.0 solutions. In the following two years, the research team will be working on concepts and solutions for smart SME manufacturing, smart SME logistics and organisational models and implementation strategies for smart SMEs.

CONCLUSION The concept of the Smart Factory, based on the approaches and technologies of Industry 4.0, is currently being discussed intensively in research as well as in industrial practice. The proposed 5-step approach for knowledge transfer and the introduction, as well as implementation, of Industry 4.0 concepts is aimed primarily at SMEs in order to help them with a methodological guidance to access the potentials of Industry 4.0 in a step-by-step procedure towards the planning, design and implementation of a Smart Factory. Within this research, a methodology of an efficient transfer of knowledge in the context of Industry 4.0 was derived from a careful literature research and evaluated by the promising experiences made in two industrial case studies, wherein Industry 4.0-relevant knowhow and concepts were transferred from research into industrial practice. The methodology serves, above all, to sensitise small and medium-sized enterprises to the possible potentials of the so-called Industry 4.0. The first results from the case studies suggest that the use of complementary channels of informal knowledge transfer within the framework of seminar events and collaborative on-site training methods enables industry participants, within a short 296

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time, to be able to develop suitable project approaches for the use of Industry 40 applications in their own company.

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Szulanski, G. (1996). Exploring internal stickiness: Impediments to the transfer of best practices within the firm. Strategic Management Journal, 17(S2), 27–43. doi:10.1002mj.4250171105 Szulanski, G. (2000). The process of knowledge transfer: A diachronic analysis of stickiness. Organizational Behavior and Human Decision Processes, 82(1), 9–27. doi:10.1006/obhd.2000.2884 Szulanski, G., & Jensen, R. J. (2004). Overcoming stickiness: An empirical investigation of the role of the template in the replication of organizational routines. Managerial and Decision Economics, 25(6–7), 347–363. doi:10.1002/mde.1195 Thompson, M. K. (2013). A Classification of Procedural Errors in the Definition of Functional Requirements in Axiomatic Design Theory. In Proceedings of the 7th International Conference on Axiomatic Design (pp. 107-112). Worcester, MA: Worcester Polytechnic Institute. Törngren, M., Bensalem, S., Cengarle, M. V., Chen, D.-J., McDermid, J., Passerone, R., … Runkler, T. (2014). CPS State of the Art, EC FP7 Project 611430 CyPhERS. Tech. Rep. Deliverable D5.1. VDMA. (2014). Maschinen- und Anlagenbau [Mechanical engineering and plant engineering]. Retrieved March 10, 2017, from http://www.berufsstart.de/unternehmen/branchen/maschinen-anlagenbau/verband. php Wagner, T., Herrmann, C., & Thiede, S. (2017). Industry 4.0 Impacts on Lean Production Systems. Procedia CIRP, 63, 125–131. doi:10.1016/j.procir.2017.02.041 Wan, J., Tang, S., Shu, Z., Li, D., Wang, S., Imran, M., & Vasilakos, A. V. (2016). Software-defined industrial internet of things in the context of industry 4.0. IEEE Sensors Journal, 16(20), 7373–7380. Wehn, U., & Montalvo, C. (2016). Knowledge transfer dynamics and innovation: Behaviour, interactions and aggregated outcomes. Journal of Cleaner Production. Xia, F., Yang, L. T., Wang, L., & Vinel, A. (2012). Internet of things. International Journal of Communication Systems, 25(9), 1101–1102. doi:10.1002/dac.2417 Xu, X., Sheng, Q. Z., Zhang, L. J., Fan, Y., & Dustdar, S. (2015). From big data to big service. Computer, 48(7), 80–83. doi:10.1109/MC.2015.182 PMID:27110033 Yoon, J. S., Shin, S. J., & Suh, S. H. (2012). A conceptual framework for the ubiquitous factory. International Journal of Production Research, 50(8), 2174–2189. doi:10.1080/00207543.2011.562563 Zuehlke, D. (2010). Smart Factory - towards a factory-of-things. Annual Reviews in Control, 34(1), 129–138. doi:10.1016/j.arcontrol.2010.02.008

KEY TERMS AND DEFINITIONS Cyber Physical System: The combination of physical and digital (cyber) systems with its advantages compared to the only physical or digital system. Industry 4.0: The fourth industrial revolution after mechanization, electrification, and computerization dealing with internet of things, big data, and cyber physical systems.

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Knowledge Transfer: The ability to transfer knowledge and competences from a skilled person or group to another less-skilled group. Maturity Model: A model that describes different stages of maturity of a system. Self-Assessment: A system or approach to assess and evaluate the own capabilities and competences. Smart Factory: The holistic combination of all Industry 4.0 concepts in a factory to become intelligent. SME: Small and medium-sized enterprises between 50 and 250 employees according to the European Commission.

This research was previously published in Analyzing the Impacts of Industry 4.0 in Modern Business Environments; pages 256-282, copyright year 2018 by Business Science Reference (an imprint of IGI Global).

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Chapter 16

Adoption of Design Thinking in Industry 4.0 Project Management Ebru Dilan Kadir Has University, Turkey Mehmet N. Aydin Kadir Has University, Turkey

ABSTRACT Management of Industry 4.0 projects needs to have a distinct discourse, be flexible, iterative and creative. These projects are tightly linked with the way people work which is directly related to both their capabilities and their ways of thinking. Challenging Industry 4.0 projects entail out-of-the-box thinking. The basic premise of this research is that the complex transformation accompanying Industry 4.0, which involves various dimensions, requires extensive and effective project management that can leverage novel approaches and techniques such as design thinking. This new approach may overcome the limitations of the dominant model of standard project management and has the potential to bridge the gap between a refreshed project management perspective and the tools/techniques in practical use. Deciding whether, and to what extent, design thinking needs to be adopted in practice in Industry 4.0 project management is a challenge. However, it is time to start exploring the challenges governing the interface between agile approaches such as design thinking and Industry 4.0 project management.

INTRODUCTION In the last decade, society is increasingly surrounded by a socio-technical-digital ecosystem involving manufacturers, service providers, customers and users, in which more interactions occur between people, machines and digital technologies to meet the needs of society and deliver added value for all involved in the ecosystem. From an agriculture society through an industrial revolution towards a smart industrial and service driven society, the ecosystem represents an industrial structural transformation (Gerlitz, 2015). DOI: 10.4018/978-1-7998-8548-1.ch016

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 Adoption of Design Thinking in Industry 4.0 Project Management

The fourth industrial revolution, “Industry 4.0” initiated in Germany as a roadmap was later promoted in other countries (Brettel et al., 2014). The roadmap was designed by the German Ministry of Education and Research to promote the German high technology industry and its strategy. Broadly, the linking of the virtual world with the physical world is associated with Industry 4.0. Intra-company linking of intelligent products and systems and their cross-company integration into industry value networks in manufacturing is referred to as Industry 4.0 (Kagermann, 2015). Many companies especially in the manufacturing industry (i.e. automotive, machine) compete on product quality, manufacturing costs and time to market performance. Offering customized products of remarkably high quality at competitive prices can be realized through intelligent automation and the rearrangement of people in manufacturing systems. On one hand, Industry 4.0 facilitates increased flexibility, mass customization, acceleration, improved quality, and enhanced productivity in manufacturing, on the other hand it requires firms deal with various challenges such as individualized products, shortened lead time to market, and high product quality. For instance; typical issues for smart manufacturing system in Industry 4.0 involve complex problems of design, machining, monitoring, control, scheduling, industrial applications, sensor and actuator deployment, data collection, data analysis, and decision making (Zheng et al., 2018). In manufacturing firms, increasing speed of technological capabilities, development and diffusion, in terms of robotics, advanced manufacturing technologies, integration of information and communication technologies (such as artificial intelligence, big data analytics, industrial Internet of Things) and sensors into the manufacturing process have high impacts on business/operations, people and culture. Indistinct boundaries of virtual and real worlds force manufacturing firms to master the cyber-physical interface. Shifting and accelerating customer preferences cause manufacturing firms to shift from being reactive to proactive (Roos, 2016). Shepherd and Ahmed (2000) introduce that manufacturers should evolve from product-driven to customer-driven approaches by moving from the conventional new-product business model to a solutions-innovation business model. At various stages of product life cycle maturity, there are various manufacturing approaches to improve business performance. Supply chains are being forced to turn into supply networks which constitute concurrent processes necessitating higher levels of agility, flexibility and wide range of soft skills (interpersonal and communication) across the labor force. Higher levels of employee responsibility, autonomy and managerial delegation are demanded at all levels in the organization (Davis et al., 2012). One of the key problems in Industry 4.0 projects is that, Industry 4.0 solutions require a comprehensive approach both on technical and on organizational/processual level. Due to required scope of the solution it is not possible for single manufacturing company to build new solutions due to knowledge and accessibility barriers on either technical or processual level (Albers et al., 2016). Projects become more complex and ambiguous in Industry 4.0. Project management becomes more challenging. Furthermore, projects are regarded as highly dependent on stakeholders but require cooperative processes among them. These project characteristics require special skills and competences in human. In this context, inclusion of prospective innovative approaches in project management such as design thinking is likely to be underrepresented in the Industry 4.0 transformation. That is, researchers believe that the extent to which design thinking is adopted in Industry 4.0 project management is a relevant and persistent research issue. Referring to this research gap, through this chapter, the researchers expect to open a discussion on the conceptual and empirical basis of design thinking and project management within the Industry 4.0 context.

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One of the key contributions of this chapter is to establish a basis for elaborating on how design thinking can provide project management with new perspectives for addressing Industry 4.0 challenges. We take into account both conceptual and empirical aspects of an evolving basis for this research issue. It requires a conceptual examination as three relevant notions (design thinking, project management, Industry 4.0) are intertwined. The empirical aspect is needed since we need to understand the very idea of design thinking in Industry 4.0 project management in a real-world context. The chapter is organized as follows. The background section presents the key concepts of Industry 4.0, a refreshed perspective of project management, design thinking and its potential in the context of Industry 4.0 project management. The main focus of the chapter section is the display of the methodology of the research and case study in a nutshell. In the succeeding section called ‘solutions and recommendations’, the results from the case study are discussed. This section argues for a new perspective in Industry 4.0 project management. The chapter continues with future research directions and key observation-based implications and ends with the conclusion section.

BACKGROUND Industry 4.0 has drawn considerable attention from academics and practitioners in recent years, but its basis is notably built on a technology push. However, it is hardly possible to execute it in “plug-and-play” mode. It necessitates complex and continuous transformation of different aspects including business/operations, technology/infrastructure, people, and culture. The transformation towards Industry 4.0 emerges as a continuous evolutionary process, integrating physical objects (technologies, machines and people) into the information network, connecting with intelligent machines, manufacturing systems, processes and people through the Internet, thus turning the real world into an information system (Gerlitz, 2015; Kagermann 2015). Through Industry 4.0, all productive units in an economy are linked and consistently digitalized thereby forming a sophisticated network (Blanchet et al., 2014). New industrial concepts and policies encouraging social and technological innovation are changing the common understanding of many industries and manufacturing systems as well. Various digital maturity models apply to organizations. Schumacher et al. (2016) propose a maturity model for assessing Industry 4.0 readiness and maturity of manufacturing enterprises. Nine dimensions are categorized (strategy, leadership, customers, products, operations, culture, people, governance, technology) and exemplary maturity items are identified. Most of these maturity items overlap with the projects (i.e. digitalization of sales/products/services, individualization of products, interdisciplinary, interdepartmental collaboration, and open innovation, utilization of mobile devices and machine-to-machine communication). Industry 4.0 research seems to be mostly technology-driven and appears to undervalue a managerial and human-centered perspective (Arnold et al., 2016). Schneider (2018) identifies 18 managerial challenges of Industry 4.0 categorized into six interrelated groups: strategy and analysis, planning and implementation, cooperation and networks, business models, human resources, and change and leadership. These challenges trigger organizational and cultural change and are governed by managers. Change is governed centrally from the top-down or horizontally from the bottom-up. Innovative projects are conducted by heterogeneous decentralized units facilitating the role of experimentation and iterative learning which are significant sources to deal with uncertainties accompanying the transformation. However, the knowledge penetration from these decentralized units may be difficult (Fleisch et al., 2014). Likewise, Bechtold et

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al. (2014) state that the uncoordinated array of bottom-up initiatives will block the path towards Industry 4.0 and emphasize the importance of a clear top-down governance. Industry 4.0 transformation requires new employee level and company level capabilities. While simple and repetitive tasks are automated, new and more complex tasks emerge (Becker and Stern 2016). Due to task enrichment, managers and technical experts are supposed to have a T-shaped, interdisciplinary competence profile, rather than a specialized one, providing broader perspective across business models, processes, technologies and data-related procedures. There will be a demand for enhanced social and technical skills, and a shift toward design thinking instead of production thinking (Blanchet et al., 2014). Labor work will still remain permanent, but will change in context, needed to be skilled in decision-making and collaboration. The role of workers is changing towards becoming coordinators and problem-solvers when faced with unforeseen events (Brettel, 2014). The basic premise of this research is that the complex transformation accompanying Industry 4.0 involving various dimensions requires extensive and effective project management that can leverage novel approaches or techniques such as design thinking. In real world practice, projects have been carried out using various approaches and techniques based on conventional (linear, plan-driven, predictive) or emerging paradigms (Design Thinking, Kanban, Agile, Lean) to address such transformations. One can argue that standard project management is inadequate for addressing changes in the environment or in business needs (Morris, 2013; Pajares et al., 2017). Projects with high uncertainty are referred to as exploration or soft projects (Lenfle, 2008; Atkinson et al., 2006) where technologies, market and customer requirements are unknown at the beginning or are constantly changing. These types of projects enable experimentation, exploration and knowledge creation. In a similar context, one of the key assumptions of standard project management is an act of optimizing (scope, time, cost, quality) and now substitutes a more creative and open-ended approach. Shenhar & Dvir (2007) suggest a framework with four dimensions to manage complexity in projects; technology, novelty, complexity and pace. Novelty highlights the uncertainty in projects (goals, customer requirements, market etc.). Pace addresses the competitive pressure. Rapid change, accelerated innovation, and rising complexity are characterizing today’s world, and uncertain contexts are becoming the norm rather than the exception (Ben Mahmoud-Jouini et al., 2016; Hobday et al., 2012). Therefore, the usual, basic assumptions utilized in standard project management have become open to discussion. Consequently, three streams of work have emerged to reformulate project management in such a context focusing on; the importance of an exploration phase allowing requirements and specifications to emerge during the project life through trial-and-error and learning; the importance of stakeholders and the need to mobilize them to build the project context; and the need to link project management to strategizing at the firm level (Ben Mahmoud-Jouini et al., 2016). Particularly in the manufacturing industry, innovation-based competition increases the project management’s significance as a strategic capability. However, when reviewed in such perspective, effective methodologies, tools and professional attitudes do not seem adequate to implement these three streams of recommendations. In this regard, the refreshed perspective of project management can get help from the field of design (Ben Mahmoud-Jouini et al., 2016; Lenfle, 2016; Lenfle et al., 2016). Designers deal with open and complex problems for many years. Studying the way designers work and borrowing some practices from the field could be interesting for organizations (Dorst, 2011). Today, design is untied from its domain and freely used in many fields such as business innovation as a complex thinking process for realizing new realities (Tschimmel, 2012).

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Design is perceived as an activity and process, because it is managed towards product and service innovations in organizations. Because of its iterative nature, it is an activity to be developed further. It frames the problem space, at the same time receives potential emerging solutions gladly (De Blois & De Coninck, 2008). On the other hand, design is a strategic resource, an organization asset and information for competitive advantage. It is also knowledge, because it is used to create new forms and meanings. Similarly, design is understood as capability as well (Gerlitz, 2015). Amit & Schoemaker (1993) states that developing design capabilities is a long-term learning process requiring complex interaction between an organization’s human capitals. Perceiving design as an activity, process, resource, asset, information, knowledge and capability shows appreciation of its value in smart and digitalized production and service development in Industry 4.0. In this light, design becomes an essential enabler for innovation within the dynamic emerging smart community and assures a playroom for creativity and its tangible/intangible outputs as products, services and processes (Gerlitz, 2015). According to Brown (2008), design thinking is an approach to innovation. Le Masson et al. (2011) designate situations of ill-defined problems with high uncertainty related to either technology or markets (such as smart cities, electrical devices or complex machines) as “innovative design situations” requiring specific design processes that act as an interplay between the space of concepts (C) and the space of knowledge (K) (Hatchuel & Weil, 2009). Design management and design thinking are intertwined approaches to innovation, which share the same conceptual base (Carlgren, 2013). Design thinking goes beyond the traditional problem-solving approach (linear and analytical) which lacks definitive solutions. In projects characterized by high uncertainty, understanding and defining the problem requires uncertainty reduction strategies through a learning-focused, hypothesis driven approach (Beckman & Barry, 2007; Owen, 2007). De Blois & De Coninck (2008) present the concept of a project as an organizing process (“organizing project”) in which all actors and stakeholders play a primary role in contrast to the traditional perspective of “the organized project”. A project is dependent on its context, rather than being a free object. The role of actor and stakeholder participation is highlighted both in the concept of the organizing project and thinking by design. Thinking by design revisits the problem space iteratively, whereas project management theory mostly ignores problem-setting activities assuming that project objectives will become clear in the feasibility phase (Dijksterhuis & Silvius, 2017). Therefore, the focus in project management is shifting towards the problem space from the solution space. Design thinking involves three main phases with iterative cycles; the first phase of exploratory focusing on data gathering to identify user needs and define the problem, the second phase of idea generation and the final phase of prototyping and testing (Liedtka, 2015). Non-linear process structure of the phases in design thinking is presented visually in Figure 1. Some specific examples from manufacturing industry can be found in Product-Service System (PSS) (Baines et al., 2007) design. Design thinking as a human-centered problem solving and need-discovery approach, is used with business analytics to build a profitable PSS (Scherer et al., 2016). Service-design thinking (Stickdorn et al., 2011) approach is adopted in a manufacturing firm to explore and create new integrated product-service solutions (Costa et al., 2015). Management of Industry 4.0 projects needs to have a distinct discourse, be flexible and iterative and utilize a creative approach. These projects are tightly linked with the way people work which is directly related to both their capabilities and their ways of thinking. Challenging Industry 4.0 projects entail out-of-box thinking. Thus, there is a need to review tools and approaches. Common design thinking tools/techniques (visualization, storytelling, brainstorming, co-creation, prototyping, field experiments, 307

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Figure 1. An exemplary three main iterative cycle of design thinking

observation and mind mapping) have potential to help surmount the challenges of Industry 4.0 projects. This new approach may overcome the limitations of the dominant model of standard project management and has potential to bridge the gap between a refreshed project management perspective and the tools/techniques in practical use. In this research, the researchers examine to what extent a new approach (design thinking) can be embedded in project management in a manufacturing company which is subject to Industry 4.0 transformation and how design thinking has been adopted in practice.

MAIN FOCUS OF THE CHAPTER Issues, Controversies, Problems This chapter presents the preliminary investigations into research exploring new and promising approaches to better managing Industry 4.0 projects. Organizations require employees to accumulate generic capabilities to function in the 21st century knowledge economy (Benešová & Tupa, 2017; Marnewick et al., 2017; Pinzone et al., 2017). New approaches (i.e. design thinking) accompanying a refreshed perspective of project management provide accessibility to new knowledge and mindsets. Organizations that face the altered circumstances of Industry 4.0 may utilize new tools and techniques giving them the chance to develop new skills such as design thinking. This section of the chapter displays the methodology of the research and case study in a nutshell. Regarding impacts of Industry 4.0 on working life, Schneider (2018) proposes that in depth, qualitative case studies may improve our understanding of sociocultural aspects and the changing role of humans in manufacturing. As we adopted a similar logic of inquiry, we conduct an interpretative case study as a research method (Yin, 2013). The case organization is one of Europe’s leading spare-part manufacturers with a history of operating in the manufacturing industry for almost 50 years. The organization has already initiated a strategy to cope with the challenges of Industry 4.0 and has demonstrated on-going efforts to survive in the competitive industry. It has adopted a corporate innovation system to improve creative and innovative outcomes and inquired into opportunities to become more flexible and responsive

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to changing industry dynamics. In addition, the organization has implemented lean production methods focusing on the creation of customer value through the elimination of production waste and reducing costs (Hannola et al., 2016; Lacerda et al., 2015), which has been used more frequently in discrete manufacturing than in the process sector (Abdulmalek and Rajgopal, 2007). The organization’s focus is on new product development with high health, safety and environmental standards. With this focus, its engineering base and its Research & Development Center, the company has been responding to the changing dynamics of the sector and specific customer demands. An Enterprise Resource Planning system, automated manufacturing lines, robotics and a computerized tracking system at each manufacturing point are part of the company’s assets. The researchers employed semi-structured qualitative methods so that the phenomenon under investigation (adoption of design thinking in Industry 4.0 project management) is explored from conceptual and empirical point of views. Collecting and analyzing data is decomposed into three rounds as seen in Figure 2. In the first round, researchers performed needs assessment and maturity level analysis to understand the organizational capabilities. The researchers synthetized core information about the organization, the plant in general and core processes of product development and project management at the company. The results are evaluated to propose a series of recommendations to the senior management to comply with the challenges of Industry 4.0 transformation. One of the recommendations relates to experimenting with convenient approaches to support the organization to go beyond the offerings of conventional project management in the fourth industrial age in manufacturing. In the second round, data is collected by conducting workshops, which enable observation and in-depth discussions with participants via focus groups. Two researchers’ reflective notes are used to collect written observations of activities, experiences, thoughts and interactions in the workshops. In the third round, publicly available data and document analysis are employed (post-workshop reviews). The researchers examine all project deliverables / project artifacts of the groups (researchers’ reflective notes, groups’ project documents, prototypes and reflection papers of W1 participants). The transcribed verbal data and written data (project documents, reflection documents, and focus group notes) is thematically analyzed using open and axial coding (Baskerville & Pries-Heje, 1999). The unit of analysis is a group of practitioners at different organizational levels. Table 1 displays the characteristics of two workshops involving seven practitioner groups in total. Two distinct entire-day intensive workshops (W1 & W2) were conducted with seven groups in the plant. The first workshop involved three groups of fifteen participants whereas the second workshop involved four groups of twenty participants. The participants chose to join one of the workshops which fit their schedules. The random composition of W1’s participants are mostly the operational staff. W2 is incidentally balanced. On the other hand, W2’s seniority seems to be relatively higher than W1’s seniority. A diverse group of people were involved in both workshops including all types of staff (technicians, experts, engineers, marketing, sales and other administrative staff) and managers at different seniority levels. Seven cross-functional groups (each with five participants) were formed. W1 groups were given a domain independent project with high uncertainty and complexity, and were invited to adopt design thinking in hands-on group projects. Domain knowledge was low due to the context unrelated project. W2 was conducted one week later. Meanwhile the organization’s top management asked the researchers to work with a domain related project. Thus, the project of W2 groups involved medium uncertainty along with high complexity and domain knowledge. In addition, top management emphasized their preference for the immediate completion of a context related project applying conventional project management instead of the adoption of a new approach (design thinking). Top manage309

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ment’s intervention caused the researchers to make a number of adjustments in W2 characteristics and structure as explained in Table 1 and Table 2. Figure 2. Data collection and analysis

Table 1. Workshop characteristics (group related and project related) Workshop 1

Group related Characteristics

Project related

310

Workshop 2

Number of participants

15

20

Number of staff (S) & managers (M)

S: 12, M: 3

S: 10, M: 10

Number of groups

3

4

Seniority level

Low to Medium

Medium to High

Diversity

High

High

Project management literacy

Low to Medium

Low to Medium

Project subject

Context free

Context related

Project domain knowledge

Low

High

Project uncertainty

High

Medium

Project complexity

High

High

Approach adopted

Design Thinking

Conventional

 Adoption of Design Thinking in Industry 4.0 Project Management

Table 2. Workshop design

Session 1

Workshop 1

Workshop 2

Kickoff / Warm up

Project management literacy Contemporary approaches to project management

Focus groups

Organizational project management practices / participants’ experiences / examples Organizational, departmental and individual problems / issues in managing Industry 4.0 projects

New approach introduction

Design Thinking

Preparation for Session 2

Project subject introduction Random group formations

N/A

Session 2

Break 1st Round

Hands-on group projects adopting design thinking approach

2nd Round

Evaluation rounds & iteration for continuous improvement

Hands-on group projects via conventional project management approach (project-specific questions provided for guidance)

The workshops were divided into two sessions (Session 1 and Session 2) and were structured as seen in Table 2. Session 1 takes three hours. The researchers think that involving the participants in discussion and reflection on their own actions and intentions makes sense (Harrell & Bradley, 2009). Session 1 decomposed into four parts. The first part, which is the kickoff, began with a warm up and lasted approximately thirty minutes. One researcher started the discussion on overall project management literacy and contemporary project management approaches. The researcher delivered generic questions to confirm the background and ensure the common understanding of the project management domain among all participants. Before conducting the workshops, the first round of data collection (needs assessment and maturity level analysis) revealed that the participants demonstrate low to medium project management literacy. Likewise, the first part of Session 1 provided confirmation to the same level of project management literacy and competency (low to medium). The second part of Session 1 was reserved for focus groups (see Table 2). Two focus groups were conducted in two workshops because this is helpful for investigating complex behaviors and opinions, and for collecting a diversity of project management experiences (Clifford et al., 2016). The number of participants was fifteen and twenty respectively. Groups were made up of diverse participants who already knew each other through work and had encountered common problems in challenging Industry 4.0 projects. The researchers utilized focus groups as dynamic group discussions providing insights about the type of issues they had confronted. Thus, the method permits rich discussions of project management challenges. Data collection through focus groups was partially structured. Two researchers formulated the questions. Descriptive questions were asked to better understand the organizational project management practices. One researcher took the role of facilitator/moderator, allowing interactions between participants and keeping discussions on topic. The researcher, who acted as moderator, facilitated the free-flowing discussion and encouraged participation to provide rich description and examples (Harrell & Bradley, 2009). Focus groups were conducted in the workshop days and at the plant in a U-shaped meeting room allowing face-to-face interaction, lasting approximately ninety minutes. The third part of Session 1 was allocated for new approach introduction (design thinking). None of the participants had any experience of design thinking methods. At the beginning of the third part, one

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researcher referring to notes taken in focus groups summarized the management issues which had arisen in the case organization in Industry 4.0 projects. The researchers were offered a route to partial insights into what participants do and think via focus groups (Clifford et al., 2016). The issues derived from the focus groups helped the researchers to link the challenges of Industry 4.0 project management to the benefits of new approaches such as design thinking. One researcher presented a refreshed perspective of project management in the new era and new methods that may contribute to solving complex problems in 21st century society. Design thinking approach and several of its tools/techniques (such as visualization, brainstorming, prototyping) relevant to the case organization were introduced. The alignment of Industry 4.0 project management and the potential advantages of the new approach were discussed. This took approximately one hour. The third part of Session 1 was only held in W1. It was not duplicated in W2 due to the intervention of top management. In W2, instead of new approach introduction, dynamic group discussion via focus groups was held, enabling extended discussion of participants’ experiences and problems. Hence, the focus group of W2 took nearly one hour longer than the focus group of W1. The fourth part of Session 1 was reserved for preparation for the hands-on group projects in Session 2. In this last part, the participants randomly formed the groups, the researcher introduced the project subject/problem, and the session ended. Session 2 started after the thirty-minute break and took four hours. This session was held for handson group projects. Participants worked in groups to submit requested project deliverables/artifacts at the end of Session 2. Meanwhile, two researchers observed the groups, answered questions and took notes. In Session 2, W1 groups were assigned a context free project subject having high complexity and high uncertainty. All three groups of W1 were invited to adopt a design thinking approach in hands-on group projects, and were provided design thinking tool kits (plasticine, markers, post-its, highlighters, pencils and pen, tape, dot stickers in various colors, paper sheets in various sizes and colors, glue, scissors etc.). In the first round, applying a three phased design thinking approach (exploration, ideation, prototyping and testing) and using three design thinking tools (visualization, brainstorming, prototyping) the groups were asked to develop problem solutions for the context free project subject in a set time. In the second round (evaluation round), each group visited other groups’ project artifacts (documents, prototypes, visuals, paper sheets), provided feedback and put up stickers for visualizing votes (red stickers for problematic issues, green stickers for favorable aspects of a proposed solution). At the end of the evaluation rounds, problematic issues (red intense areas) became apparent to everyone. All groups reached a consensus on the most appropriate solution. Then, continuous improvement started due to the iterative nature of the approach. In addition, the researchers had the W1 participants complete a reflection document (problem solving framework) to explore how they constructed the task and proceeded with the solution process, and to understand the way the participants think and work. The original research design involved assigning two context free project subjects and the same project tasks to the participants of W1 and W2 in Session 2. The project groups in W1 would adopt a design thinking approach, whereas the project groups in W2 used conventional project management approaches. The aim of the hands-on group projects was to assess the impact of design thinking approaches on the participants’ perception of the project task and its context. However, in Session 2 due to the senior management’s intervention, W2 groups were assigned a context related project and applied conventional project management approaches. Therefore, four W2 groups were given various exercises / project tasks and provided further project-specific questions to further define the project’s problem and find their ways in hands-on group projects, in contrast to the W1 groups. Table 3 summarizes the exercises involved in the workshops. 312

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Table 3. Various exercises in workshops Workshop 1

Workshop 2

Project subject

Context free

Context related

Approach adopted

Design Thinking

Conventional

• Developing problem solution to the context free project subject (free format visuals, prototypes) • Writing reflection document (brief explanation of problem-solving framework)

• Developing project deliverables/artifacts (draft versions): Strategic positioning Project organization Project life cycle model Stakeholder analysis Responsibility Assignment Matrix (RAM) Project charter Project scope statement Work breakdown structure (WBS)

Exercises

In the third round of data collection, two researchers conducted post-workshop reviews. All project deliverables / project artifacts of all groups (researchers’ reflective notes, groups’ project documents, prototypes and reflection papers of W1 participants) were examined and publicly available data was analyzed.

SOLUTIONS AND RECOMMENDATIONS This section of the chapter discusses the results from the case study and argues for a new perspective in Industry 4.0 project management. Two researchers observed the participants, held in-depth discussions and reviewed the hands-on project deliverables/artifacts and reflection papers. Evidence suggests that, In W1, •

•

•

Level of abstraction was not adequate to manage exploration and ideation phases: Exploration and idea generation stages require extensive conceptualization. Understanding, thinking, abstracting and evaluating the project subject/problem leads the participants to brainstorm, negotiate and plan for action for framing the problem, developing solutions and solving the problem. These two stages require intensive cognitive skills and effective communication. Groups were asked to outline the problem formulation via reflection papers. Mapping the problem to higher constructs was inadequate. Prototyping/Testing was far from being implementable: Prototyping and testing stages engage participants in discovery and understanding of the nature of the problem. Project subjects with high complexity and uncertainty requires iterative inquiring and assumption elimination. Low project domain knowledge could be eliminated through constant questioning. W1 groups lacked sufficient inquiring and proper representations. Various range of models and prototypes was observed: Two groups generated poor models and prototypes while only one group generated a slightly better or more representative version. Inadequate investigation during exploration and idea generation stages affected the richness of models and prototypes negatively.

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Self-defensive behavior of participants was observed when receiving feedback from others: In evaluation rounds participants lacked the personal attribute of confidence and exhibited self-defensive behaviors. Justifying own ideas and trusting own professional abilities were poorly realized. Groups rarely embraced others’ comments, expertise and know-how. Being open and responsive to diverse perspectives and accepting feedback from other groups was limited.

•

In W2, • •

•

•

Well defined stages/documents/deliverables but lack of creativity (similar solutions developed) was observed: Following the set of project-specific questions provided by the researchers, groups produced project artifacts which were satisfactory, implementable, but quite similar. Groups just focused on completing the deliverables of the context related project (resultoriented groups): The most aggressive constraint in the organization’s projects is time. Groups were exposed to time pressure in almost all departmental and/or cross-functional projects. Such time pressure forces participants to complete the work as soon as possible without any distraction and time wasted. The acquired practice continued in the workshop. Groups solely concentrated on completing the project artifacts as they normally do. Groups were too confident of the way they work (near to paradigm blindness): Four groups were overconfident of their way of working. Having good command of domain knowledge and the given context related project comforted the groups. However, the excess of confidence restricts the embracing of innovative ideas. The groups perceived that they had demonstrated the best way of working, which may be regarded as an indicator of paradigm blindness. It seems contradictory to have such a perception while attempting to improve organizational project management capabilities using new approaches. The organization needs to be aware of challenges regarding paradigm blindness. Openness to learning new ways of working was low: The overconfidence mentioned above resulted in poor acceptance of new ideas. The capacity to deal with new approaches is limited.

Although top management declares that it is seeking new approaches to successfully manage Industry 4.0 projects and to become more flexible and agile in addressing various dimensions of complex transformation processes, it seems that their main priority is to have the context related project completed as soon as possible, rather than facilitating new ways of thinking and working. The organization does not seem able to let the participants invest time and space to co-create and learn new skills. When employees do not have prior experience with such new methods (design thinking) and top management is too keen to have the work rapidly carried out, challenges to adoption may lead to giving up on the method without becoming aware of its potential benefits (Seidel & Fixson, 2013). However, the skills needed to adopt a new method/technique successfully develop in an individual over time. Design thinking is a way of thinking that leads to transformation, evolution and innovation, to new forms of living and to new ways of managing business (Tschimmel, 2012). The approach provides a way of thinking that is close to a philosophy to change a company’s traditional culture into a dynamic culture that has a core competency of transformational readiness (Ochs & Riemann, 2017). Literature regarding the professional competences useful for Industry 4.0 project management and competency models for Industry 4.0 employees (Cerezo-Narvaez et al., 2017; Prifti et al., 2017) highlights people’s (human capital) role in competing in the digital age. 314

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Ochs & Riemann (2017) propose a comprehensive approach to overcome the traditional perception that change is not an episodic element but a continuum. Transformation towards Industry 4.0 proceeds in non-linear and overlapping phases (evolutionary pace). Industry 4.0 does not only encourage a continuous change regarding technology, but also regarding the DNA of a company. In this context, innovative thinking activates new business capabilities and replaces traditional silo mentality. In such circumstances, traditional change management methodologies have limitations and a search begins to find new approaches to support the transformation leading to new elements/approaches in which design thinking is also involved. This new approach does not only support the innovation processes but also encourages a corporate culture enabling seamless Industry 4.0 transformation.

FUTURE RESEARCH DIRECTIONS Conceptual implications based on the challenges cited and empirical implications of the research are summarized in Table 4. Our empirical findings suggest that organizations can be stuck into being closed to innovation and tend to manage Industry 4.0 transformation without taking into account business model orientation. Industry 4.0 encourages new business models as well as new products and services, which may call for innovation in Industry 4.0 project management. The researchers believe that understanding pragmatic behaviors to deal with ill-suited project contexts for Industry 4.0 project management is an open issue for both researchers and practitioners. Table 4. Conceptual and Empirical Implications Notions/Subjects

Conceptual Implications

Empirical Implications (Adoption context)

Change governance (centralized vs decentralized)

Industry 4.0

Continuous evolutionary vs revolutionary approach Technology push vs managerial human centric approach Automation vs T-shaped new capabilities Different ecosystem (open innovation / projects in network) Uncertainty & complexity

Industry 4.0 Project Management

Emerging projects & products (unknown goals, technologies, methods, market & customer requirements) Firm-level strategizing Mobilizing stakeholders for formative project context Open, complex problems

Design Thinking

Complex thinking process Lack of definitive solutions

1) Characterizing Project and/or Problem situation • Project subject: Context free vs context dependent • Project complexity: high • Project uncertainty: high vs medium • Project domain knowledge: low vs high • Approach adopted: Design thinking vs conventional 2) Behavioral approach for uncertainty and complexity handling (Individual & Team level) • Incompetent problem formulation • Lack of proper inquiring and representation • Weak modelling and prototypes • Self-defensive behaviors and a deficit in the personal attribute of confidence 3) Appropriate learning style and culture (Team & Organizational level) • Lack of openness to learning new ways of working • Priority is time-to-market with opportunistic agenda rather than visionary outlook • Lack of encouragement for investing time and space to co-create and learn new skills

Uncertainty reduction

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Standard project management measures the project’s success using triple constraints (time, cost, quality). However, a broader and fresher perspective, focusing on business- and entrepreneurial-oriented dimensions such as strategic value, success in the market and value propositions is needed in new project contexts. In such cases, uncertainty of goals causes expectations to be uncovered through the project and leads to an iterative approach and trial-and-error type management (Pajares et al., 2017). Minimum viable products (MVP) are generated, tested in the market, and enhanced (iterative process). MVPs are utilized to find a convenient business model. The researchers believe that the question of how to leverage emerging techniques utilized in complementary areas (e.g. lean start up) for Industry 4.0 project management is one possible future research direction (Ries, 2011). One can expect that in many cases, change governance can be centralized (top-down) but may not be aligned with firm-level strategizing. This approach neglects the importance and contribution of stakeholders. This research (the case organization) demonstrates high dependency on a single supplier. Industry 4.0 projects require the commitment of people and organizations with multidisciplinary expertise and abilities constituting an ecosystem. Open innovation is compulsory in firm networks. The above-mentioned attributes of Industry 4.0 project management (iteration, trial-and-error, learning, exploration, innovation, cross-disciplinary competences) are common characteristics of design thinking as well. In fact, these are the themes or concepts where the three notions of Industry 4.0, project management and design thinking converge. Pajares et al. (2017) suggest there is a need to investigate new methods utilizing the learning-emerging approach beyond the agile framework, and emphasize the link between innovation management and project management practice; researchers concerned with innovation need to inquire into the potentials of the design thinking method.

CONCLUSION This research is concerned with adoption of design thinking as a new approach to transformation of organization by Industry 4.0 projects. In particular, we examined a real case of two project contexts where traditional and design thinking-oriented project management approaches were adopted. The understanding of Industry 4.0 is revisited and expands through a multidimensional viewpoint addressing project context, uncertainty and complexity handling, learning style and culture. The common discourse states that the fundamentals of Industry 4.0 appear to be built on a technology push, whereas in practice it requires a holistic change regarding managerial and human centric aspects. This emerges at a continuous evolutionary pace rather than as a big bang or revolution. It necessitates automation, while requiring T-shaped, multidisciplinary new capabilities. Furthermore, a mindset shift is needed from production thinking towards design thinking. In recent years, Industry 4.0 projects are becoming increasingly strategic transformation-oriented rather than short-term outcome oriented. These projects require intra-company and cross-company integration and collaboration in and among networks in a different and innovative ecosystem, instead of competition. Design thinking and project management in the Industry 4.0 context are held together as transformation factors and processes, evolving and shifting toward a strategic perspective to embrace recent advances in the socio-technical, digital and economic landscape. Balancing the stakeholders’ needs and expectations through an integrative view is the common focus of both fields. Individual creativity in the design field expands into the collaborative design of project teams. As a result, Industry 4.0 projects adopting design 316

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thinking might be associated with various innovation types including business model, organization and process innovation at the firm and network level through project portfolios. Deciding whether, and to what extent, design thinking needs to be adopted in practice in Industry 4.0 project management is a challenge. In this study, the researchers emphasize the need for aligning agile approaches such as design thinking with project management approaches in Industry 4.0 context. There is some agreement between innovation management and project management in this new context. The convergence of these two subject areas (design thinking and project management in Industry 4.0 context) and their alignment can be examined by utilizing various theoretical lenses such as organizational maturity, organizational learning and complexity handling. An organization’s Industry 4.0 readiness and maturity need to be discussed using a holistic viewpoint embracing various dimensions such as technology, people, culture, strategy and leadership. The willingness and competence of management, the competence of employees, and the openness of people to new approaches, as well as the readiness to invest time and space for collaborative learning, and to encourage creativity through new approaches are vital links in the Industry 4.0 chain. The design thinking approach promises to contribute to the enriched perspective on project management and deserves further study.

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ADDITIONAL READING Almeida, A., Tereso, A., Faria, J., & Ruão, T. (2018, March). Knowledge Sharing in Industrialization Project Management Practices. In World Conference on Information Systems and Technologies (pp. 5362). Cham: Springer. 10.1007/978-3-319-77703-0_5

KEY TERMS AND DEFINITIONS Complex Transformation: The significant change of status quo (state) that triggers adoption of socio-technical elements to the context perceived as critical to system sustainability. Design Thinking: A way of complex problem solving by means of creative, iterative, learning focused, collaborative, experimental and explorative approach.

This research was previously published in Agile Approaches for Successfully Managing and Executing Projects in the Fourth Industrial Revolution; pages 80-98, copyright year 2019 by Business Science Reference (an imprint of IGI Global).

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The Project Management of Industry 4.0 Strategy for Software Houses Ufuk Cebeci Istanbul Technical University, Turkey

ABSTRACT Nowadays, Industry 4.0 is becoming a strategic issue for software companies. Because of fast digital conversion, they should review their visions and strategies. In this study, a project management framework is proposed for software companies considering Industry 4.0 as a future strategy. Global ERP firms try to find a good integration of ERP and Industry 4.0 applications. A global ERP firm’s solution partner is used as a case study in this chapter. The study includes: the development of an internet-based portal application that integrates all their business partners (customers, suppliers); a collaborative project management software; and an industry 4.0 portal. The benefits of this study after applying in the software house are explained.

INTRODUCTION Software companies, including global ERP firms’ solution partners, must be involved in industry 4.0 applications, because in the near future their customers will ask industry 4.0 applications. If they do not satisfy their customers about industry 4.0, they will lose some of their customers. Therefore industry 4.0 strategy is vital for them. On the other hand, industry 4.0 software companies need information gathered from ERP systems. It is very important to manage industry 4.0 projects collaboratively for successful customer industry 4.0 applications of software houses. Industry 4.0 is an important strategy not only manufacturing companies but also software houses. According to a study, German companies from different sectors are expected to invest 650 million Euros into Industry 4.0- related technologies and applications in 2015. (Bitkom, 2015). But Erol et al. (2016) state that companies have substantial problems to grasp the idea of Industry 4.0 and relate it to their specific domain. DOI: 10.4018/978-1-7998-8548-1.ch017

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 The Project Management of Industry 4.0 Strategy for Software Houses

Lot sizes are getting smaller. Therefore, a greater level of customization for manufacturing companies is necessary. The customers push the ERP software vendors beyond classical ERP. Industry 4.0 solutions can integrate agile supply chains both vertically and horizontally. The manufacturing companies need to be smarter and agile. The rest of the paper is organized as follows: Literature review is explained in the next section. How to manage industry 4.0 strategy for software houses section explains the proposed framework. In the case study section, the proposed methodology is detailed. Further research directions are discussed into Future Research Directions section and results are evaluated in Conclusion Section.

BACKGROUND There is no scientific research regarding a software house’s industry 4.0 strategy. IT solution providers are developing internet-based applications to enable integration with their external counterparts. With the development of cloud technology, the solutions offered in the internet environment become unlimited. When examining existing solutions, there are some specialized applications such as CRM, B2B. Oztemel and Gursev (2018) reviewed the industry 4.0 related technologies in literature. They explain some leading countries’ investments and activities. For example, China spent approximately $200 billion on research and development, the second-largest investment by any country. (McKinsey, 2017). France, the second biggest economy of European Union, started “New Industrial France” initiative in 2013 to be an innovation leader country and to push the technological frontier to create the products and the uses of tomorrow (DEF., 2016). United States, Japan, Germany, (the origin of Industry 4.0 concept, Kagermann 2013), South Korea are aware of Industry 4.0 and start similar projects. In Turkey, TUBITAK (Turkish Science and Technology Research Council) funds the original and value-added industry 4.0 projects. Moghaddam and Nof (2017) stated a collaborative control theory to provide a framework of the collaborative factory of the future. Wollschlaeger et al. (2017) studied the impact of IoT (Internet of Things) and CPSs (Cyber-Physical System) on industrial automation from an industry 4.0 perspective, used a survey of the current state of work on Ethernet time-sensitive networking (TSN), and shed light on the role of fifth-generation (5G) telecom networks in automation. Preuveneers and Ilie-Zudor prepared a survey and had an analysis of emerging trends, research challenges and opportunities in Industry 4.0. Ellialtioglu and Bolat (2009) proposed a conceptual framework for building supply chain strategies to meet marketplace requirements and to give an insight into the managers of supply chains. The Agile methodology was explained with Agile Manifesto in 2001. (Larson & Chang, 2016) Kaur and Singh (2016) reviewed critical success factors in agile software development projects in India. Whitney & Daniels (2013) study the primary causes of IT project management failure and complexity. Khalid analyzed the application issues of SME’ cloud computing. Schumacher et al. (2016) define a maturity model for assessing industry 4.0 readiness and maturity of manufacturing enterprises. The first group of dimensions “Products”, “Customers”, “Operations” and “Technology” are formed to assess the basic enablers. In addition, the other groups of dimensions “Strategy”, “Leadership”, Governance, “Culture” and “People” allow for including organizational aspects into the assessment.

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Erol et al. (2016) proposed a 3-stage model for the transformation of industry 4.0 in manufacturing firms. The first stage is “envision.” The involvement of top management and commitment is approved and shared with the employees in this envision stage. The external experts explain the industry 4.0 and the importance of doing it in a collaborative way. The second stage is “enable.” The roadmapping of industry 4.0 is prepared in the enable stage. The roadmapping is prepared for short-term, mid-term and long-term. Internal and external success factors are identified. They used 4 perspectives; market, product, process, and value network. The last stage is “enact” stage. The preparation of transformation is done in this stage. The proposals of Industry 4.0 projects are formed again in this stage. The output of the stage is roadmaps for every project. Chesbrough (2006) proposes a practical definition of business models and offers a Business Model Framework (BMF) that illuminates the opportunities for business model innovation. Albers et al. (2016) study spring coiling production process and prepare a procedure with value-added partners, for intelligent condition monitoring quality control system. The intelligent quality control system focuses on the initial phase of an industry 4.0 project.

HOW TO MANAGE INDUSTRY 4.0 STRATEGY FOR SOFTWARE HOUSES A vision defines the future directions of the companies and a strategy explains the roadmap. Nowadays, software companies need to include industry 4.0 strategy for sustainable growth and to achieve their vision. A vision of software house should be prepared by involving employees. In addition, it should be shared with all employees. A SWOT Analysis is very useful to focus and define strategies. Strategy maps are very useful to find out KPI’s (Key Performance Indicators) for industry 4.0 strategy. After defining industry 4.0 strategy, the software house should start strategic project(s). Industry 4.0 contains many concepts and expertise such as cloud computing, cyber physical systems, internet of things, big data, etc. Therefore, it is very difficult for a software company to serve its customer with all types of components of industry 4.0. It may outsource different services (including some software) and various hardware components. The software company can provide the other services. How the project management framework of Industry 4.0 Strategy for software houses is managed is demonstrated in Figure 1.

Define Vision and Share It First, the software house should define its vision. Because it shows the focused target and it may affect almost every activity of the firm in the future. The vision should be both difficult to achieve and realistically accomplishable. The vision should be shared with the employees and explained to them clearly. If the employees are involved in the stage of defining the vision, their motivation will be increased and there will be a synergy.

Define Strategic Project for Strategy Industry 4.0 One of the strategies of a software house is to be an industry 4.0 supplier for present and potential customers.

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Figure 1. The project management framework of Industry 4.0 Strategy for software houses

Then, the company should select proper strategic projects according to the conditions of customers and its own realities and starts by using agile project management techniques for this strategy. Industry 4.0 strategy may change frequently, because it has been getting new concepts and technologies. Companies may give up even some technologies.

Make or Buy Decision Since industry 4.0 includes dozens of applications, a software-house needs to determine which applications to be developed and which applications to be outsourced according to its strategy and constraints. These constraints can be human resources qualifications, capital, financial advantages, other areas to be focused on etc. If the company is a solution-partner of a global ERP firm, it can use industry 4.0 portal and other solution partners such as cyber-physical-system expert firms. It is easier to decide on make or buy decisions after a cost-benefit analysis. However, the firm should not outsource its confidential or critical processes and products. A cost-benefit analysis may be required to make or buy decision. The analysis is used for estimating the strengths and weaknesses of alternatives. A common unit of measurement should be defined in order to reach a conclusion.

Realizations of the Activities Activities are managed according to the agile project management techniques. Because of fast changes in these technologies, the company should be aware of and prepare action plans according to different scenarios.

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The process ends after all activities are finished.

CASE STUDY Different software houses are visited for this study. MBIS Software Company is selected for this case study, because: • • • • • •

It is SAP’s first “Gold Partner” in Turkey. It has implemented 500 projects in 350 companies from various industries such as automotive, mining, food, machinery manufacturing, metal, pharmaceutical, chemical, health care, cement. The company has ISO 9001 Quality and ISO 27001 Information Security system certificates. The firm has applied many solutions, especially SAP solutions in hundreds of domestic and international companies from all industries and from all sizes. It has a large industry-specific expertise obtained from projects that were implemented in various industries. It offers process design, end-to-end information systems design, industry 4.0 project consulting, hardware, and infrastructure services to customers wishing to have a digital transformation.

With the SAP S / 4 HANA system, the SAP solution family, which comes in a different dimension, forms the backbone of a transformation story from the ground up with Hybris, Leonardo (industry 4.0 platform of SAP) and the SAP Cloud Platform.

Define Vision and Share It The vision of the MBIS is, “To be the first SAP solution partner of the customers in Turkey, in neighbor countries and in South East Europe.” The vision is explained to the employees and shared by top management.

Define Strategic Project for Strategy Industry 4.0 The project is selected according to the total number of employees, distribution of employees, their competency, their expertise. After a feasibility study, the project is selected. The selected strategic project is: • •

First, an agile project management software development both their own use and customers’ use. It is called Jimarin, Applications called Wega Port consisting of Customer Web Gate and Supplier Web Gate.

Make or Buy Decision The company selects a solution partner, which is an expert about cyber-physical-systems for manufacturing applications. The company realizes other activities, itself.

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Realizations of the Activities Jimarin project is finished. Jimarin (www.mbis.com.tr/en/products/mbis-jimarin-en): You can follow your internal affairs with e-mail systems of your company through the Jimarin Management Portal. In the same way, you can follow up your projects or investments from Jimarin on a process-byproject basis. Because it is a cloud-based software, it does not put you at the cost of additional hardware. As you are connected to social media tools, Jimarin can be linked to the portal; Follow your work, assign tasks to your employees, and organize your meetings. Jimarin is a management instrument at your elbow from every medium you visit. With Jimarin’s staff, you can analyze their performance according to the resolution period of your tasks so that you can see the places that are in trouble more easily. With the advanced dashboard structure, you can see the vital values from the admin console more easily with graphs, and this structure also allows you to customize with detailed reports. Instead of losing time in your electronic mail for your company, employees and departments in different folders you create in your e-mail box, you can organize your company into Jimarin and follow your work, assignments, and projects from one screen to another. Gymnastics helps you achieve immediate results with notifications. Another project is Wega Port (Çetin et al., 2017): When examining existing solutions, we see specialized applications such as CRM, B2B, SRM. The companies that use these portals have great advantages in terms of cost and in terms of efficiency if they are combined in a single platform. Thus, both companies that use these systems get solutions at a reasonable cost and very fast, as well as allowing IT companies to develop new solutions for all users in a cloud environment. There are areas of expertise in such solutions. For example, human resources, procurement, financial management, task follower. This process leads to different solutions which are independent of each other on the internet. Companies are also using many portals and mobile apps at the end of the day. Another portal for suppliers, another portal for customers, employees, customs brokers, and other portals for transporters. Considering the use of these ports is two-sided, it can be seen that there is actually an integration problem for the other side. For example, a company using a purchasing portal intends to build a portal of all its suppliers. But the purchase of the firm means the sale of the other company. However, the CRM system used by the supplier company is not integrated with this portal. These needs have been taken into account when developing the WEGA product. This product enables all business needs to be met on one platform and integration with other cloud solutions. The portal environment to be developed will have the ability to collect data from company systems and devices. In this way, for instance, the management of the transport operations with the data coming from the vehicles will be provided. Thus, WegaPort will include IoT functions. WegaPort will be integrated with the SAP Leonardo IoT platform. The Portal application we are going to develop will have an advanced level of Industry 4.0 capabilities. In this way, data collected via WegaPort will be processed by machine learning, big data or predictive analytics tools.

Functional Components of WegaPort Our product will include the following modules in the first place: 327

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• • • • •

Customer / Dealer Portal Supplier Portal Service Portal Forwarder Portal Customs Broker Portal

Generic Components of WegaPort While our product creates all this portal environment, it will also present some basic features to all users. • • • • • • • • • •

Notification Management Module: Indicators produced in the system will be conveyed to the relevant person as a notification. Instant Messaging: Messaging between system users will be possible. Questionnaire Module: Surveys to business partners such as customers, vendors, forwarder will be managed by this module to improve business processes. Audit Module: Monitoring the interlocutors that the firm receives services from, in certain periods, loading and grading of evaluation results into the system, functions like follow-up of the work list will be managed with this module. IoT Integration Module: The data from the “things” will be transferred to the system via this module. Business Intelligence Reporting Module: The data in the system can be converted into business intelligence reports, which can be generated dynamically by the user. Work flow Management Module: The workflow module will be used in flows where the need for approval is required. Users will be able to define their own workflows. Mobile Access Module: IOS and Android-based mobile devices will be able to access and use the system. Security Module: The security module will be used to provide information security in the collaborative environment created. SAP Leonardo Integration Module: The data will be integrated with the SAP Leonardo solution for analysis and forecasting. This integration will be established by means of this module.

SAP Leonardo is a technological platform developed by SAP AG, the world’s leading business solution provider, that connects objects and business processes. The structure of the platform is shown in Fig. 2. SAP AG, managed to connect the business processes, MBIS will integrate the WegaPort platform to the IOT world with SAP Leonardo Platform. The basic functions of the Leonardo Foundation are as follows and in Figure 2: • • •

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Supports all message protocols required for object linking Enables management of devices by taking security protocols into account Supports other technologies required by the IOT world (Data Streaming, UI, Geo-mapping, Predictive Analytics etc.)

 The Project Management of Industry 4.0 Strategy for Software Houses

Figure 2. SAP Leonardo (https://blogs.sap.com/2017/07/14/sap-leonardo-live-2017-frankfurt)

DETAILS OF WEGA PORT 1. Customer Portal Dealers, customers, sales staff will be able to create orders from users in the role. Tracking created orders includes the ability to track financial situations. The features of this module can be summarized as follows: • • • • •

Order Management Functions Credit and Risk Management Functions Delivery Tracking Functions Invoice Tracking Functions Business Intelligence Reports (Orders / Deliveries / Invoices / Financial Status) The functions of the Customer Portal are detailed below:

a. Admin Functions • • • • • •

User management Role-based authorization Site theme and ad area management System access log analysis Translation: The requested languages are entered into the dictionary to enable the system to work in other languages. SAP integration settings

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b. Order Functions • • • • • • • • • • •

Product listing Order entry Create basket Copy order Create order from template Order list Order credit check Order payment Contract listing Creating orders from a contract Order confirmation

c. Delivery Functions • •

Delivery list Delivery receipt confirmation

d. Billing Functions • • •

Invoice list Sales reports Payment: Direct payment by credit card

e. Reporting • • • • • • • •

Current status reports Extract Open document listing (all FI documents such as check, collateral, etc.) Listing of accredited documents Inventory report Price report Sales performance report Sales staff, affiliated dealer performance report

f. Notification Management (Announcement / Warning) • • • •

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Automatically send mail to the user Order confirmation workflow Send a SMS to the user Customer complaints and suggestions entry

 The Project Management of Industry 4.0 Strategy for Software Houses

g. Document Management • • •

View product documentation Display contract documents Display delivery related documents (Quality certificates)

h. Survey Management • • •

Defining the survey Periodic survey repetition Analysis of the survey results

2. Supplier Portal The role of the dealer / customer / salesperson in the role includes the ability to open orders, track orders, track financial situations. The features of this module can be summarized as follows: • • • • •

Order management functions Credit and risk management functions Delivery tracking and traceability functions Invoice tracking functions Business intelligence reporting

3. Service Portal • • •

Service request management Maintenance / service activity management Service costs follow

4. Forwarder Portal • • • •

Through this portal, it is aimed to carry out the transactions of the companies or users involved in transportation operations. Freight forwarding planning functions Drivers’ goods reception-transport-goods delivery functions Vehicle tracking functions

a. Shipping Service Process The entry of loading request to the system: The upload request will be entered as a transport order in the system. Different methods will be used to create transport orders. • •

Web service integration Mobile application for IOS and Android 331

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• •

Manually via Portal Loading Requests Planning: The created loading requests will be optimized and transport documents will be created in the system. Load-Driver Assignment: Load requests will be assigned to driver and license plate. The driver will then be able to see the load assigned to him on the mobile phone application. Accepting the Load: The load will be accepted by the car driver with the mobile application. In the cellular phone, the work order related to the load request will be marked as received and the status in the system will be updated. Delivery of The Load: Load will be recorded in the system by taking a photograph at the moment of delivery to the buyer.

• • •

Coordinate information of the hand terminal will also be recorded in the system during freight delivery. • • • •

Vehicle Tracking: The collection of the data from the vehicles will be integrated with SAP Leonardo platform. The following functions will be used on this platform: Vehicle location tracking and traceability for nonconformities Automatically calculating the arrival time of the load and informing the receiver Automatic alarm generation according to predefined warning points by the users according to the data taken from the vehicle

b. Portal Functions The portal will be designed for manufacturers. This portal will have the following functions: • • • • • • • •

Create goods order Automatic Load order creation service (for creating automatic load order for external systems) List or change load order Goods tracking system on the map Billing report (load associated) Status report Load handing photos display page Alert system: Send an e-mail to the interested parties after delivery.

c. Mobile Functions With the Android-based application to be developed, drivers will perform the following functions: • • • • • •

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To be hold assigned freight list Load acceptance Unloading approval Cargo delivery confirmation Taking and loading photos Signing the loading acceptance by pen on the device

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5. Customs Broker Portal It is aimed that the customs firms carry out the transactions related to the import / export processes. • • •

Customs files management function Import / export costs management function Document preparation function

MBIS industry 4.0 team also applies the framework and the developed software in a manufacturing firm successfully. The benefits of MBIS after the study: • • • • • •

The clear and shared vision with employees Well-defined strategy and strategic project related to industry 4.0. Successful industry 4.0 software products and continuous improvements in these products Better image Increased sales New R&D projects

FUTURE RESEARCH DIRECTIONS A global ERP firm’s solution partner is used successfully as a case study in this paper. The framework can be converted to a decision support system including some recommendations. These recommendations can be derived from literature and industrial experts. A study for determining the maturity level of software houses may be another area. After clustering the software houses, the right directions can be prepared for the managers of software houses.

CONCLUSION In this study, a project management methodology is proposed for software companies’ industry 4.0 strategy. The framework is applied in a global ERP firm’s solution partner and the results are satisfactory. For the software companies, Industry 4.0 and its transformation are a must strategy. The top managers should evolve their company in the right way and continuously. The author of this study has not seen a research regarding a software company’s industry 4.0 strategy. The benefits of this study after applying in a software house: • • • • • •

Clear and shared vision with employees Well-defined strategy and strategic project related to industry 4.0. Successful industry 4.0 software products and continuous improvements in these products Better image Increased sales New R&D projects 333

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The developed framework can be used as a part of Industry 4.0 strategy. It can also be used for other types of projects.

ACKNOWLEDGMENT This research was supported by MBIS Software Company. They provided the necessary data and information for this study.

REFERENCES Albers, A., Gladysz, B., Pinner, T., Butenko, V., & Stürmlinger, T. (2016). Procedure for defining the system of objectives in the initial phase of an industry 4.0 project focusing on intelligent quality control systems. Procedia Cirp, 52, 262–267. doi:10.1016/j.procir.2016.07.067 Çetin, M. Y., Cebeci, U., & Kocabaş, C. (2017). A new approach to gather different cloud solutions on a single platform. In International Symposium on Industry 4.0 and Applications (ISIA 2017) (pp. 71-74). Chesbrough, H. (2007). Business model innovation: It’s not just about technology anymore. Strategy and Leadership, 35(6), 12–17. doi:10.1108/10878570710833714 DEF. (2016). Retrieved from https://www.economie.gouv.fr/files/files/PDF/web-dp-indus-ang.pdf Ellialtioglu, B., & Bolat, B. (2009, July). A proposed conceptual framework for building supply chain strategies to meet marketplace requirements. In International Conference on Computers & Industrial Engineering CIE 2009 (pp. 886-891). IEEE. 10.1109/ICCIE.2009.5223878 Erol, S., Schumacher, A., & Sihn, W. (2016). Strategic guidance towards Industry 4.0–a three-stage process model. In International conference on competitive manufacturing (Vol. 9, pp. 495-501). Kagermann, H., Helbig, J., Hellinger, A., & Wahlster, W. (2013). Recommendations for implementing the strategic initiative INDUSTRIE 4.0: Securing the future of German manufacturing industry; final report of the Industrie 4.0 Working Group. Forschungsunion. Kaur, N., & Singh, G. (2016). Critical Success Factors in Agile Software Development Projects: A Review. International Journal on Emerging Technologies, 7(1), 1. Khalid, A. (2010, February). Cloud computing: Applying issues in small business. In International Conference on Signal Acquisition and Processing ICSAP’10 (pp. 278-281). IEEE. Larson, D., & Chang, V. (2016). A review and future direction of agile, business intelligence, analytics and data science. International Journal of Information Management, 36(5), 700–710. doi:10.1016/j. ijinfomgt.2016.04.013 MBIS. (2018). Products. Retrieved from www.mbis.com.tr/en/products/mbis-jimarin-en McKinsey. (2017). China develops from ‘sponge’ into innovation leader. Retrieved from https://www. your-bizbook.com/en/Club-China-News/mckinsey-china-develops-from-sponge-into-innovation-leader

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Moghaddam, M., & Nof, S. Y. (2017). The collaborative factory of the future. International Journal of Computer Integrated Manufacturing, 30(1), 23–43. Oztemel, E., & Gursev, S. (2018). Literature review of Industry 4.0 and related technologies. Journal of Intelligent Manufacturing, 1–56. Preuveneers, D., & Ilie-Zudor, E. (2017). The intelligent industry of the future: A survey on emerging trends, research challenges and opportunities in Industry 4.0. Journal of Ambient Intelligence and Smart Environments, 9(3), 287–298. doi:10.3233/AIS-170432 S.A.P. (2017). Leonardo Live, 2017 Frankfurt. Retrieved from https://blogs.sap.com/2017/07/14/sapleonardo-live-2017-frankfurt/ Schumacher, A., Erol, S., & Sihn, W. (2016). A maturity model for assessing industry 4.0 readiness and maturity of manufacturing enterprises. Procedia CIRP, 52, 161–166. doi:10.1016/j.procir.2016.07.040 Statista. (n.d.). Bitkom, “Investition in Industrie 4.0 in Deutschland bis 2020 I Prognose.” Retrieved from http://de.statista.com/statistik/daten/studie/372846/umfrage/investition-in-industrie-40-indeutschland/ Whitney, K. M., & Daniels, C. B. (2013). The root cause of failure in complex IT projects: Complexity itself. Procedia Computer Science, 20, 325–330. doi:10.1016/j.procs.2013.09.280 Wollschlaeger, M., Sauter, T., & Jasperneite, J. (2017). The future of industrial communication: Automation networks in the era of the internet of things and industry 4.0. IEEE Industrial Electronics Magazine, 11(1), 17–27. doi:10.1109/MIE.2017.2649104

ADDITIONAL READING Calisir, F., & Akdag, H. C. (Eds.). (2017). Industrial Engineering in the Industry 4.0 Era. In Global Joint Conference on Industrial Engineering and Its Application Areas, GJCIE 2017, Vienna, Austria, July 20-21. Springer. Ustundag, A., & Cevikcan, E. (2017). Industry 4.0: Managing The Digital Transformation. Springer.

KEY TERMS AND DEFINITIONS Strategy: One of the ways to achieve to the vision of a company. Some examples are innovation focused growth, merging, industry 4.0, productivity by using lean manufacturing etc. Vision: A vision is a picture of the future of an organization. It should be both hard to achieve and realistically accomplishable.

This research was previously published in Agile Approaches for Successfully Managing and Executing Projects in the Fourth Industrial Revolution; pages 228-241, copyright year 2019 by Business Science Reference (an imprint of IGI Global).

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The Development of Servitization Concept in the Era of Industry 4.0 Through SCM Perspective Tunca Tabaklar Izmir University of Economics, Turkey Cansu Yildirim Dokuz Eylul University, Turkey

ABSTRACT The transition from goods-dominant logic to service-dominant logic has captured the attention of industries for decades now. Servitization is one of the concepts that enable organizations to make this transition by providing services along with their products and has become an important competitive strategy for organizations to survive in their ecosystems. Thus, in this chapter, the objective is to increase the understanding of servitization concept in the era of Industry 4.0 from supply chain management perspective. The content analysis methodology is used to examine articles that bring together servitization and supply chain management and to find out where servitization stands with regards to Industry 4.0 applications. The findings show Industry 4.0 applications during servitization operations are yet to develop, and accordingly, the chapter concludes with further research directions in relations to servitization and Industry 4.0 applications in the frame of supply chain management.

INTRODUCTION Traditionally, it was likely to increase market share through maximization of the tangible quality of a product or through productivity improvements via standardization techniques, nevertheless; globalisation and advances in technology in recent years have altered the business landscape, and this has changed value perception of consumers (Vandermerwe, 1990). As manufacturers have acknowledged that global DOI: 10.4018/978-1-7998-8548-1.ch018

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 The Development of Servitization Concept in the Era of Industry 4.0 Through SCM Perspective

competition is not just based on price, they tried to create value through altering their orientation towards service (Gremyr et al., 2010), and start adding services to their product offerings (Vandermerwe, 1990). In order to understand the concepts better, it is essential to start with definitions. A product is defined as anything offered to a market for consumers’ attention, acquisition, use or consumption with an aim to satisfy their wants and needs (Kotler & Armstrong, 2006). Taken its roots from Industrial Revolution, the traditional language of exchange was dependent on products in which value was added during manufacturing process, and so on operand resources (tangible, static ones which need changes to make them valuable) (Lusch et al., 2008; Vargo & Lusch, 2008). This traditional paradigm is called goodsdominant logic (GDL), and it idealizes manufacturing and distribution of tangible goods as they can be standardized and inventoried until they are purchased by consumers (Vargo et al., 2010). A service is, on the other hand, generally defined by not being a product (Baines et al., 2009a). The previous literature gathered the characteristics associated with services under IHIP characteristics which includes Intangibility, Heterogeneity, Inseparability (e.g. simultaneous production and consumption), and Perishability (e.g. non-storable or non-transportable) (Lovelock & Gummesson, 2004). However, the IHIP characteristics harden to study the services within the lenses of GDL (Vargo et al., 2010), thus a need occurs for a new paradigm: service-dominant logic (SDL). SDL emphasizes intangibles, knowhow, and skills as the fundamental factor in exchange (Vargo & Lusch, 2004; 2008), hence it highlights operant resources (intangible and dynamic ones that can create value) (Vargo & Lusch, 2008) and a relationship based approach as it is a significant ingredient to form a set of loyal and profitable customers (Miller, et al., 2000). Nevertheless, after some time, especially with the developments in technology, several scholars (e.g. Araujo & Spring 2006; Goldhar & Berg 2010; Pawar et al. 2009) stated that the IHIP characteristics are myths (Vargo & Lusch, 2004a), and there is no crystal-clear distinction between goods and services. Slack et al (2009) even rejected this distinction and supported Levitt (1972) by stating that if the objective of all business is to serve customers, then everybody is in service business. Therefore, it can be stated that every business, whether it is in manufacturing or service business, is providing service to some extent (Yildirim, 2010). By keeping this argument in mind, as mentioned above, in order to survive in severe global competitive environment, organisations start to place service orientation at the center of their operations (Gremyr et al., 2010). Accordingly, they started to add services to their product offerings to create value (Vandermerwe, 1990), and this strategy is called as servitization (Vandermerwe & Rada, 1988). The aim of this research is to deepen the understanding of the servitization concept by identifying, interpreting, and summarizing the current knowledge in the era of Industry 4.0 through a supply chain management (SCM) perspective, and the subject will be detailed in the next sections. The chapter is organized as follows: The next section investigates servitization and supply chain management and explains where these two meets. The two subsequent sections explain our research methodology and the findings from the content analysis, respectively. The last two sections present future research directions and the conclusion regarding what we have studied in the chapter.

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BACKGROUND Servitization and Supply Chain Management Through years, developing service provision for strategic competition has become a differential feature of innovative manufacturing firms (Spring & Araujo, 2009). Bundling products with services could be a way of differentiation and could provide a defence for severe market competition especially for manufacturing firms which have a high installed product range (Wise & Baumgartner, 1999). Accordingly, since the term “servitization” first coined by Vandermerwe and Rada (1988) and supported and enhanced the idea that services are a means of maintaining competitiveness, research on the role of services and on servitization has been increasing (Vandermerwe & Rada, 1988). The first definition of servitization of manufacturing stated that it is a process of adding services to product offerings with an aim to develop additional value (Vandermerwe & Rada, 1988). After the term appeared, other researchers offered diverse definitions. For instance, while Desmet et al. (2003, p.46) explained the term as “paying more attention to the services component of the product offering”, Lewis and Howard (2009) described it as the extension of customer offering with service elements for increasing profitability. Weeks and Benade (2009) provided a similar definition to the first one and they stated that servitization is a transition of manufacturing organisations into ones that provide an integrated bundle of products and services. Further definitions, on the other hand, highlighted innovative aspect of servitization process. For example, several researchers (e.g. Baines et al., 2009b; Neely, 2008; Visnjic, 2010) indicated that servitization creates innovations in organizations’ capabilities and processes. Kastalli and von Looy (2013) later created an integrated definition indicating both the process/transition and innovative aspects. According to the researchers, durable goods manufacturers prefer to innovate their offering through adding services to their existing offerings throughout the life cycle. Similarly, recent definitions also highlighted the transition towards service-centric approaches and innovations in business models. For instance, Raddats et al. (2019, p.) stated that servitization “…is the the transformation of a firm from taking a product- to taking a service-centric approach. It represents a significant change in the business model and mission of the firm, whereby the service business serves as a growth engine of the firm.” While coining the term, Vandermerwe and Rada (1988) also explained the evolution of servitization. According to the researchers, organizations go through the following three steps: (1) Providing goods or services, (2) providing goods and services, (3) providing goods, services, support, knowledge and selfservice which are called as “bundles”, since these elements are interconnected, and therefore they are sources of entry barriers. Due to this evolution, concepts such as “transition from products to services” (e.g. Oliva and Kallenberg, 2003), “service transition” (e.g. Fang et al, 2008), “service innovation” (e.g. Evanschitzky et al.,2011), “manufacturing service integration” (e.g. Schmenner, 2009) are used to describe the transition of manufacturing companies. Moreover, the topic of innovative combination of goods and services (e.g. the second step described above) has been discussed within other domains such as “product-service systems” (e.g. Johnstone et al.,2009), “complex services” (e.g. Neely et al., 2011), “hybrid offerings” (e.g.Ulaga & Reinartz, 2011), “integrated solutions” (e.g. Brady et al.,2005; Davies, 2004). Thus, nowadays these terms are used interchangeably with servitization (Kowalkowski et al., 2017a;2017b).

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Although servitization is a complex field, the number of organizations engaged in servitization is increasing. Recent research have demonstrated that more than a third of large manufacturing firms propose services (Neely, 2008). Caterpillar, GE, IBM, Xerox and Rolls-Royce are common examples of servitized companies (Cohen et al., 2006; Kastalli and von Looy, 2013). For instance, Rolls-Royce now offers a total care package called as “power by the hour” which includes an engine and accessory replacement service offered with a fixed-cost-per-flying-hour basis (Rolls-Royce, 2012). This means Rolls-Royce is responsible for risk and maintenance, and the company generates revenues through providing engine for customers (Neely, 2008). The reason of the increase in the number of servitized companies is the demand of consumers. Consumers began to demand solutions and/or customized offerings rather than pure products or services, information and knowledge from suppliers with customized relationships (Vandermerwe, 1994). This demand can be met by servitization as it brings service culture and customer orientation to organizations (Neu & Brown, 2008). This culture creates a new capability which is difficult to imitate and brings new customers while maintaining existing ones (Schmenner, 2009). As customers’ demands are met through more reliable and durable bundles of products and services, they are released from the problems and some costs that are related with use and maintenance (Manzini & Vezzoli, 2003). This means servitization create a “peace of mind” for consumers (Weeks & Benade 2009, p. 392) because, in case of a problem, it will be handled by the company/the provider of the offering. With servitization, organisations experience a transition from transactions towards relationships (Neely et al., 2011). As the organizations engage in servitization, they develop closer and long-lasting relationships with customers which lead to a loyal customer base. This in turn increases market entry barriers, sales revenue, profits, and market size (Goldhar & Berg 2010; Schmenner 2009). Another reason for increased revenues through servitization is that services are more stable during fluctuations in economies, and thus they may be able to create high profits margins (Oliva & Kallenberg, 2003; Salonen, 2011). Therefore, organisations engaged in servitization are more likely to remain competitive (Martinez et al. 2010; Pawar et al. 2009). Although servitization seems beneficial, it is not free from obstacles. For instance, due to the requirement for diverse organisational principles, structures, processes, and capabilities, organisations experience managerial challenges (Oliva & Kallenberg, 2003; Martinez et al., 2010). These managerial challenges may also be the reason for negative service quality (Oliva, 2001) which leads to reduced value for consumers (Gebauer & Friedli, 2005). This may also decrease the chance to create a brand reputation as a reliable provider for companies (Oliva & Kallenberg, 2003) which may bring competitive disadvantage. According to previous literature, communication is another problem. This challenge is two-sided. First, the communication between managers and employees may create an obstacle. Managers may have a fear of losing power and authority (Ashforth & Lee, 1990), whereas employees may fear losing their jobs, as addition of services might require new capabilities or skills. Therefore, if management does not communicate well with employees regarding customer orientation and new service culture, the transition towards servitization will face challenges (Gebauer et al., 2010). Second, the transition towards relationship-based transactions with servitization increases the number of customer touch-points (Martinez et al., 2010) which requires better communication between, especially front-line employees and customers. Another obstacle is about the product itself (Baines et al., 2009a, b; Brax, 2005). Services’ unique characteristics produce challenges during production, delivery and design of product service bundles (Brax, 2005). This means that adding services to product offerings may bring extra challenges such 339

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as creating a fit between technical competences and capabilities (Windahl et al.,2004) and improving internal efficiencies of operations (Salonen, 2011). Due to these challenges, companies may experience a loss in their revenues. For instance, Rolls-Royce is a common example while describing servitization. However, Rolls-Royce’s proportion of revenues coming from services declined from 53% to 49% between 2006 and 2009 (Neely et al., 2011). Throughout the last decade, industries have been facing many transformations which dramatically change production, operations and delivery of products as well as the companies’ relationship with customers (Gersch & Goeke, 2007). While servitization can be considered as one of these transformations, the other one is technological one which creates a digital transformation for the industries and global economy (Kowalkowski et al., 2017a). A recent phenomenon is Industry 4.0 which uses different emerging technologies such as Internet of Things (IoT) and creates a cyber-physical and intelligent systems for developing value (Frank et al., 2019; Müller et al., 2018). This means that with Industry 4.0, industrial products and systems integrate with more software and embedded intelligence (Lee et al., 2014). Therefore, this new phenomenon, despite being born from a different field of research (e.g. engineering and computer science), (Liao et al., 2017) brings new challenges for servitization. For instance, IBM has recently acknowledged that: • • •

Customers demand access to offerings from the cloud and require cognitive capabilities in the cloud as well as in their mobile phones, Industrial customers are more concerned about data security as any attack may damage trust, or decrease revenue and market value, IoT and cloud are ways of creating innovation in service journey required by customers, and ways to reach competitive advantage, as well,

Therefore, IBM has altered its business model and become a cognitive solution and cloud platform company (Spohrer, 2017). As IBM example demonstrates, Industry 4.0 changes not only the behavior of the customers but also the development of response to the needs of them by organizations. However, although both servitization and Industry 4.0 have implications for creating a better competitive position in the market, previous literature (e.g. Ardolino et al., 2017; Belvedere et al., 2013; Coreynen et al., 2017; Kamp et al., 2017; Vendrell-Herrero et al., 2017) has devoted little attention to connect these two fields of research. As mentioned before, one of the challenges of servitization is related with managerial issues. There needs to be a shift towards a service culture, and this may create some problems within the organization. However, as Christopher and Peck (2004) stated “no organization is an island”. To survive in a competitive market, companies need to design and coordinate their supply and distribution networks for effective delivery of their products and services, which is known as supply chain management. However, the studies on supply chain management concentrate on manufacturing, and there is a debate on the transferability of theories to services contexts (Baltacioglu et al.,2007; Ellram et al.,2006). Although servitization and supply chain management were born from the same field of research, few studies in previous literature (e.g. Bustinza et al.,2013; Finne & Holmström, 2013) focused on developing value through service additions to product offerings. Therefore, although servitization has been at the center of research fields such as operations management and marketing, the concept needs to be further discussed within the lenses of supply chain management and Industry 4.0. Accordingly, the aim of this chapter is to deepen the understanding of 340

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the servitization concept by identifying, interpreting, and summarizing current knowledge through the lenses of supply chain management and Industry 4.0, and thus we have used supply chain management in order to understand the servitization examples in the literature with regards to industry 4.0 applications. Supply chain management has been studied for decades in the literature since its introduction in the beginning of 1980s (Oliver & Webber, 1982). Throughout the 1980s and 1990s, the term gained a lot of attention from the researchers (Huan et al., 2004). However, its implications can be traced back in 1960s in the literature of business studies (Forrester, 1958). Like servitization, supply chain management also tries to address customer requirements with proper inventory control and increase customer service level by enhancing the performance of all members of the chain (Ellram & Cooper, 1993). The key components of supply chain management were identified to be business process, management components and network structures (Lambert et al., 1998) and integration of business processes in the chain has been the core of supply chain management (Cooper et al., 1997). All in all, as we discussed before, starting with definitions in order to understand a concept is essential, therefore we adopt the definition of Mentzer et al. (2001) as it is one of the most cited definitions of all. The authors define supply chain management as “the systemic, strategic coordination of the traditional business functions and the tactics across these business functions within a particular company and across businesses within the supply chain, for the purposes of improving the long-term performance of the individual companies and the supply chain as a whole” (Mentzer et al., 2001, p. 18). As the aim is the maximization of the entire chain performance, by taking a system approach (meaning the whole may be greater than the sum of its parts) (Christopher, 2010), supply chain should be managed as a single entity (Lummus et al., 2001). However, to become a single entity, trust and collaboration between the diverse partners in the chain should be present (Burgess et al., 2006). While trust is a key requirement to manage and monitor various partners in the chain to obtain long-term and mutually favourable relationships (Lee & Fernando, 2015), collaboration aids partners to share real-time and open information which may reduce alignment problems (Croom et al., 2000), improve customer satisfaction and value, and consequently increase profitability and create a competitive advantage (Baltacıoğlu et al., 2007; Mentzer et al., 2001). Since the development of supply chain management is continuous, as there are many diverse fields of it emerged such as sustainable supply chain management and humanitarian supply chain management, advancement of technology has also altered the understanding of the management of the chain. With the digitalization, digital supply chain management, and components of management like supply chain management, digitalization and technology implementation have recently been of interest (Büyüközkan & Göçer, 2018).

METHODOLOGY The chapter exploits content analysis methodology in order to deepen the understanding of development of servitization during the era of industry 4.0. Traditionally, content analysis methodology consists of four methods; material collection, descriptive statistics, category selection and material evaluation (Seuring and Gold, 2012) and is “a class of methods within empirical social science that can be applied both in a quantitative and qualitative way” (Seuring & Gold, 2012, p. 546). Therefore, the methodology is suitable for studies that are focusing on literature like our chapter which includes articles from 1988 to 2019 that examine servitiziation in the frame of supply chain management with regards to industry 4.0. Moreover, 341

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content analysis methodology has been used widely in supply chain management research (Cullinane & Toy, 2000; Fahimnia et al., 2015; Haavisto & Kovács, 2014; Kovács et al., 2012; Kunz & Reiner, 2012; Seuring and Gold, 2012; Tabaklar et al., 2015; Seuring and Müller, 2008; Srivastava, 2007;; Tate et al., 2010;) and servitization research (Brax & Visintin, 2017; Ferreira et al., 2013; Rabetino et al., 2018). The rest of the methodology section explains the processes of material collection, descriptive statistics, category selection and material evaluation along with reliability and validity of the research.

Material Collection Our material collection in the literature of servitization has started with the keyword search. The term “servitization”, with the combinations of supply chain management such as supply chain, channels, value chain, partnership, ecosystem in, has been used in databases, namely, in Emerald and ScienceDirect. The first reason behind the selection of the two databases is that these two databases are the most popular ones in marketing and supply chain management field. Secondly, these two databases cover most of the top journals in the field of supply chain management such as Supply Chain Management: An International Journal, International Journal of Physical Distribution & Logistics Management, International Journal of Operations and Production Management and International Journal of Logistics Management as well as top marketing journals where servitization articles published frequently such as Industrial Marketing Management. In our keyword search, since our focus is on industry 4.0 implementations, we have also used “servitization” with different industry 4.0 enablers (Rüßmann et al., 2015) such as digitalization, Internet of Things, big data, and cloud computing.

Descriptive Statistics The concept of servitization was first introduced by Vandermerwe and Rada (1988) in 1988. Therefore, we have also limited our keyword research from the year of 1988 to 2019. After filtering the databases with keyword combinations and reading through all articles that came up, we have found that there are 51 articles (21 from ScienceDirect, and 30 from Emerald) relevant to our research topic (See Table 1). We have excluded conference papers and other working papers that came up from our keyword research. Table 1. Material collection process Databases Keywords

ScienceDirect

Emerald

Number of articles

Relevant number of articles

Number of articles

Relevant number of articles

Servitization

70

19

72

30

Servitization + Big Data

5

0

0

0

Servitization + Cloud computing

0

0

0

0

Servitization + IoT

8

1 (repeated one) 0

1(repeated)0

1 (repeated) 0

Servitization + Digitalization

13

3 (one is repeated) 2

2(repeated)0

1(repeated)0

Total

96

21

72

30

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Category Selection and Material Evaluation After collecting articles out of keyword search, we have come up with categories and developed a template on Excel in order to evaluate the articles. Our categories involve aim of the articles, methods and theories used along with practical implications and empirical context. For validity and reliability, we coded articles separately based on identified categories. In order to solve disagreements and increase validity and reliability of our analysis, we have exploited “discursive alignment of interpretation” (Seuring and Gold, 2012) which gives authors freedom to discuss on issues related to coding and evaluation.

FINDINGS The fundamental aim of the study is to deepen the understanding of the servitization concept by identifying, interpreting, and summarizing current knowledge within the lenses of supply chain management and Industry 4.0. The analysis showed that most of the articles are published in journals such as Industrial Marketing Management (n=12), International Journal of Production Economics (n=4) from ScienceDirect, International Journal of Operations & Production Management (n=10) and Journal of Manufacturing Technology Management (n=5) from Emerald. For instance, Industrial Marketing Management journal published more articles related with servitization because the journal published two special issues on “Servitization” and “Service and Solution Innovation” in 2017, and in 2011; respectively. Table 2. Journal list Journals from ScienceDirect

Frequency

%

Industrial marketing management

12

23.5

International Journal of Production Economics

4

7.8

Journal of Business Research

3

5.9

Energy Policy

1

2.0

Journal of Cleaner Production

1

2.0

Table 3. Research methodology distribution Research Methodology

# of articles

%

Case study

27

52.9

Systematic literature review

9

17.6

Survey

8

15.7

Interview

2

3.9

Analytical model

3

5.9

Viewpoint

2

3.9

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The articles are also analyzed in terms of their research methodologies. Most of the articles use qualitative methods for data collection. Moreover, the researchers mostly use multiple case studies. For the sources of evidence, researchers use semi-structured interviews, secondary data from the sample companies (e.g. annual reports), and from online sources, and observation. Most of these case studies focused on theory building. Furthermore, relative numbers of studies are conducted as literature reviews and viewpoints (see Table 3 and Figure 1). Figure 1. Research methods distribution over the years

Although, servitization first coined in 1988, a theory of servitization is still in a developing era, thus majority of the studies focuses on theory building by providing conceptual frameworks as findings. Despite not having its own theoretical bases, servitization concept has been studied under diverse theories coming from different fields such as management or marketing. For instance, since servitization is considered as a chance to survive through competition, Resource-Based View (RBV) is used by researchers (e.g. Lütjen et al., 2017) as a theoretical foundation. Furthermore, RBV’s extensions such as Dynamic Capabilities View (e.g. Raddats et al., 2017), and Relational View (e.g. Weigel & Hadwich, 2018) are also common theoretical lenses. Another finding of the study reveals the frequency of keywords. Obviously, “servitization” has mentioned most. However, along with servitization, related concepts such as Product-Service Systems (PSS), and service infusion are also included in keywords. Apart from servitization and consistent with the aim of the study, there are keywords related with SCM such as channel competition, marketing channels, distribution channels, ecosystem and integration. Furthermore, although Industry 4.0 has not mentioned much within keywords, IoT and digitalization were used. Besides, as more articles have been published in Industrial Marketing Management journal, there are some keywords related with marketing such as customer orientation, value creation, and customer involvement. As a part of these articles, majority of them mentioned industry related terminologies. For instance, while some articles (e.g. Xing et al., 2017) used country names (e.g. Germany, China) as keywords for the data gathered in there, some others use “manufacturing” (e.g. Ambroise et al., 2018) or the name of the industry such as publishing industry (e.g. Vendrell-Herrero et al., 2017).

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Table 4. Keyword distribution Keywords

Frequencies

%

Servitization (e.g. PSS, service infusion, service transition, service innovation, etc.)

92

35.1

Industry related (e.g.manufacturing, publishing industry, Germany,etc.)

28

10.7

Marketing related (e.g. Customer orientation, value co-creation, etc.)

30

11.5

Industry 4.0 (e.g. IoT, Digitalization)

8

3.1

SCM related (e.g. channel competition, integration, distribution channels, ecocsystem etc.)

40

15.3

Methodology (e.g. literature review, quantitative theory, case study, etc.)

18

6.9

Strategy (e.g. capabilities, competitive strategy, competence, etc.)

19

7.3

Others (e.g. sustainability, financial performance, centralization, energy efficiency, etc.)

27

10.3

As most of the studies classify servitization as a competitive strategy that creates advantage, some of the articles have also come up with used strategy related keywords. Mostly, capabilities (related to resource-based view, dynamic, relational and operational), competence and competitive strategy are summed up under the category “strategy”. Since every article has at least three keywords, there are some only mentioned once, and these keywords are gathered under the title of “Others” which includes but not limited to energy utilities, lighting, practice, success factors, barriers, and organizational design (See Table 4).

Contribution to Servitization Literature in the Era of Industry 4.0 and Supply Chain Management The analysis demonstrated that there are really a limited number of studies (e.g. Reim et al., 2019; Sklyar et al., 2019) focusing on servitization in the era of Industry 4.0. These studies mostly published in Industrial Marketing Management and International Journal of Operations & Production Management. Since Industry 4.0 is a relatively new concept, it is expected that the number of studies is limited and most of them are published in 2019. These studies focus on the advantages created by Industry 4.0 about related concepts such as IoT, or digitalization. For instance, while Rymaszewska et al. (2017, p.92) addressed “how organisations offering product-service systems can reap the benefits that the IoT”, Sklyar et al. (2019) concentrated on digital servitization, which is the utilization of digital tools for transforming processes when a company experiences a shift from a product-centric to a service-centric business model as well as logic (Kowalkowski et al., 2017a). Accordingly, the results showed that within the selected keywords related with Industry 4.0, the most popular one is digitalization. As digitalization separates information from devices and technologies, it may help to reshape the nature of services as well as service operations (Lusch and Nambisan, 2015). Therefore, it is seen as an aid to adapt advanced service offerings (Cenamor et al., 2017) and as a part of servitization strategy (Reim et al., 2019). Furthermore, this separation creates knowledge dispersal and a requirement to collaborate with internal actors as well as actors that are from outside of firm’s boundaries (Sklyar et al., 2019), meaning supply chain partners. Prior studies assumed that the strategies of supply chain partners and manufacturers are aligned (Oliva and Kallenberg, 2003), however; as the global markets present diverse conditions for every partner, they

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are making different strategic choices (Reim et al., 2019), which creates further challenges for organizations shifting towards servitization. Thus, we need a deeper understanding of the supply chain partners’ role during servitization. As mentioned above, the number of studies (e.g. Bustinza et al.,2013; Finne&Holmström, 2013) focusing on servitization and SCM are already limited, and studies working on servitization and supply chain partners in the era of Industry 4.0 are scarcer with a handful of studies. The limited number of previous works integrating servitization, SCM, and Industry 4.0 have concentrated on issues such as network partners/actors (e.g. Reim et al., 2019), buyer-supplier relationships in supply chain (e.g. Saccani et al., 2014), intrafirm and interfirm change processes (e.g. Sklyar et al., 2019). For instance, Reim et al. (2019) addressed the challenges service network partners face when trying to provide advanced services. According to the researchers, these challenges are capability related challenges (e.g. a lack of service provision vision), and market-related challenges (e.g. unfavourable local conditions), and accordingly some servitization strategies need to be provided to match with these specific challenges. Rymaszewska et al. (2017) further proposed that IoT technology requires a redesign of value chains, and as it aids improvement in offerings, it will enrich value chain with more innovative value-adding activities that gather and analyze data coming from machines and devices. In that way, value is created through quicker product introductions, new business models, supporting customer success, developing a product as a part of broader system, and data analytics (Rymaszewska et al., 2017). Similarly, in a fashion setting, Pal (2016) developed six dominant ways to create value by driving responsibility, and these are value-adding services, product leverage, collaborative partnership, information transparency, awareness and platform-enabled networking. Some studies have been taken a dyadic view by focusing on buyer-supplier relationships. Saccani et al. (2014) contributed to the literature by indicating that there is no best way to form a buyer-supplier relationship during servitization. However, the researchers also found out that the type of service is one of the fundamental factors affecting the formation of upstream relationships. With a similar focus, Vendrell-Herrero et al. (2017, p.78) worked on digital transition of publishing industry with a bidirectional perspective and concluded that digital servitization produces asymmetric power dynamics of the interdependencies between upstream and downstream parties, which empowers downstream companies (e.g. retailers) when resources are not inimitable. In another study, Sklyar et al. (2019) focused on intrafirm and interfirm change processes, and indicated that increased demand for market efficiency and closer collaboration between ecosystem actors are the reason for enhanced relational and structural embeddedness, which further highlights the requirement for future studies focusing on supply chain/value chain/network/ecosystem partners.

FUTURE RESEARCH DIRECTIONS As every research, the research of this chapter has limitations. First of all, there are limited keyword combinations in our material collection which resulted in rather smaller number of articles. Therefore, for further studies, we are considering expanding our keywords beyond servitization in order to picture the state-of-art of the relationship between servitization and supply chain management thoroughly. Findings regarding keywords out of the content analysis show that integrated solutions, product-service systems as well as service dominant logic can be used to expand the sample for further studies. Another way to expand the sample could be looking into the special issues dedicated to servitization, integrated 346

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solutions and product-service systems in diverse journals. Moreover, in our study, we only include two popular research databases, thus, including more databases enables us to reach more articles regarding the concepts under investigation. Content analysis also gave interesting results on research methods exploited in sample articles. Over the years, using empirical research, specifically, case study methodology has been increased for several reasons. First, organizations have become more willing to share information with researchers in this topic as they believe the cooperation between the industry and the academia. As a result, most articles in our sample have great space and great number of practical implications for the industry. Second, since most of the articles using case study methodology have a theory building approach apart from some exceptions (see Raja et al., 2017) with theory elaboration approach, this could be a sign showing that servitization from supply chain management perspective is still developing. In terms of theory building, researchers are encouraged to conduct longitidunal studies (Polzin et al., 2016; Sklyar et al., 2019). For instance, Reim et al. (2015) encouraged future researchers to conduct longitudinal case studies focusing on better understanding of the complex relationships between business models, as well as more quantitative studies to empirically test the effect of different business models on company performance and growth. Another suggestion for future research direction for researchers in this field could be theory elaboration and theory testing using case study methodology. For instance, some studies (e.g. Baines and Lightfoot, 2013; Martinez et al., 2010; Rymaszewska et al., 2017) developed conceptual frameworks as a result of their studies, thus these frameworks can be tested in the future. Similarly, other studies also suggested to test the effect of servitization on financial performance (e.g. Ambroise et al., 2018), profits (e.g. Kohtamaki et al., 2013), price and service quality (e.g. Lee et al., 2016). Hence, another aspect of methodological directions is regarding the variables that are tested. Some researchers (e.g. Ayala et al.,2019; Reim et al., 2015; Weigel and Hadwich, 2018) advised looking for interaction effects between variables. For example, Weigel and Hadwich (2018) offered to search for further variables and look for moderating effects of, for instance, satisfaction with service network and commitment of employees. There are studies focusing on servitization as a competitive strategy. Previous work acknowledges that combining products and services is a way to gain competitive advantage (Davies, 2004) as the final offering is more durable and reliable for consumers (Morey, 2003), and as it requires a closer and long-lasting relationship with consumers (Mont, 2002a). Therefore, these offerings could be imperfectly imitable, non-substitutable, or immobile, which are the characteristics described in Resource-Based View/Approach (RBV). Due to this relationship, previous research (e.g. Lütjen et al., 2017)) used RBV as a theoretical foundation. Furthermore, related with the criticism of RBV, due to changes in environmental or market related conditions, organizations need to develop diverse capabilities (e.g. routines and processes) to stay competitive (Zollo & Winter, 2002), thus Dynamic Capabilities View may be another theoretical basis, for instance, to study buyer-supplier relationships and networking capability (Mitrega et al., 2012). Moreover, supply chain management has become what it is today by borrowing theories from other fields (Halldorsson et al.,2007; Halldorsson et al.2015.). This gives a research field to see other perspectives and to be able to advance more. Thus, future research can borrow theories from other fields in order to advance servitization and form a supply chain point of view which also gives researchers a chance to understand how industry 4.0 enables can be implemented in supply chain in order to implement servitization better. For example, Ambroise et al. (2018) suggested the use of organizational and contingency theories. Moreover, network perspective and ecosystem view can be adopted in order to transfer knowledge and practices beyond organizational level (Raddats et al., 2017).Similarly, in order to test models presented in our sample, some authors suggested to use multi-actor analysis (Ruiz-Alba 347

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et al. 2018; Story et al; 2017) and expand their empirical studies to multiple industries (Hakanen et al., 2017; Ruiz-Alba et al., 2018). As the Rolls-Royce example demonstrates, some companies struggle to obtain the mentioned benefits of servitization, and this may lead to servitization failures (Benedettini et al., 2015; Brax, 2005, Gebauer et al., 2005). Due to the challenges and sometimes failures, some firms also choose deservitization which is a process of reducing or withdrawing service elements in the offering (Gebauer & Kowalkowski, 2012; Kowalkowski et al., 2017a). Despite being cited as a successful example, Xerox deservitized its offerings and set up an independent firm called Conduent for their service-centric business process outsourcing offerings (Kowalkowski et al., 2017a). Therefore, future studies may not only focus on successful cases, because focusing on failures or deservitized may help companies understand capabilities in creating competitive strategies better (Raddats et al., 2017).

CONCLUSION Servitization has been a competitive advantage for organization in competing within the global market and a good strategy to get the attention of customers. However, it is important to see how servitization will evolve in the era of industry 4.0. In this chapter, we specifically have an interest of servitization as a strategy in supply chain management and seeing the understanding of servitization in the frame of supply chain management. Looking at the enablers of industry 4.0 (Rüssman et al., 2015), there is little evidence in the literature that servitization meets industry 4.0 in supply chain management. Servitization has been developing and there are quite many literature review articles and case studies published in top journals in the field. Our sample came up with limited number of articles where servitization meets supply chain management; however, creating ecosystem might need collaboration and integration of process among the members in a supply chain. Therefore, we believe that servitization can be studied by adopting different theoretical approaches (e.g. Ambroise et al., 2018) from other fields as this has become one of the ways of conceptualizing supply chain management. Furthermore, as the world is becoming more digitalized, digital tools (e.g. big data, cloud computing, etc.) are utilized during the transformation process of companies from a product-centric to a servicecentric logic (Kowalkowski et al., 2017a) which is called digital servitization. However, apart from some exceptions (See Sklyar et al., 2019), and as the results of this study demonstrates, there are still limited number of studies looking for the process of servitization in the era of Industry 4.0, or addresses the question of how digital tools could be utilized during servitization for creating sustainable competitive advantage. Moreover, with digital transformation, everything becomes interlinked, which requires further need to collaborate with other actors in supply chains/value chains/ ecosystems/networks. Therefore, by focusing on diverse methodologies, and industries, future research is still needed on the process of servitization with a multi-actor focus in the era of Industry 4.0.

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Vargo, S. L., Lusch, R. F., & Akaka, M. A. (2010). Advancing service science with service-dominant logic. In Handbook of service science (pp. 133–156). Boston, MA: Springer. doi:10.1007/978-1-44191628-0_8 Vendrell-Herrero, F., Bustinza, O. F., Parry, G., & Georgantzis, N. (2017). Servitization, digitization and supply chain interdependency. Industrial Marketing Management, 60, 69–81. doi:10.1016/j.indmarman.2016.06.013 Visnjic, I. (2010). Servitization: When is service oriented business model innovation effective. Service Science Management and Engineering, 6, 30–32. Weeks, R. V., & Benade, S. (2009). Servitization: A South African Perspective. Journal of Contemporary Management, 6, 390–408. Weigel, S., & Hadwich, K. (2018). Success factors of service networks in the context of servitization– Development and verification of an impact model. Industrial Marketing Management, 74, 254–275. doi:10.1016/j.indmarman.2018.06.002 Windahl, C., Andersson, P., Berggren, C., & Nehler, C. (2004). Manufacturing firms and integrated solutions: Characteristics and implications. European Journal of Innovation Management, 7(3), 218–228. doi:10.1108/14601060410549900 Wise, R., & Baumgartner, P. (1999, September). Go Downstream the New Profit Imperative in Manufacturing. Harvard Business Review, 133–141. Xing, Y., Liu, Y., Tarba, S., & Cooper, C. L. (2017). Servitization in mergers and acquisitions: Manufacturing firms venturing from emerging markets into advanced economies. International Journal of Production Economics, 192, 9–18. doi:10.1016/j.ijpe.2016.12.010 Yildirim, C. (2010). Creating Sustainable Competitive Advantage Through Servitization (MSc Dissertation). University of Manchester, Manchester, UK. Zollo, M., & Winter, S. G. (2002). Deliberate learning and the evolution of dynamic capabilities. Organization Science, 13(2), 339–351. doi:10.1287/orsc.13.3.339.2780

ADDITIONAL READING Barney, J. (1991). Firm Resources and Sustained Competitive Advantage. Journal of Management, 17(1), 99–120. doi:10.1177/014920639101700108 Bigdeli, A., Baines, Z., Schroeder, T., Brown, A., Musson, S., Guang, E., ... Calabrese, A. (2018). Measuring servitization progress and outcome: The case of ‘advanced services’. Production Planning and Control, 29(4), 315–332. doi:10.1080/09537287.2018.1429029 Carlborg, P., Kindström, D., & Kowalkowski, C. (2013). The Evolution of Service Innovation Research: A critical review and synthesis. Service Industries Journal, 34(5), 373–398. doi:10.1080/02642069.2 013.780044

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Ceci, F., & Prencipe, A. (2008). Configuring Capabilities for Integrated Solutions: Evidence from the IT Sector. Industry and Innovation, 15(3), 277–296. doi:10.1080/13662710802040879 Ellram, L. M., Tate, W. L., & Billington, C. (2006). Understanding and managing the services supply chain. The Journal of Supply Chain Management, 40(4), 17–32. doi:10.1111/j.1745-493X.2004.tb00176.x Kindström, D., Kowalkowski, C., & Sandberg, E. (2013). Enabling service innovation: A dynamic capabilities approach. Journal of Business Research, 66(8), 1063–1073. doi:10.1016/j.jbusres.2012.03.003 Mont, O. (2004). Product-Service Systems: Panacea or Myth? Ph. D. Sweden: Lund University; Retrieved from https://lup.lub.lu.se/search/publication/467248 Rexfelt, O., & af Ornas, V. H. (2009). Consumer Acceptance of Product-Service Systems: Designing for Relative Advantages and Uncertainty Reductions. Journal of Manufacturing Technology Management, 20(5), 674–699. doi:10.1108/17410380910961055 Rolls-Royce. (2017). Nor Lines and Rolls-Royce sign landmark Power-by-the-hour service agreement. Retrieved from https://www.rolls-royce.com/media/press-releases/2017/24-05-2017-nor-lines-and-rrsign-landmark-power-by-the-hour-service-agreement.aspx Tuli, K. R., Kohli, A. K., & Bharadwaj, S. G. (2007). Rethinking customer solutions: From product bundles to relational processes. Journal of Marketing, 71(3), 1–17. doi:10.1509/jmkg.71.3.1

KEY TERMS AND DEFINITIONS Digital Servitization: A process of shifting from product-centric logic to service-centric logic by providing bundles of products, services, and digital tools (e.g., big data, cloud computing, etc.) to develop value for customers. Industry 4.0: The present trend of high utilization of automation and data exchange during manufacturing products. Integrated Solutions: The bundles of products and services to maintain competitive position in the market. Service: Intangible offerings of companies that hold IHIP characteristics (intangibility, heterogeneity, inseparability, and perishability). Service-Dominant Logic: A logic that highlights intangibles, know-how, and skills as the key factor during transactions. Servitization: A process of shifting from product-centric logic to service-centric logic by providing bundles of products and services in order to create value for customers. Supply Chain Management: Chain of three or more organizations directly included in both upstream and downstream flows of products, services, cash, and information from the point of origin (e.g., supplier) to the ultimate consumer.

This research was previously published in the Handbook of Research on Strategic Fit and Design in Business Ecosystems; pages 593-615, copyright year 2020 by Business Science Reference (an imprint of IGI Global).

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Competency Framework for the Fourth Industrial Revolution Mustafa Kemal Topcu https://orcid.org/0000-0002-3298-1283 ST Strategy and Technology Development LLC, Turkey

ABSTRACT Today’s business environment is described with volatility, uncertainty, complexity, and ambiguity. In order for organizations to survive in the fourth industrial revolution characterized by continuously changing resulted from digital transformation and technological development, it is critical to identify a vision, to attract qualified human resources, to motivate them, to allocate resources to complete the mission, and to speed activities up to achieve the desired end state. It is of great significance to analyze the organization and create a competency framework to harbor all relevant steps to move the organization further. Therefore, this study aims at drawing attention to competency framework for the Industry 4.0 environment. There is no doubt that a standard competency framework for the fourth revolution may not be proposed. However, as a starting point, a generalized competency framework is proposed as a sample for further conceptual and empirical studies.

INTRODUCTION Competency as a concept was first introduced by White (1959) in the United States of America to describe attributes leading to high performance and motivation of employees. According to the researcher, competency is interaction of an individual with the work environment. By supporting the researcher with empirical studies, McClelland (1973, 1998) developed competency measurement model still in use. Inadequacy of psychometric scales to measure individual performance and that of using job descriptions to manage performance result in employing competency models (PAHRODF, 2017). Nonetheless, as Barrett and Depinet (1991) state, there is not enough evidence that McClelland’s model produces useful results. On the other hand, the model became more common by 1990s after the efforts of Boyatzis (Rothwell & Lindholm, 1999; Cardy & Selvarajan, 2006). To this end, competencies are considered untrivial factor for sustainable competitive advantage of the organizations (Campbell & Sommers Luchs, DOI: 10.4018/978-1-7998-8548-1.ch019

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1997; Nadler & Tushman, 1999). Thus, we may describe competence as a concept introducing a holistic approach to use workforce, knowledge, and technology effectively and interactively. Likewise, job-based learning urges organizations to analyse, plan, and conduct human capital of the organizations with a better understanding of competencies. In short, competency may be defined as everything to achieve better results in efficiency, effectiveness, and productivity. So, competency is the total of the factors that distinguish the best from the rest at a given work environment (Monk, 2001, p. 47). However, there are some studies identifying competency as a skill or an ability (for instance; Boyatzis & Sala, 2004). To this end, we may contend that competency comprises behaviours, attitudes, and knowledge providing reliable high performance. And we may widely describe competency as a guiding tool including knowledge, abilities, personal attributes, and behaviours contributing to achieving strategic goals of the company (Gangani et al., 2006; Dessler, 2007; Petersen et al., 2011). Accordingly, competency based human resources management is fed by individual differences, industrial psychology, leadership researches, and job analyses. Besides, contemporary work environment is much more different than the past and is ever changing faster than expected. The fourth industrial revolution is organization of life cycle of a product from customer requirements through recycling following delivery to the end user (Prifti et al., 2017). The aim of the fourth industrial revolution is efficient, flexible, and customized production by means of digitally decentralized controls, self-organized supply chains, and fully automated factories by real-time and sensor technologies (Kagermann et al., 2013; Gebhardt et al., 2015). Therefore, the workforce is anticipated to comprehend the processes, connection along the networks, digitalization, and data collection and utilisation (Ras et al., 2017). By digital transformation and technological development, business environment is continuously changing leading to changes in competencies business world would like to employ. As Longo and his colleagues (2017) state, employees are required to be more flexible and to display adaptive capabilities in this dynamic working environment. The tasks they work on are getting less routine and ask for continuous knowledge and skills development (Ras et al., 2017). Rapid changes in technology, globalization, and environment require business world adapt to changes to sustain in the VUCA environment. Today’s business competitive environment is described with the acronym “VUCA”, stands for volatility, uncertainty, complex, and ambiguity. The changing environment urges companies to use resources efficiently, become decentralized and less bureaucratic, decide and act rapidly, produce more qualified products, speed up innovations, deliver better service, and make use of employees to adapt to the (Brockbank et al., 2003). To this end, efficient and effective use of organizational resources may result in facilitating the adaptation of the firms to VUCA environment. It is renowned that human resources are one of the factors that provide organizations sustainable competitive advantage. Hence, competencies as well as other factors are to be well described to employ and develop qualified workforce. Therefore, this study aims at drawing attention to competency framework for the Industry 4.0 environment. Literature on competency models regarding the fourth industrial revolution is scarce (Prifti et al., 2017). Although the research on the fourth revolution itself recalls the change in the workplace, no models are recommended regarding human resources management. Similarly studies on competency modelling in the literature lack a universal understanding of competency framework and relation with organizational strategies and policies (Vraniak et al., 2017). Thus, the study contributes to the literature by a methodology proposal for competency framework oriented through the future’s workplace. Within this context, the rest of the study is as follows; after defining competency as a concept, types of competencies are given, and competency framework is discussed. Consequently, a methodology is proposed to prepare competency framework for future workplaces in the fourth industrial revolution. 360

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COMPETENCY FRAMEWORK Competency as a Concept Boyatsiz (1982) popularized competency concept developed by McClelland (1973, 1998) in order to measure performance rather than to employ IQ tests in the workplace. Although it has been a long time, literature is not definite on the definition of the competency (Deist, 2005; Heinsman, 2008). Unfortunately, competence and competency, two very similar words, are used to describe two very different constructs and they are used interchangeably (Bartram, 2012). Hartle (1995, p. 107) describes competence as “knowledge, skills, and abilities as well as personal attributes and motivational factors for higher performance workers.” Burgoyne (1988), Woodruffe (1990, 1991), and Dessler (2007) describe competence as characteristics to meet job demands and highlight the role of attributes to get a job sufficiently done. In a similar vein, Cockerill (1989) lists competence as concrete ones like presentation skills and abstract ones like self-esteem. On the other hand, Mansfield (2004) underlies that competency is a product of tasks, outcomes, and personalities. Gangani and his colleagues (2006) focuses organizational success and stress the role of skills, knowledge, behaviours, attributes, and motivation as competencies to contribute to the desired end state. Leung and his colleagues (2016) define competency as a multifaceted characteristic of performance. To this end, making a distinction between the two constructs may be helpful to understand the competency concept. Competence is a job-specific term while competency is generic applying across all jobs (Bartram, 2012). While competence refers to minimum job standards to do it efficiently, competency refers to distinguished personal attributes leading to higher performance. So the two words, competence and competency, are not interchangeable; rather, they are complementary. In short, competence is impossible without competency. However, competency doesn’t indicate real performance but lays out competencies for a higher performance. Towards this end, we may conceptualize competency as a concept that fills the gap between formal learning and job demands (Boon & van der Klink, 2002). It is of great significance that organizational culture is the most dominant factor on competencies. Constructivist theory examines competency as a phenomenon within its context (Sandberg, 2000). Dulewicz (1989) advocates that 70% of competency is generalizable whilst the rest is specific to organization. To summarize, competency conceptually covers attributes providing effective results and higher performance at a given organization (Boyatzis, 1982). As a result, this study refers to competency as a group of observable knowledge, skills, and abilities that ensure higher performance (Gürbüz, 2017). To summarize, some significant features of competency are listed as follows: • • • • • • •

Competency is not a job description. Competency is in compliance with mission, vision, strategies, and values. Competency needs to be easily understood. Competency is observable, measurable, and open to development. Competency is based on behaviours. Competency describes excellent behaviours. Competency may differ by sectors, even sometimes within the sector.

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Types of Competencies Competency came to table after the declaration of the target of EU to make Europe more competitive and knowledge-based public in Lisbon in 2005. Multi-factorial structure is accepted in the literature; but, there is no clear-cut definition for the factors (Le Deist & Winterton, 2005). Haddadj & Besson (2000) epistemologically groups competency as individual and collective. Cheetham & Chivers (1996, 1998) propose a 5-component model including cognitive, functional, personal, ethical, and meta-competency. There are also some methodological differences because of cultural issues. For instance, cognitive and functional competencies as well as behavioural ones are focused in the United States of America. On the other side, cognitive and behavioural competencies are covered by functional ones in the United Kingdom. French approach distinguishes three distinct competencies; knowledge, experience, and behaviours (Le Deist & Winterton, 2005). German type competencies concentrate on learning process rather than results (Straka, 2004). Action competence approach of Germany describes three competencies; domain, personal, and social (Le Deist & Winterton, 2005). Domain competency includes cognitive and functional competencies, personal competency covers cognitive and social competencies, and social competency includes functional and social competencies. Austria adopts a similar categorization with Germany and groups as cognitive, social, and personal competencies (Archan & Tutschek, 2002). In general, there are four types of competency; cognitive, personal, functional, and ethical (TRACE Project Report, 2015). On the other hand, German system has been output-based and categorized competency as functional, personal, and social (Straka, 2004; Le Deist & Winterton, 2005). While functional competencies focus on cognition and tasks such as problem solving and analytic thinking, personal competencies have individual and social sides such as developmental opportunities and work-life balance. Likewise, social competencies are related to humanitarian and societal cases such as responsibility, collaboration, communication, and cooperation. There is a similar categorization in Austria model: cognitive, personal, and social (Archan & Tutschek, 2002). Cognitive competencies include knowledge, skills, and abilities of a person towards the job. Personal competencies are related to openness to development and motivation while social competencies collaboration, teamwork, and social responsibility. One may summarize common characteristics of American and European competency models that they are both based on knowledge and experience, and they both include behavioural dimension (Dejoux, 1999). To this end, we adopt a common view and group competencies as organizational or core, managerial, and functional. Core competencies are asked from all people in the organization irrespective of their statute and position, ranging blue-collar workers to top management (Raja & Swapna, 2010). Core competencies direct human capital towards vision of organizations and are related to values, beliefs, intentions, and emotions of the organization (Gangani et al., 2006). Diversity, sustainability, trust, innovativeness and relevancy may be given examples for core competencies. Core competencies are contingent based on organizational culture, management team, regulations, and industry (Tricker & Lee, 1997). Managerial positions and manager candidates are awaited to have managerial competencies. Managerial competencies cover organization specific processes for organizational success (Raja & Swapna, 2010). Punctuality, documentary, data collection, programming, meeting management, and budgeting are examples for managerial competencies. American Management Society defines 5 managerial competencies as technical knowledge, intellectual capital, entrepreneurship orientation, relation management, and task orientation based on a nationwide research (Hayes, 1979). Russell (2000) categorizes managerial competencies into three groups; conceptual, technical, and relational. Robertson and his colleagues (1999) list managerial competencies as action, motivation, creativeness, flexibility and sen362

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sitivity, leadership and communication, autonomy, and analysis. Scullen and his colleagues (2003) add compliance to managerial competencies. No matter what managerial competencies cover, it is obvious that they refer to cognitive and initating skills in an organizational context. Managerial competencies ask us to comprehend human, organizational structure, processes, and policies. Competencies required for excellence regarding working style are functional ones. Functional competencies include knowledge, skills and processes to make core product/service of the organization in an excellent manner differentiating the organization from the rest (Raja & Swapna, 2010). Product/ service know-how, service presentation, research, indigenous design, and technology literacy are some examples for functional competencies.

Competency Framework Competency framework is the output of competency modelling, the art and science of identifying the success factors for organizations to distinguish it best from the rivals by driving higher performance led by cultivating the competencies of human resources (PAHRODF, 2017). By creating competency modelling, types and levels of competencies may be identified in order to conduct the job in the most efficient manner and to meet the possible gap between current and future needs of the organization (Mansfield, 2000; Vraniak et al., 2017). Management by competency modelling is human-centric and stresses the significance of human resources to achieve organizational success (Wickramasinghe & Zoyza, 2008). In order to conduct mission of an organization and gain a competitive edge, competencies of human capital is expected to comply with and support organizational strategies. It seems impossible that strategies may not be realized unless competencies are not well conceptualized and modelled for the organization (Cardy & Selvarajan, 2006). Competency based management facilitates identification of knowledge, skills and behaviours related to organizational strategies and policies, of which human resources have today and will require in the future (Draganidis & Mentzas, 2006; Stokes & Oiry, 2012). It provides an integrated design for a sustainable and strategic human resources management. Competencies are common language for organizations to manage and retain qualified workforce Competency-based human resources management is depicted on Figure 1. Figure 1. Competency-based human resources practices

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Retention of qualified personnel, succession plans, backups, developmental plans, training needs identification, performance appraisal, and staffing are some human resources practices based on competency framework (Sparrow, 1995; Markus et al., 2005; PAHRODF, 2017). Competency framework is a gateway for organizations to meet changing demands of the market by developing talent portfolio for a more competitive and volatile environment and align effectiveness of human resources with the changes (Gangani et al., 2006). Competency framework facilitates to benefit from trainings within lifelong learning concept. Training needs are identified in accordance with knowledge, skills, and abilities related to competencies. Moreover competency framework aligns human resources practices with organizational strategies and policies (Lucia & Lepsinger, 1999; Shipmann et al., 2000; Gangani et al., 2006). Performance appraisal clearly lays out whether employees have the competencies described in the job descriptions. Therefore, behaviorally-based performance standards are detailed in the framework and employed in the organizations (PAHRODF, 2017). Regarding staffing, competency-based practices highlight quality rather than quantity (Lucia & Lepsinger, 1999). Therefore, while return on investment on human capital is maximized, risk taken by employing unqualified person who does not fit the organization is minimized. There are also some personal benefits of competency framework for employees. Job demands are clearly defined, personal and professional development are well organized, organizational and environmental factors are unambiguously determined, performance appraisal is fairly and objectively made, person-job fit is sought in the beginning. Competencies are used to develop personal growth and career development plans for professionals (Draganidis & Mentzas, 2006: 51-64). In addition, competency framework facilitates managers to assess performance with objectivity and to communicate expectations and obligations effectively with employees (PAHRODF, 2017). Wright (2005) defines three key performance indicators for competency framework; engagement, empowerment and accountability. Regarding engagement job demands and resources are clearly identified, empowerment refers to testing compliance of criteria with human-centric practices, and accountability urges organizations to create a task- and human-oriented workplace culture for a sustainable success of organization’s mission and vision. Areas to benefit from competency framework may be listed as a summary. • • • • • • • • • • •

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Change management Gaining sustainable competitive advantage Backup Plans Professional development Analysis Role certainty and job characteristics Integrated HR strategies Total quality management and standardization Recognition and rewarding Motivation Effectiveness and productivity

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Best Practices on Competency Framework Competency framework of OECD is composed of technical and core competencies. Technical competencies are identified as a prerequisite to perform a specific job; but, they are not documented in the framework. Instead, they are highlighted in job vacancy announcements. On the other hand, OECD defines core competencies and describes key indicators of the competencies in concordance with levels related to the positions. Each level of the core competencies has behavioural indicators that highlights how an individual can demonstrate that competency. Behavioural indicators are designed to show the requirements for successful performance. Levels span 1 through 5 with a bottom-up approach. That’s to say, level 1 is typically associated with jobs such as assistants, secretaries and operators whilst level 5 typically associated with jobs such as heads of division, counsellors, deputy directors and directors. OECD competency framework groups competencies into three clusters, i.e. strategic, interpersonal, and delivery-related. The categories are not relevant with the positions and the competency types. OECD lists 15 core competencies under three clusters: •

•

•

Interpersonal ◦◦ Client focus ◦◦ Diplomatic sensitivity ◦◦ Influencing ◦◦ Negotiating ◦◦ Organisational knowledge Delivery-related ◦◦ Analytical thinking ◦◦ Achievement focus ◦◦ Drafting skills ◦◦ Flexible thinking ◦◦ Managing resources ◦◦ Teamwork and team leadership Strategic ◦◦ Developing talent ◦◦ Organisational alignment ◦◦ Strategic networking ◦◦ Strategic thinking

The United Kingdom Civil Service competency framework is well designed and operated one as another best practice. Civil Service Values are at the heart of the framework surrounded by three clusters, i.e. setting direction, delivering results, and engaging people. Worker’s Educational Association (WEA) competency framework is prepared according to British context, puts the values at the centre, and is made up of five competencies; achieving results, working collaboratively with others, managing self, delivering excellent service, and behaving student- and organization-focused (WEA, 2018). WEA believes that competencies will support organizational performance and competency framework describes how the organization wants everyone to behave at work. Competency framework is designed to supports recruitment, performance management, and personal and occupational development. WEA

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details competencies at five levels where level 1 applies to all trainees and apprentices and level 5 applies to strategic and senior leaders. International Fund for Agricultural Development (IFAD) adopted competency framework in 2004 and reviewed in 2013. IFAD designs four clusters comprising ten competencies at two levels. Level 1 behaviours are relevant with all staff while level 2 ones have wider impacts and are relevant with seniors. Four clusters and relevant competencies are listed below. •

•

•

•

Developing the business ◦◦ Strategic thinking and organizational development ◦◦ Demonstrating leadership ◦◦ Learning, sharing knowledge and innovating Achieving results ◦◦ Focusing on clients ◦◦ Problem-solving and decision-making ◦◦ Managing time, resources and information Working with others ◦◦ Team working ◦◦ Communicating and negotiating ◦◦ Building relationships and partnerships Managing people ◦◦ Managing performance and developing staff

A METHODOLOGY PROPOSAL FOR COMPETENCY FRAMEWORK A three-component competency framework may be proposed in compliance with global trends, organizational strategy, and human resources policy. The model comprises core, managerial, and functional competencies. While designing a competency framework, labour supply-demand balance is taken into account, developing human capital by job-based learning concept is aimed, organizational culture is complied, performance is easily measured, and innovation and continuous improvement are adopted. As Rothwell and Kazanas (2011) state, organization specific competency framework is more successful in achieving organizational and personal development. Methodology proposed here is process based, outcome oriented, innovative, task oriented, human centric, and in compliance with global trends. Reminding that one-fit-all recipes are not welcomed, methodology proposed here is a mix of models on hand in use. As Rothwell and Lindlom (1999) suggest, in order to avoid conceptual ambiguity and focusing lessons-learnt, methodology completely ensures future orientation by employing meta-competencies such as open to development and not heavily using terminology. We all know well that the model depends upon scope, context, and autonomy dimensions to determine the level and details. However, a methodology to create a competency framework employed by organizations may comprise the following four steps: • •

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Creating a competency dictionary Collecting data ◦◦ Organizational Analysis

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• •

▪▪ Organizational Chart ▪▪ Departmental functions ◦◦ Strategy Analysis ▪▪ Mission, vision, and core values ▪▪ Strategic goals and targets ◦◦ Process Analysis ▪▪ Processes and procedures ▪▪ Work flows ◦◦ Job Analysis ◦◦ Job descriptions ▪▪ Interviews ▪▪ Focus groups Creating competency pool Creating competency framework

While creating a competency framework, support of top management is critical since vision of the organization makes sense with appropriate talents employed in the organization (Gangani et al., 2006). Thus, competencies are to be aligned with organizational strategies and policies (Cardy and Selvarajan, 2006). Competencies are to be clearly defined and made observable by underlying main behaviours related to the competency (Mansfield, 2000). Basic principles in measuring competency are as follows (McClelland, 1973); • • • • • • •

Group competencies in regard with organization size, strategies, policies, and industrial factors, Monitor personal development growth, Measure observable behaviours, Transfer competency not only to job attitudes but extra-role behaviours, Measure not reactive behaviours but proactive ones, Make results common, Standardize desired end states.

Competency framework is detailed regarding observable behaviours and levelled according to positions in the organization because competencies differ by position and reflection on behaviour is diversified. The number of levels span from three to five (PAHRODF, 2017). Nonetheless, at most a four-level structure may be convenient for the organizations. As level increases, it also covers the specifications of the lower. There are some descriptive information on levels in Table 1. The levels depends upon scope, context, and autonomy dimensions. Scope is about the responsibilities of the position while context describes the environment where the jobs are done. Autonomy is related to the degree of supervision asked and the amount of power to make a decision. Levelling by means of the rubrics mentioned above facilitates the employment of competency framework. First, jobs and roles in the organizations are easily compared. Once behaviour-based performance indicators are clearly defined in accordance with the levels, performance management is easily made.

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Table 1. Levels for competency framework Level

Remarks

Level 1

• Beginner level • Covers all employees. • Applies to employees who have no managerial responsibility.

Level 2

• Intermediate level • Applies to lower level management. • Applies to few employees whose job requires specialties (e.g. consultant, expert, etc.).

Level 3

• Competent level • Applies to employees who make long-term planning. • Applies to mid-level management. • Applies to few employees whose job requires specialties (e.g. advocate, project coordinator, etc.).

Level 4

• Expert level • Applies to upper-level management.

THE FOURTH INDUSTRIAL REVOLUTION General Description 1st industrial revolution is caused by mechanization, the cause of the second is the use of electrical energy and 3rd industrial revolution is resulted from electronics and automation (Lasi et al., 2014). The fourth industrial revolution is characterized by technical integration of cyber-physical systems into manufacturing and logistics as well as the use of the Internet of Things and Services in industrial processes (Kagermann et al., 2013). The fourth industrial revolution is organization of life cycle of a product from customer requirements through recycling following delivery to the end user (Prifti et al., 2017). The fourth industrial revolution is differently recalled worldwide even though the context is similar. German-speaking countries call it “Industry 4.0” while France prefers “Industrial Internet” and the United Kingdom uses “Smart Industry” (Grangel-González et al., 2016; Prifti et al., 2017; Ras et al., 2017). And Turkey approaches it as “technological” or “digital transformation”. “Smart Factory” is also commonly used for the fourth industrial revolution. The aim of the fourth industrial revolution is efficient, flexible, and customized production by means of digitally decentralized controls, self-organized supply chains, and fully automated factories by real-time and sensor technologies (Kagermann et al., 2013; Gebhardt et al., 2015).

The Needs and the Common Trends of Technological Nature of the Fourth Industrial Revolution All these industrial revolutions do not influence only the production itself, but also the labour market and the educational system as well (Benešová & Tupa, 2017). It is worth noticing that the concepts addressed by the fourth industrial revolution pertain to different disciplines and key enabling technologies including robotics, big data analysis and, in particular, visual computing and simulation (Longo et al., 2017). The Internet of Things, the Internet of Services and the Internet of People will make connection among the actors in smart factories: machine-machine, human-machine or human-human, and at the same time an enormous amount of data will be obtained (Benešová & Tupa, 2017). To this end, nine technologies are commonly employed by all nations in effort of policy-making supporting the fourth industrial revolution (BCG, 2015): 368

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• • • • • • • • •

Big data and analytics Autonomous robots Simulation Horizontal and vertical system integration Internet of things Cyber security The cloud Additive manufacturing Augmented reality

The Competency Gaps in the Fourth Industrial Revolution The fourth industrial revolution will not only change the environment the work is done but also affect the work itself as well as the way the work is performed (Prifti et al., 2017). As Longo and his colleagues (2017) state, employees are required to be more flexible and to display adaptive capabilities in this dynamic working environment. The tasks they work on are getting less routine and ask for continuous knowledge and skills development (Ras et al., 2017). Since talents are rare, many positions remain unfilled and it seems that they will stay unoccupied due to skills shortage (Ras et al., 2017). On the other hand, competency requirements are changing in concordance with the changes in the workplace. The capabilities of digital solutions have opened up new opportunities and raised ambitious challenges for manufacturing systems (Longo et al., 2017). But only qualified and highly educated employees will be able to control these technologies (Benešová and Tupa, 2017). Once competency requirements are asked for facilitating effective performance in the workplace, a clear guideline for competencies needed for transformation through the fourth industrial revolution is a must for companies to ease the transformation process (Gebhardt et al., 2015; Prifti et al., 2017). Therefore, European Commission (2016) reviews key competencies required in the workplaces and proposes an action plan as a part of life-long learning. In addition, smart factory concept is developed to support life-long learning to adapt incumbent employees to new work environment. Shallock and his colleagues (2018) advise that smart factory cover technical, transformation, and social skills.

SOLUTIONS AND RECOMMENDATIONS The fourth industrial revolution transforms the workforce and workplace environment. As design, production, and distribution systems change by Industry 4.0 concept, nature of work changes and job demand and resources diversify through the change. New workplaces are designed as socio-technological places that digitalization is fully employed while providing socialization areas for employees. Advanced technology wants workers equipped with high technological skills as well as creative thinking. It is not wrong to say that competencies change by the changes developed within the Industry 4.0 concept. There are some competency proposals for new era. For instance; Curtis and McKenzie (2002) stress communication, teamwork, problem-solving, initiative, planning and organizing, self-management, learning, and technology skills. Ananiadou and Claro (2009) research 21st century skills in 16-member countries in OECD. They find out that all countries employ regulations to teach 21st century skills in educational institutions. However, there is no common monitoring and evaluation mechanisms in the 369

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countries. The fourth industrial revolution requires identifying basic research, analytical, and entrepreneurial thinking for new solutions, monitoring of practical effects of such implementations and reviewing the potential for future (Grzybowska and Łupicka, 2017). The timely analysis of the obtained data is important for planning and managing of the flexible production. The obtained data can contain classified information and this leads to increased demands on cyber security to prevent leaks of any data (Benešová and Tupa, 2017). Grzybowska and Łupicka (2017) review literature on managerial competencies and conclude that future managers and engineers require eight main competencies listed below. • • • • • • • •

Creativity Entrepreneurial thinking Problem solving Conflict solving Decision making Analytical skills Research skills Efficiency orientation

Prifiti and his colleagues (2017) survey the literature and find out that 68 competencies are mostly mentioned and related with the fourth industrial revolution. The researchers employ SCL universal competency model to develop the framework in order to guide IS, IT, and engineering graduates. SHL universal competency model developed by academicians and practitioners is a behaviour-based competency framework consisting three hierarchical levels as factor level, competency level, and behaviour level (Bartram, 2012). Factor level called “Great Eight” is formed by eight core competencies underpinning organizational and individual performance. According to Bartram (2005), great eight competencies are emerged as a result of factor analyses and multidimensional scaling analyses. At the second level, 20 competencies are described under eight clusters. 112 components are defined as behaviours under the competencies. Factor levels and 20 competencies as well as 68 components found by Prifiti and his colleagues (2017) are displayed on the Table 2 below. Kantane and her colleagues (2015) ask employers to categorize competencies of employees. Attitudes, motivation, and intellectual properties form a group as well as general skills, appearance and social behaviour form the second group. And third group is formed by professional knowledge and skills. What’s interesting is attitudes, motivation, and intellectual properties are attached great significance, more than the rest two groups. And, whatever employee’s profession is, the need for soft skills such as communication and collaboration is ever growing. Communication is the most researched competency in the literature, followed by technology literacy, problem-solving, life-long learning, teamwork, and creativity (Prifti et al., 2017). Communication skills are attached great significance because workforce for the fourth industrial revolution is asked to excel in general competencies like managerial skills, teamwork, and customer relations as well as strong domain-specific competencies like advanced technology and digital skills (Kusmin et al., 2017). This is true and compliance with EU and UN declarations about the workplace skills as well as ten top competencies business world will be seeking in 2022 mentioned by the World Economic Forum (2018). The World Economic Forum (2018) underlies ten top competencies

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as analytic and innovative thinking, active learning and learning strategies, creativity, proficiency in new technologies, critical thinking, complex problem solving, leadership and social influence, emotional intelligence, quick comprehension, and system analysis. The EU unveils eight competencies in 2001 that EU citizens would have to move the Union further: Table 2. Competencies in the fourth industrial revolution Big Eight Leading & Deciding

Competency Dimensions Deciding and Initiating action Leading and Supervising

Leadership Skills

Working with People

Teamwork Collaborating with Others Communicating with People

Adhering to Principles and Values

Respecting Ethics Environmental Awareness Awareness for Ergonomics

Relating and Networking

Compromising Creating Business Networks Maintaining Customer Relationships

Persuading and Influencing

Negotiating Emotional Intelligence

Presenting and Communicating Information

Presentation and Communication Ability

Writing and Reporting

Targeted/Technical Communication Literacy

Applying Expertise and Technology

IT and Technology Affinity Economics Extract Business Value from Social Media Big Data/Data Analysis and Interpretation

Analyzing

Problem Solving Optimization Analytical Skills Cognitive Ability

Learning and Researching

Life-long Learning Knowledge Management

Creating and Innovating

Innovating Creativity Critical Thinking Change Management

Formulating Strategies and Concepts

Business Strategy Abstraction Ability Managing Complexity

Planning and Organizing

Project Management Planning and Organizing Work Management Ability

Delivering Results and Meeting Customer Expectations

Customer Orientation Customer Relationship Management

Following Instructions and Procedures

Legislation Awareness Safety Awareness Individual Responsibility

Supporting & Cooperating

Interacting & Presenting

Analyzing and Interpreting

Creating and Conceptualizing

Organizing and Executing

Competencies Decision Making Taking Responsibility

Source: Prifti et al., (2017).

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• • • • •

Native language proficiency; Foreign language proficiency; Ability to apply basic math and science; Ability to learn by digital function; Abilities to learn skills such as time-management, problem-solving, information seeking and applying; Social commitment; Entrepreneurship such as creativity, planning, achievement motivation; and Ability to appreciate culture such as art, music and literature.

• • •

Likewise, the UN declares 21st century skills as: • • • • • • • • • •

Creativity and innovation Critical thinking, problem solving, decision making Learning to learn Communication Collaboration (teamwork) Information literacy ICT literacy Citizenship – local and global Life and career Personal and social responsibility – including cultural awareness and competence

There is no doubt that a standard competency framework for the fourth revolution may not be proposed. However, as a starting point, a generalized competency framework is formed here to be taken as a sample for further conceptual and empiric studies. But it is critical that competency framework designed here is for general purpose. Table 3. Competency framework Core Competencies

Managerial Competencies

Functional Competencies

Stakeholder Focus

Planning and Organizing

Quality Assurance

Analytical Thinking

Future Orientation

Technology Literacy

Communication

Adaptation

Professionalism

Openness to Learning and Development

Leadership

Business Acumen

Social Commitment

Change Management

Knowledge Transfer and Sharing

Teamwork

Holistic Approach

Autonomy

Creativity

Mentorship

Strategic Alignment

Corporate Entrepreneurship

IT security

Proactivity

Risk-based Working

Ethics and Compliance Cyber Security

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Competency framework is not a “will” list; instead, it is a “must” list that carries the organization to the vision (Gangani et al., 2006). Competency framework here presented in Table 3 is a sample for the organizations to survive in the VUCA environment and the fourth industrial revolution.

FUTURE RESEARCH DIRECTIONS Qin and his colleagues (2016) state that the technology roadmap for accomplishing industrial revolution is still not clear and the gap analysis between current manufacturing systems and the fourth industrial revolution requirements shows that there is still a long way to go (Longo et al., 2017). Therefore, researchers are highly recommended that requirements of contemporary manufacturing systems regarding human resources be researched and modelled. On the other hand, Cseh (2003) highlights the effect of national culture on competency. It is obvious that nations and industries employ technological transformation at different rates in accordance with their skilled workforce and finance structure. Hence, national and industrial employment policies may be studied and developed to ease the transformation duration. There is no standard modelling for the companies; however, it is renowned that competencies correlated with higher performance can be measured and developed by training other developmental activities (PAHRODF, 2017). Thus, an integrated human resources management model may be developed to support competency frameworks for future work environment. Schools have mission to develop human capital for the market. In order to keep up with the changes of the skills required in the industry, schools may change curricula in accordance with future workforce competencies. Towards this end, researchers and practitioners may collaborate and design new curricula covering university-industry collaboration, technology transfer, and apprenticeship models.

CONCLUSION It is critical for firms to identify a vision, to attract qualified human resources equipped with or ready to equip with competency framework determined, to motivate the qualified workforce, to allocate resources to complete the mission, and to speed activities up to achieve the desired end state. As Gudanowska and his colleagues (2018) recall, organizational success through transformation is based on three milestones; managerial capacity, innovative technology, and competent employees. Therefore, it is of great significance to analyse the organization and create a competency framework to harbour all relevant steps to move the organization further. Because competencies are considered significant factors determining organizational success (Raja & Swapna, 2010), firms are expected to create competency frameworks specific to them by detailing observable behaviours in compliance with organizational culture, strategy, and policies. Unlike general belief in less use of human resources in future’s work environment, studies indicate that growth in manufacturing processes results in 6 percent increase in workforce demand. But it is true that required skills at workplace will be changed. Nations and industries are to prepare for competency transformation as well as digital transformation. To this end, this study proposes a generic competency modelling methodology and accordingly a competency framework. Organizations may embrace methodology to prepare competency frameworks specific to themselves and align human resources practices with the framework. 373

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KEY TERMS AND DEFINITIONS 21st Century Skills: UN declares 21st century skills as: creativity and innovation; critical thinking, problem solving, decision making; learning to learn; communication; collaboration (teamwork); information literacy; ICT literacy; citizenship (local and global); life and career; and personal and social responsibility including cultural awareness and competence. Competence: Knowledge, skills, and abilities as well as personal attributes and motivational factors for higher performance workers.

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Competency: A guiding tool including knowledge, abilities, distinguished personal attributes, and behaviours for higher performance contributing to achieving strategic goals of the company. Competency Framework: A belief that one’s own culture is superior to other cultures. Competency-Based Human Resources Management: The belief that family is central to well-being and that family members and family issues take precedence over other aspects of life. Core Competencies: Competencies asked from all people in the organization irrespective of their statute and position, ranging blue-collar workers to top management. Functional Competencies: Competencies required for excellence regarding working style. Managerial Competencies: Managerial positions and manager candidates awaited to have managerial competencies covering organization specific processes for organizational success. VUCA: Acronym stands for volatility, uncertainty, complex, and ambiguity.

This research was previously published in Human Capital Formation for the Fourth Industrial Revolution; pages 18-43, copyright year 2020 by Business Science Reference (an imprint of IGI Global).

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Managing Social Innovation Through CSR 2.0 and the Quadruple Helix: A Socially Inclusive Business Strategy for the Industry 4.0 José Manuel Saiz-Alvarez https://orcid.org/0000-0001-6435-9600 EGADE Business School, Tecnologico de Monterrey, Mexico

ABSTRACT The quadruple helix models are widely used when you want to have an integrating vision of the strategies used to combat poverty in emerging countries, including Mexico. The objective of this chapter is to propose a novel model of quadruple helix based on ethics and CSR 2.0 that can lay the foundations to develop the Industry 4.0 in emerging countries. To achieve this objective, the author distinguishes between CSR 1.0 and 2.0. Second, these concepts are united with the economy of the common good and the economy of solidarity. These conceptual bases will allow us to develop the relationship between business ethics and the Industry 4.0 to reach some conclusions.

INTRODUCTION When applied to desired social changes, social innovation is composed of changes in the cultural and regulatory business structure that optimizes collective resources focused on improving socioeconomic development (Heiskala, 2007). These changes are reinforced with the use of Corporate Social Responsibility (CSR) 2.0 policies integrated into a quadruple helix model that is proposed in the chapter. The combination of this socially comprehensive business strategy reinforces the firm and increases its efficiency and the generation of EBITDA (Earnings Before Interests, Taxes, Depreciation, and Amortization). The DOI: 10.4018/978-1-7998-8548-1.ch020

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 Managing Social Innovation Through CSR 2.0 and the Quadruple Helix

objective of this work is to propose a Model of Quadruple Helix rooted in CSR 2.0 and the Economy of the Common Good with the goal of promoting the Industry 4.0 grounded in ethics. Although Bowen (1953) is considered the modern father of the CSR, also Bernard (1938) and Kreps (1940) have seminally thought about this issue (Milian, 2015). These seminal works have been enlarged by Davis (1960, 1967), cited by Schwartz and Carroll (2003), in which he asks what the entrepreneur owes to society (social mortgage)(Ramírez, 2016; Saiz-Álvarez, 2017a) and what responsibility companies have in front of the community. The role of the entrepreneur in almost all the entrepreneurial ecosystems of the planet is fundamental, especially when technology is applied (Bernal-Conesa et al., 2017). These ecosystems create triple helix models (Carlsson and Stankiewicz, 1991; Carlsson et al., 2002; Edquist, 2005; Bergek et al., 2005) defined by their diffuseness, heterogeneity, intense focus on institutions, low visibility of the role of individuals in the innovation process, and system boundaries (Ranga and Etzkowitz, 2013). Triple helix models that are drifting toward quadruple helix models (Figure 1). As a result, quadruple helix models are permeating both civil societies and organizations, the need for greater awareness and social support towards the most disadvantaged and that population at risk of social exclusion. Models formed by the positive effects created by the interaction between: • • • •

Universities, defined by the 7-K (Know-how, know-why, know-who, know-where, know-whom, know-when, and know-what) (Saiz-Álvarez and García-Ochoa, 2008); Non-governmental organizations (NGOs) established by the social assistance and the fight against poverty and inequality; Business organizations, with the creation of positive externalities regarding job creation and wealth for a good part of society; and The public sector, whose processes of public intervention, mainly through fiscal policy, generate crowding-in effects to benefit the population.

The Latin American and the Caribbean (LAC) countries are strengthening quadruple helix models for social change. While in developed nations individual entrepreneurship is the predominant one, in the developing countries it would be more desirable to create a social enterprise that aims to integrate the most disadvantaged communities, as well as to achieve a process of sustainable social change. Thanks to social entrepreneurship, combined with CSR-based business policies, the methods of social transformation lead to the achievement of more just and solidary societies defined by the creation of a broad middle class that sustains with the payment of taxes to the State. For this reason, public intervention in developing countries is more necessary due to the crowding-in effects it generates since the private sector is fragile. In this sense, the model existing in the countries of the first world must necessarily be different from that which arises in the third world if a real social change is desired. Based on Carayannis and Campbell (2008) who affirm that the quadruple helix is formed by the sum of multi-level innovation systems and networks, knowledge clusters, and technology-based life cycles, the quadruple helix model defined in this work is created by the amount of: 1. Universities and academia, characterized by R&D, knowledge, and innovation; 2. Industry and firms, where the combination of investment and employment is vital to creating social wealth; 3. Civil Society and NGOs focused on aid and social assistance, and

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4. Public Administration directed to develop crowding-in effects and fiscal distribution through a progressive tax system. 5. This theoretical quadruple helix model is also defined by including accelerated feedback formed by the merge of industrial R&D, the intensive use of mass media, the development of social networks between universities, civil society and NGOs, and public administration. The combination of these economic variables determines competitiveness, innovation, and R&D, which are drivers to develop the Industry 4.0 (Briones-Peñalver et al., 2018) that can benefit the society through CSR policies. Resulted from this constant interaction, social and economic wealth are both created. Figure 1. The Quadruple Helix Model

Source: Adapted from Harbers, Van Waart, and Visser (2015) and Lindberg, Lindgren, and Packendorff (2014)

Corporate Social Responsibility (CSR) is crucial in quadruple helix models, as is defined as the “compliance with society’s economic, legal, ethical and discretionary expectations about business organizations at any given time” (Carroll, 1979: 500). Expectations to be met in the medium and long-term to generate social change. Carroll (1991) states four levels of CSR responsibility: economic (having benefit), legal (obeying the law), ethics (ethical behavior) and philanthropic (being a good corporate citizen). However, it is not enough to comply with all four levels of responsibility, as CSR-based companies must invest more in intellectual capital, physical environment, and relations with stakeholders (Mendizábal, and Tufiño, 2015). These investment processes have made CSR evolve, although this evolution has not been the same in all countries, is the highest distance among the European nations and the nations of Latin America and the Caribbean, although there might be no significant difference among the LAC countries, but there are significant differences on this matter among European nations, as organizations in the North and West do much better in comparison with organizations in the South and Eastern Europe. The best CSR strategy is the one that is carried out locally to fully adapt it to a specific context (Ikeda, Ferranty, and Mendes,

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2015) and thus have a social impact. Social impact that will lead to social change that is desirable for the entire population and the economy in general. We will see this evolution in the following epigraph.

BACKGROUND CSR 1.0.: Philanthropy and Charity It must be conducted by the ethical behavior of organizations (public or private) to guide CSR by benevolence and kindness (CSR 1.0), which strengthens the pride of belonging to the firm (John et al., 2017). Besides, this pride enhances the process of attraction and retention of the best intellectual capital (Lim and Greenwood, 2017). By definition, ethical behavior eliminates cases of corruption, regardless of where they occur. In this respect, Adda, Azigwe, and Awuni (2016) distinguish between three levels of ethics: 1. The law that determines what practices and norms are allowed or prohibited to establish the minimum degree of welfare and legal certainty to be met in society; 2. Organizational policies and procedures to guide companies and individuals in their decision-making process, and 3. The moral stance defined by ethical-driven decision-making when there is no ad hoc law approved for that purpose. In this ethical process, the State (in its various organizations and schemes of action that change slightly among nations) has a vital role in fighting corruption. With corruption it is not possible to implement CSR-based policies and, consequently, to generate social change. Therefore, the fight against corruption must be holistic, at all levels, regardless of the public or private ownership of organizations. Complementary to avoiding corruption practices, particularly in some Latin American and the Caribbean countries (Table 1), firms must generate EBITDA (Earnings Before Interests, Taxes, Depreciation, and Amortization) to carry out CSR-based policies. At least, a percentage of this business benefit is aimed at improving the social environment of companies, which enhances their brand and corporate reputation, and complements public policies carried out with the same objective. It is important to note that companies often confuse CSR with philanthropy or charity, and even with humanism (Lamoneda, 2016). The difference comes because, while philanthropy and charity alleviate misery at the level of distribution, humanism seeks to annihilate indigence at the level of production (Kuljic, 2016). However, CSR aims to provide a way of life for those most in need. In short, and as the English proverb states, ‘Give a man a fish, and you feed him for a day; teach a man to fish, and you feed him for a lifetime.’ As a result, the beneficiary learns and in the medium term will not live either from philanthropy or charity. Philanthropy and charity-based business policies have a more significant impact when are done by worldwide-known corporations. Chen and Lee (2017) show that just large and well-known medium-sized enterprises when applying CSR, augment their market value and, in the case of being listed on the Stock Exchange, increase their market capitalization, measured as the market value of the shares multiplied by their market value (price). For this reason, the impact of CSR on Microenterprises and Small and Medium Enterprises (SMEs) is meager and even inexistent. As a result, when SMEs apply CSR-based policies, they make it for ethical, philanthropic or charitable reasons. 383

 Managing Social Innovation Through CSR 2.0 and the Quadruple Helix

Table 1. Corruption levels in Latin America and the Caribbean 2016 Rank

Country

2016 Score

2015 Score

2014 Score

2013 Score

2012 Score

21

Uruguay

71

74

73

73

72

24

Chile

66

70

73

71

72

41

Costa Rica

58

55

54

53

54

60

Cuba

47

47

46

46

48

79

Brazil

40

38

43

42

43

87

Panama

38

39

37

35

38

90

Colombia

37

37

37

36

36

95

Argentina

36

32

34

34

35

95

El Salvador

36

39

39

38

38

101

Peru

35

36

38

38

38

113

Bolivia

33

34

35

34

34

120

Dominican Republic

31

33

32

29

32

120

Ecuador

31

32

33

35

32

123

Honduras

30

31

29

26

28

123

Mexico

30

31

35

34

34

123

Paraguay

30

27

24

24

25

136

Guatemala

28

28

32

29

33

145

Nicaragua

26

27

28

28

29

166

Venezuela

17

17

19

20

19

Source: Transparency International (2018)

One of the keys to successful CSR-based design and implementation is the resilience of those who apply it. In fact, for an organization to be more resilient, it has to diversify its leadership, so that the company has several leaders with high capacity of entrainment within the organization and thus reduce risks. This diversification of leaders also influences the democratization of the firm, both in the knowledge of the processes within the company and in the greater transparency that exists within it. This method of corporate democratization and therefore higher openness can be the first step towards the signing of joint ventures with other public and private organizations, both domestic and foreign. This process is even more efficient when the organization’s human resources are clear about the vision, mission, and values of the company, as well as the objectives of the company. However, when organizations only conceive CSR as philanthropy and charity, no results are achieved in the medium and long-term. That is why it can not be considered a valid CSR, but a timely aid process to solve a specific problem, without resulting in lasting social change. Therefore, the ideal situation is to refine the RSC to transform CSR 1.0 into CSR 2.0. A process described in the following section.

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 Managing Social Innovation Through CSR 2.0 and the Quadruple Helix

CSR 2.0.: Conscious Responsibility and Integral Management With CSR 2.0 companies and organizations are entering the Age of Responsibility defined by a comprehensive transformation of business structures based on CSR, environmental sustainability, and the common good. To achieve this triple objective, companies must 1. Have full sustainability in their productive and commercial processes and be directed towards a transformational leadership that shares knowledge towards the advantages of sustainability. To this end, companies have to articulate “blue skies,” that is, to achieve great objectives with great visions through policies anchored in transformational leadership. 2. Adopt CSR-based policies and the creation of foundations and, where appropriate, labor cooperatives and cooperative entrepreneurship among firms to enhance partners’ entrepreneurial status (Rezazadeh and Nobari, 2018), to create business groups that can compete successfully in international markets and thus achieve social change. 3. Empower women, trying to reconcile work and family life, as empowerment is defined as the decision-making ability of a woman regarding her strategic and non-strategic life choices (Ballon, 2018). The feminine gender plays a fundamental role in the humanity, for the procreation of the human species, as in the labor and familiar world. Hence, it is desirable that there should be no discrimination regarding labor and wages, since social justice must be given priority in any situation, regardless of the size and characteristics of the organization. 4. Change the vision and objectives of innovation, by moving from the sole benefit of the company to the whole society thanks to CSR. An impact in which the technology has a fundamental role that we could summarize synthetically in the following formula: INN = (B+I+T) x A being INN = Innovation, B = Benefit, I = Investment, T = Technology, and A = Attitude Have corporate transparency, that is, be honest and clear before the stakeholders (stakeholder group) to make the business sustainable. With transparency, the degree of commitment of the stakeholders is greater. 5. Adopt ISO 26000 standards to ensure good governance in the organization following a holistic and interdependent approach based on seven principles: a. Adopt human rights at work avoiding labor exploitation, especially child labor exploitation; b. Participate actively in the development of the community so that there are positive externalities within it; c. Protect consumers by offering products and services with the best price-quality ratio possible in the market; d. Perform work practices, especially among the youngest, to ensure continuity in the tasks to be performed in the company; e. Conduct fair operating practices within organizations fostering intrapreneurship and social justice, and f. Implement safe environmental policies to achieve sustainable and shared business activities (in the sense of not increasing the gap between rich and poor).

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 Managing Social Innovation Through CSR 2.0 and the Quadruple Helix

6. Conduct policies based on leadership, to reduce risks in the corporation and meet, as far as possible, the sustainability objectives. Based on all of the above, CSR 2.0 is “a new form of integrated and innovative management of companies” (Lamoneda, 2016: 63). Companies operating in the Latin America and the Caribbean nations apply CSR 2.0-based corporate policies when are demanded by their stakeholders, mainly clients who have a more significant social awareness of these corporate practices. This social-oriented behavior results from the characteristics of the European model based primarily on solidarity, cooperation, and mutual aid through the Structural Funds, formed by the sum of the European Regional Development Fund (ERDF), European Social Fund (ESF), Cohesion Fund, European Agricultural Fund for Rural Development (EAFRD), and European Maritime and Fisheries Fund (EMFF). With less intensity than in Europe, the Latin American consumer expects and appreciates, in particular, any certification or seal that demonstrates there is no economic, social and ecological dumping in the products or services they acquire. Certifications, stamps, and logos shown by socially responsible companies can attract socially responsible consumers and stakeholders, what reinforces the corporate’s reputation image. As a result, sales increase and new investors are invited to expand the company. So, by following Sajardo (2009), the badge to be granted CSR companies should be graded, so that only those institutions and businesses that comply with more than 80 percent of the proposed CSR measures should bear the ‘High RSC’, while the ‘Medium CSR’ mark will be for companies between 50 percent and 80 percent, and ‘Low CSR’ for those who did not reach 50 percent in their CSR application. Given its social impact, the implementation of CSR-based strategies positively affects the United Nations Millennium Development Goals (MDGs), the Human Development Index, and the Social Progress Index what benefits humankind. Regarding CSR as a useful tool to fight poverty following the MDGs, there are two opposing views, according to Valor and Merino (2007). The first is provided by multilateral organizations, such as the United Nations and the World Bank Group, and business organizations, such as the World Business Council for Sustainable Development, the Global Reporting Initiative (GRI) and the International Business Leaders Forum. For these organizations, the ‘CSR is, by its very nature, private sector development and is the perfect complement to the efforts of governments and multilateral organizations’ (Jenkins, 2005: 525). In fact, they affirm that ‘economic growth is the way to reduce poverty, so the company appears as a central element in the strategy for its eradication’ (Valor and Merino, 2007: 5). The second view is more critical concerning CSR, as both civil society and non-governmental organizations (NGOs) tend to be more skeptical about the results of implementing policies based on CSR in society. Where there is no possibility of discussion is about the impact on the Human Development Index (HDI) of corporate policies based on CSR. The more effective the policies carried out by companies regarding CSR are, the greater will be the HDI. Education and health, alone or simultaneously, positively affects HDI and the final effect depends on how the company applies CSR. In fact, the HDI is an indicator created by the United Nations Development Program (UNDP) to calculate the level of development that nations have in terms of GDP per capita in purchasing power parity (PPA), education (adult literacy level, level of education achieved, and length of years of compulsory education) and health (life expectancy at birth). The HDI provides values between 0 and 1, with 0 being the lowest rating and 1 the highest value. From these values, countries are classified into countries: • 386

With high human development (HDI> 0.8)

 Managing Social Innovation Through CSR 2.0 and the Quadruple Helix

• •

With an average human development (0.5 OIC -> OP (β=0.001, t=0.035) was not significant and the 95% Boot CI Bias Corrected: [LL = -0.050, UL = 0.062] straddle a 0 in between indicating that there was no mediation effect. Hence, H5 was rejected, and H6 and H7 were accepted. Table 5 summarized the hypothesis testing on the mediation effect.

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DISCUSSION Findings in the present study suggested that knowledge management capability plays the most significant role in influencing organizational innovation capability. Knowledge management capability has positive and significant effects on organizational innovation capability. These findings highlight the critical roles of knowledge management capability, which enable the value creation process via a strategic flow of relevant knowledge throughout the organization. Development of Industry 4.0 requires an innovative mechanism to manage the way of knowledge acquisitions, conversion and applications. Within the Industry 4.0 framework, big data are collected various automate electronic devices such as sensors and stored in a cloud-based database which eventually analyzed by much artificial intelligent system to provide real-time monitoring and control. Knowledge management capability of an organization becomes crucial for the company to achieve a competitive advantage through highly effective data processing and analyzing the process. Organizational innovation capability relying heavily on the organization’s capability to crystallize and connect process, which includes knowledge development, discovery, and capture. This study presented that organizational culture demonstrated a positive relationship with organizational innovation capability. These findings are consistent with Khan and Mir (2019) where organizational capabilities to innovate and change are deeply rooted in an organization’s ability to build on its traditions and past and to learn and be able to drive innovation streams. An influential organizational culture that includes the norms and values with support the generation of creative ideas that contribute to sustaining innovation in a company. A standard set of shared belief and understanding is vital in developing an organizational culture that promotes organizational innovation capability. Safe innovative environments that allow collaboration across various organizational boundaries; sharing and teaching among and across business units and alliances can be effective ways of promoting collaborative innovation. Contrary to expectations, this study did not find a significant relationship between human resource management and organizational innovation. An explanation for this could be related to the reduction of human interaction in the Industry 4.0 framework. Many data collection and analyzing processes have done using artificial intelligence. Data are collected, processed and interpreted by computer software which required very minimal human interaction. Most of the research explained that human resource management practices such as sourcing, deployment, and upgrading of human capital would influence organizational innovation performance level. However, within Industry 4.0 framework has disrupted the traditional human resource management. Many of the HR processes will be automated using new technologies such as Internet-of-Things, Big Data, and artificial intelligence. As a result, this could affect the established relationship between human resource management practices and organizational innovation capability. These findings suggest that organizational innovation capability will positively influence organizational performance. The findings showed a consensus result with the literature (Donkor, Donkor, Kankam-Kwarteng, & Aidoo, 2018; Saunila 2014). Organizational innovation involves the invariable implementation of new organizational practices that improve process efficiency and effectiveness. Within the Industry 4.0 framework, reconfiguration of the business model is required based on a different degree of innovation to embrace the challenges posed by the digitalization of the business process. Ibarra, Ganzarain, and Igartua (2018) emphasized that disruptive innovation that provides the change of almost all the elements of the business model is needed for the organization to compete in the Industry 4.0 environment. To improve organizational performance from a traditional business model, incremental

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innovation of both value creation and value delivery is essential. Radical innovation on the actual business model is needed through reconfiguration and diversification of value network ecosystem focusing on product innovation, service innovation, and customer innovation. This study proved that organizational innovation capability mediates the relationship between organizational culture and organizational performance. In order words, the positive effect of organizational culture occurs because of culture fosters innovation capability among the employees, which eventually improves the organization’s performance as innovation is a process of transforming business ideas into something practical and profitable. Therefore, encouragement to supply ideas needs to be substantial to channel the creative ability of the employees to convert ideas into innovations. Positive organizational culture promotes creativity, risk-taking attitude, freedom, and flexibility, which is an excellent recipe for organizational innovation. Only when the culture is in favor of innovation, employees will be worryfree to express their ideas and encouraged to challenge the existing way of doing things. Organizational culture is influencing organizational performance indirectly in such a way that it provides a positive environment to encourage innovation, and subsequently, organization performance improves with improvement achieve in any business deliveries. This research has investigated and concluded that the positive relationship between knowledge management capability and organizational performance is mediated by organizational innovation capability. The findings explained that excellent knowledge management capability would heighten organization innovation capability and subsequently improve organization performance. Knowledge management capability has been considered as a strategic resource for an organization to sustain a competitive advantage. Knowledge management capability ensures effective dissemination of knowledge throughout the firm and contributes to organizational perform via the enhanced capability to respond to new and unusual situations. As a result, knowledge management capability allows knowledge gaps to be identified, complex data to be organized into useful information, and stimulating creative thinking skills that lead to innovation. Organizational innovation capability helps the organization to reshapes the competitive landscape and creates new market opportunities. All this will not happen without a proper knowledge management system to serve as the backbone to manage the abundance of big data in the Industry 4.0 environment. As such, an excellent knowledge management capability will influence organization innovation capability and ultimately improve organizational performance.

CONCLUSION There has been relatively little literature on the organizational performance of Malaysian SMEs in the biomass industry, especially with the mediating role of organizational innovation capabilities. This study provides theoretical contributions and bridges the gaps of prior literature by providing insights on the positive and indirect effect of human resource management, organizational culture, knowledge management capabilities on organizational performance, with the mediating effect of organizational innovation capability. The present study integrates RBV and dynamic capabilities theories to enrich further understanding associated with the dominant influence of organizations’ resources and capabilities on organizational performance. The empirical result reflected that organizational culture, knowledge management capabilities are significantly influencing organizational innovation capability and in the end, impact on organization performance.

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Due to the turbulent business environment in Industry 4.0, the practical contribution of this study revealed the salient information that can be applied by business managers in SMEs biomass industry, who are struggling to achieve higher organizational performance. The business managers should strive to remove the cultural barrier that inhibits innovation capability. A good strategy on knowledge management should be in place to ensure organizational innovation capability can be promoted. A precise knowledge management strategy should provide a clear communicable plan about the knowledge management process in the organization by indicating where we are now, where we want to go, and how to get there. Top management support is paramount in making this a success. Future research could focus on delineating and decomposing the knowledge management capability and organizational culture and examine how possible of each dimension of the variable could influence organization innovation capability.

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Terry Kim, T., Lee, G., Paek, S., & Lee, S. (2013). Social capital, knowledge sharing, and organizational performance: What structural relationship do they have in hotels? International Journal of Contemporary Hospitality Management, 25(5), 683–704. doi:10.1108/IJCHM-Jan-2012-0010 Terziovski, M. (2010). Research notes and commentaries innovation practice and its performance implications in small and medium enterprises (SMEs) in the manufacturing sector: A resource-based view. Strategic Management Journal, 31(8), 892–902. doi:10.1002mj.841 Uzkurt, C., Kumar, R., Kimzan, H. S., & Eminoǧlu, G. (2013). Role of innovation in the relationship between organizational culture and firm performance: A study of the banking sector in Turkey. European Journal of Innovation Management, 16(1), 92–117. doi:10.1108/14601061311292878 Vaidya, S., Ambad, P., & Bhosle, S. (2018). Industry 4.0 - A Glimpse. Procedia Manufacturing, 20, 233–238. doi:10.1016/j.promfg.2018.02.034 Wallach, E. J. (1983). Individuals and organizations: The cultural match. Training and Development Journal, 37(2), 28–35. Wang, D., & Chen, S. (2013). Does intellectual capital matter? High-performance work systems and bilateral innovative capabilities. International Journal of Manpower, 34(8), 861–879. doi:10.1108/ IJM-07-2013-0167 Wang, S., Guidice, R. M., Tansky, J. W., & Wang, Z.-M. (2010). When R&D spending is not enough: The critical role of culture when you want to innovate. Human Resource Management, 49(4), 767–792. doi:10.1002/hrm.20365 Wang, Y., Bhanugopan, R., & Lockhart, P. (2015). Examining the quantitative determinants of organizational performance: Evidence from China. Measuring Business Excellence, 19(2), 23–41. doi:10.1108/ MBE-05-2014-0014 World Energy Council. (2018). World energy issues monitor 2018: Perspectives on the grand energy transition. Retrieved from http://www.im.worldenergy.org/ Wu, L. F., Huang, I. C., Huang, W. C., & Du, P. L. (2019). Aligning organizational culture and operations strategy to improve innovation outcomes: An integrated perspective in organizational management. Journal of Organizational Change Management, 32(2), 224–250. doi:10.1108/JOCM-03-2018-0073 Xu, S. (2015). A study on knowledge management capabilities towards new product innovation type and development performance of Chinese businesses. Acta Oeconomica, 65(s2), 145–157. doi:10.1556/032.65.2015.S2.11 Yousif Al-Hakim, L. A., & Hassan, S. (2013). Knowledge management strategies, innovation, and organizational performance: An empirical study of the Iraqi MTS. Journal of Advances in Management Research, 10(1), 58–71. doi:10.1108/09727981311327767

This research was previously published in Challenges and Opportunities for SMEs in Industry 4.0; pages 79-103, copyright year 2020 by Business Science Reference (an imprint of IGI Global).

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Integration of Industry 4.0 for Advanced Construction Project Management Nataša Šuman https://orcid.org/0000-0003-0470-7700 Faculty of Civil Engineering, Transportation Engineering, and Architecture, University of Maribor, Slovenia Zoran Pučko https://orcid.org/0000-0002-5472-953X Faculty of Civil Engineering, Transportation Engineering, and Architecture, University of Maribor, Slovenia

ABSTRACT The construction industry is facing the increasing process of integration of Industry 4.0 in all phases of the construction project lifecycle. Its exponential growth has been detected in research efforts focused on the usage of the building information modeling (BIM) as one of the most breakthrough innovative approaches in the construction (AEC) industry. BIM brings many advantages as well as changes in the existing construction practice, which allows for adjustment of processes in the most automated possible way. The goal in the design phase is to create a comprehensive BIM model that combines the data of all project participants and represents a digital model of a future building. In the construction phase, the monitoring and controlling the work progress is one of the most important and difficult tasks, and it is today still mostly done manually. Currently, more research and actual implementations are oriented towards the introduction of the automated construction progress monitoring (ACPMon). All of this is the basis for advanced construction project management (ACPMan).

DOI: 10.4018/978-1-7998-8548-1.ch065

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 Integration of Industry 4.0 for Advanced Construction Project Management

INTRODUCTION Integration of the Industry 4.0 is, as in many other industrial sectors, also important for the construction industry. Actually, exponential growth has been detected in efforts focused on the usage of the Building Information Modeling (BIM), which can significantly contribute to all phases of the construction project lifecycle (Borrmann, König, Koch, & Beetz, 2018; Eastman, 2011). First, it allows digitalization of the construction project resulting in adjustment of processes in the most automated possible way. Thus, the implementation of BIM is important already in the early design phase for reaching the highest possible level of digitalization, especially in Construction Project Management (CPMan). The first step is to create 3D BIM model in terms of 3D geometry, such as architectural, structural and mechanical, electrical and plumbing (MEP). The design phase introduces modern BIM approach techniques, which include the possibility to integrate data about the necessary time and cost for the construction of the building. 3D BIM, upgraded with the construction schedule plan, provides the 4D BIM, while the upgrade with estimated construction costs results in the 5D BIM model. The created 3D, 4D and 5D BIM models include all relevant information for the successful project implementation. Anyway, the goal in the design phase is to create a comprehensive BIM model that combines the data of all project participants and represents a digital or virtual model of a future building. Furthermore, the BIM approach provides knowledge sharing and interoperability between project participants; therefore, it brings together different expertise and achieves the optimal designs of BIM models. The BIM approach achieves the bidirectional connected information provided in one place, which means that in the case of a modification of the model elements (e.g. geometry changes), the information related to these elements will be changed accordingly. In this way, even higher level of quality information and better accessibility to every participant from the construction projects is assured in all phases. Integration of the Industry 4.0 with advanced modern techniques combined by the BIM approach represents Advanced Construction Project Management (ACPMan), which ensures the highest possible degree of harmonization of the three factors of project effectiveness (quality, cost and time). In the construction phase, during construction works, various causes lead to frequent discrepancies between planned and actual performance. Therefore, monitoring and controlling the progress of works are very significant factors and also among the most important and difficult tasks. These include the measurement through inspections on the construction site and the comparison with the project plan. However, the progress monitoring is carried out mostly manually as a visual observation, which is time-consuming, error-prone, and infrequent. The quality of data highly depends on the surveyor’s experience and the quality of the measurements. Therefore, more research and actual implementations are oriented towards the introduction of Automated Construction Progress Monitoring (ACPMon) using BIM technology. Such automated monitoring of the work progress has not yet reached the desired level of development, so the researchers strive to develop the method which would enable continuous construction monitoring in real time without additional preparatory woks and in a complete automatic way. The chapter initially presents a literature overview and current situation in the integration of Industry 4.0 for the construction project management. Then it leads the reader step by step into the ACPMan approach, which includes a combination of existing methodological approaches such as CPMan, BIM and ACPMon, the last two representing the technologies or methodologies of the Industry 4.0. In terms of the ACPMan approach, various software is used for building designing, time scheduling and cost estimation in the design phase and construction progress monitoring in the construction phase, which also requires additional and specialized skills. BIM approach as a process, known as a process model, 1278

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leads into digital results in the form of 3D, 4D and 5D BIM models, known as object models. The chapter concludes with a demonstration of the use of the described approach on a practical example of the pre-school building in the city of Maribor.

CONSTRUCTION PROJECT MANAGEMENT (CPMan) Project management has a long history judging from several examples of historical colossal projects that were successfully completed. For instance, Pyramids of Giza, Great Wall of China, and Coliseum are all good illustrations of such projects. The rules of project management were introduced in building construction in the early fifties of the 20th century for large defense projects (Forcada, 2005). An important step toward systematic project management was made by Henry Gantt, one of the forefathers of project management; namely, in 1917 he developed scheduling diagram, best-known as the Gantt chart. At that time, it was a radical idea and a groundbreaking innovation of worldwide importance. One of its first implementations was the Hoover Dam project, started in 1931. Gantt charts are today still in use as an important tool for project managers. Later, some significant planning techniques have been developed, such as: The Critical Path Method (CPM) in 1957, The Program Evaluation Review Technique (PERT) in 1958, the Work Breakdown Structure (WBS) in 1962, etc. An important milestone is also Project Management Institute (PMI), founded in 1969. PMI published in 1987 A Guide to the Project Management Body of Knowledge (Project Management Institute Inc, 2000), which has become a bible for project management. Furthermore, PMBOK Guide and other literature provide definitions and details about the project management for construction projects. Results of construction projects are considered as individual products, which are not comparable to mass production (automobiles, computers, etc.). Therefore, each building must be planned individually, and construction projects are divided into several phases, together forming a life cycle where the process represents the project (process model) and the result is the building (object model). The successful implementation of the construction project is determined by the harmony of three factors: quality, cost and time (Gould & Joyce, 2011). However, the constant technological development, which today represents the fourth industrial revolution, in short, Industry 4.0, has an important impact on the management of construction projects, since they are becoming increasingly complex and require modernization of project management.

BUILDING INFORMATION MODELING (BIM) Building Information Modeling (BIM) is a modern, digitized approach for construction projects, and is increasingly gaining ground in the AEC industry due to its many advantages, compared to the traditional approach. BIM fully considers construction projects throughout their life cycle, where all pieces of information from different expert treatments are collected in one place, e.g. in the BIM model, they are updated and accessible to all project participants. Thus, the BIM approach enables: • • • •

More effective collaboration of all participants. Modeling in 3D environment instead of 2D drawing. High level of project compliance. Reduction of errors in the design of the building. 1279

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• • • • • • •

Identification of conflicts. Efficient production and analysis of variant solutions. Visualization and simulation. Accuracy and coherence of information. Upgrading the 3D BIM model into 4D, 5D, 6D, etc. The production of harmonized sub-modules of architecture, constructions, installations, terrains, etc. Automatic updating of related information in the case of changes.

All this is explicitly stated in the basic literature (Borrmann et al., 2018; Eastman, 2011; Underwood & Isikdag, 2010) and many other sources, where the exponential growth of the interest in using BIM approach has been detected (Yalcinkaya & Singh, 2015) in academic, public and business sectors. Most of them describe the implementation and the adoption of BIM, with an emphasis on geometric design, i.e. 3D BIM modeling of construction objects. However, with the development of software and the increasing demand for computerization of project management, the interest of using specialized software for 4D and 5D BIM modeling is also detected (AIA & Rundall, 2006; Brisk Scott, 2007; Muhič, 2008; Rundell & Stowe, 2007; Tulke & Hanff, 2007). In practice, BIM is mainly implemented in two ways: a) top-down, where the government determines the use of the BIM-approach (mostly in public buildings) by the law; and b) bottom-up, where the BIMinitiative is given by the business (individual companies, investors, etc.). BIM software is primarily used in the early phases of construction projects, i.e. pre-design, design development and preparation for construction, and indicates a significant progress in the optimal planning of construction projects (Pučko, Šuman, & Klanšek, 2015). Both, theoretically and practically, the BIM approach in managing construction projects undoubtedly brings many advantages, particularly the cost reduction, control over the entire life cycle of a building and a significant reduction in the required time for planning and implementation (Bryde, Broquetas, & Volm, 2013). However, authors point out not only the problem of implementation and the use of BIM software in practice but also the need for additional research and comprehensive analysis of the advantages and disadvantages of the BIM approach in terms of costs, benefits, education and use. A comparison between the traditional design of construction objects and modeling using the BIM approach has shown that the latter has positive effects, which are shown successively in individual phases. Moreover, a number of simulations, variant analysis, clash detections, logistic conflicts, virtual construction animations etc. can be performed in the design phase without major costs and additional time. In this way, errors and conflicts can be avoided during the construction phase. The result is in an optimized comprehensive BIM model. The BIM approach (blue line in Figure 1), compared to the traditional approach (black line in Figure 1), demands a little more effort in the design phase, but results in lower level of errors, changes and conflicts during the construction phase, and could lead to significantly lower costs if a design is changed (green line in Figure 1). This effect is known as the Macleamy Curve (Ilozor & Kelly, 2012) as shown in Figure 1. In general, the Level of BIM Maturity (BIMTalk, 2013) and Level of Development (LOD) (BIMForum, 2015) are important for BIM approach. When BIM is implemented as a communication and collaborative tool, it is particularly important for the project participants to know in advance specification and definition of BIM-levels and LOD in order to select and use appropriate tools and achieve the requirements specified in the contract. This ensures the satisfactory precision of a design and provides the investor 1280

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with information which helps them to decide on the development of the project. Improvement of the BIM model within LOD level and other detailed information increases with the development of project documentation and project in general. Hereby, each part of the BIM model is produced at different speed and largely depends on the level of involvement of different designers, suppliers and contractors and their BIM skills. The following LOD are known: LOD 100, LOD 200, LOD 300, LOD 350, LOD 400 and LOD 500 (BIMForum, 2015) (see Figure 2), and Level of BIM Maturity form BIM level 0 to 3 (BIMTalk, 2013) (see Figure 3). Figure 1. The Macleamy Curve – comparison of efforts by traditional design vs. BIM approach (Ilozor & Kelly, 2012)

BIM approach also provides different dimensions of BIM models, where information is upgraded with regard to the methodological expert examination of the construction project (BibLus, 2018). The following dimensions of BIM models are known (see Figure 4): • • • • •

3D: Three-dimensional rendering of the model. 4D: Time analysis. 5D: Cost analysis. 6D: Sustainability assessment. 7D: Operation of the building.

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Figure 2. Level of Development (LOD) of BIM elements in the BIM model (BIMForum, 2015)

Figure 3. Level of BIM maturity (BIMTalk, 2013)

The most famous modeling tools, sometime referred to as BIM tools, which are used for 3D BIM modeling are: ArchiCAD (graphisoft, 2018), Revit (Autodesk, 2019), Tekla Structures (“Structural BIM Software | Tekla,” n.d.), Allpan (“Allplan - BIM - CAD - BCM - FM Software,” n.d.), SketchUp (“3D Design Software | 3D Modeling on the Web | SketchUp,” n.d.). Each uses its own format. In addition, an independent, neutral format known as Industry Foundation Classes (IFC) is commonly utilized. It is a collaborative format for BIM based projects, developed by buildingSMART, that enables interoperability between software platforms (buildingSMART, 2018). Today, several BIM tools for construction project management are on the market for upgrading the basic 3D BIM model to the 4D and 5D BIM models. Vico Office (Trimble, 2016), iTWO (“iTWO 4.0 | 5D BIM Cloud-based Construction Software,”

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n.d.), BEXEL Manager (“Bexel Consulting - Your Partner For Successful BIM Future,” n.d.), SYNCHRO Pro (“Construction project management software │4D Construction Scheduling – 4D BIM FOR CONSTRUCTION,” n.d.), STR Vision CPM (“BIM 3D, 4D and 5D modeling software | STR Vision | STRVision,” n.d.), Navisworks Manage (“Navisworks | 3D Model Review Software | BIM Coordination | Autodesk,” n.d.) and others are mostly used as BIM Project Management Software providing 4D BIM or/and 5D BIM modeling. The purposes and characteristics of 3D, 4D and 5D BIM modeling are presented in detail in the following chapters. Figure 4. The dimensions of BIM models (BibLus, 2018)

3D BIM Modeling A significant advantage of the BIM approach is that the design of a building is not carried out by drawing CAD plans, because the 3D building modeling is performed with elements as graphical and non-graphical information. As a result, the appropriate expert treatment in the modeling of the 3D model ensures that each building element has relevant information about the type of element, the selected materials, the technical characteristics (e.g. fire resistance rating, thermal transmittance, sound transmission class) and the like. These are the inputs for the various further professional examinations, where parametric modeling and high LOD are important. The importance of parametric modeling has also been recognized by many manufacturers of building components and materials, who intensively create the elements for

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BIM libraries to be used with various modeling tools; thereby a competitive advantage is provided and the 3D BIM model achieves a high level of accuracy and details. More specifications in the 3D BIM model already from the early stage result in better detailed treatment in later stages. Taking into account that many professions are involved in the modelling process in succession or in some cases in parallel, it is very important to have access to the latest state of the model. This enables effective upgrade to a comprehensive BIM model. Furthermore, all this enables the main benefits of the BIM approach, which are a better cooperation of all project participants, who do not need to produce their individual 2D plans, reports, tables, etc., but they together create a 3D BIM model. In this way, all information from different expert treatments is collected in one place, interconnected and accessible to all participants. Besides, experts interact with each other in the way that everyone has access to the last version of the BIM model, which excludes the possibility for an individual expert to use a previous state of BIM model. Such individual treatment is a common problem in the traditional approach with the potential mismatch in the documentation. The BIM approach allows experts to work together in a way that they complement the model with information in a central BIM model. This BIM model is accessible via the server as a closed system of the same company. However, the access for external experts to the company server can also be provided with authorization or as a modern solution using a cloud server. The employment of the cloud server is also user-friendly for the investor, who can inspect the state of model at any time with the simple access through the online link where the appropriate viewer is available, i.e. specialized software does not need to be installed. In this way, the interaction of cooperation of all participants is assured. Overall, the BIM approach represents a complex treatment of construction projects, which includes both the object and process model of the building and requires the appropriate qualification of experts and at the same time good knowledge of modern BIM tools.

4D BIM Modeling The 4D BIM model can be created by integrating the 3D BIM model and the schedule plan (Braun, Tuttas, & Borrmann, 2015; Golparvar-Fard et al., 2011). In addition to the geometric information, 4D BIM model includes time information (3D + Schedule Plan). The main result of the 4D BIM model is the project schedule, accompanying resource plans and the ability to display animation of construction works in chronological order. In the last period, BIM tools have been successfully combined with scheduling and other software (AIA & Rundall, 2006; Brisk Scott, 2007; Muhič, 2008; Rundell & Stowe, 2007; Tulke & Hanff, 2007). However, the combinations between different software were oftentimes unique and hardly generally applicable. The researchers also state that the converter must be applied for data synchronization between BIM and scheduling software. Nevertheless, the increasingly massive use of BIM approach, the development of modelling tools and the tendency for additional information, important for the construction project management, resulted in the development of sophisticated BIM Project Management Software, to upgrade the basic 3D BIM model to 4D and 5D BIM models. 4D BIM model, also referred to as the 4D As-planned BIM, is usually created in the last step in the design phase (Braun et al., 2015) and represents the optimized and comprehensive BIM model. It also allows building simulation to give the insight for the correct time of execution of the activities, and information about the needed resources, which is particularly important for the contractor. In the construction phase, the 4D BIM model is referred to as the 4D As-built BIM model and is created during the 1284

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progress monitoring activity on the construction site and provides the actual situation in a certain period of time. At the end of the construction, it represents the final 4D As-built BIM model with the highest levels of development, the LOD 500. It includes all planed information from the As-planned 4D BIM model upgraded with information regarding the actual dates of execution. In the modelling process, the geometry is assured to be linked with time information, and in cases of geometry modification, it leads to automatic update of calculated time information.

5D BIM Modeling The construction costs are of an utmost importance for the management of construction projects, therefore, the integration of the 3D BIM model and cost plan leads into the creation of the comprehensive 5D BIM model (Bryde et al., 2013; McCuen & International, 2008; Pučko et al., 2015; Staub-French, Khanzode, Analyst, & Candidate, 2007).With other words, the 5D BIM model includes cost information (3D + Cost Estimation) for the construction of the building. In 5D BIM modeling, individual information of the 5D BIM model is associated with the individual elements of the 3D BIM model; and in the case of modification of the geometry, the information about costs is automatically updated. In the recent past, BIM programs and cost estimation tools have yielded acceptable results, but they were mostly matchless and difficult for wider use. The main limitation was the lack of appropriate costing methods that would be transparent enough and would include all required cost components (i.e. labor, material, equipment, energy, consumables, overhead, profit, etc.) for the detailed cost calculation in BIM tools. Nowadays, the sophisticated BIM Project Management Software enables more precise inclusion of all costs related to the construction of the building, while estimating the cost mostly depends on the user and their professional competence (Cheung, Rihan, Tah, Duce, & Kurul, 2012; Haapio, 2012; Mitchell, 2012; Shen & Issa, 2010). In the design phase, the 5D As-planned BIM model is created during the project planning. 5D modeling includes the collected information about the cost of consuming material and work per unit of measure, and calculations of a unit price can be performed, e.g. individual works, building elements, activities, inventory items, etc. At the time of construction, the occurrence of costs can be followed and the 5D As-built BIM model is created gradually.

AUTOMATED CONSTRUCTION PROGRESS MONITORING (ACPMon) During construction work on the construction site, various causes often lead to discrepancies between planned and actual performance. This is the main reason for the implementation of monitoring and controlling activities of the work progress. Monitoring activities include the measurement through the inspections on the site and the comparison with the project plan, while the quality of the progress data highly depends on the surveyor’s experience and the quality of the measurements. Currently, the construction progress monitoring (CPMon) is carried out mainly as a visual observation, and all collected information about the progress of works is then recorded in separate documentation, which does not provide strong support for updating the 2D CAD plans. Many important information for the correct decision-making is thus lost. The situation is not much better even with the BIM approach if the collected information is entered into the basic BIM model manually as additional information, and does not provide updates. Therefore, more research and actual implementations are now oriented towards the introduction of Automated Construction Progress Monitoring (ACPMon) using BIM approach with elements from the 1285

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Industry 4.0. In the construction time, the created As-planned BIM models provide the basis for implementing the advanced monitoring activity for controlling the progress of works. Today, various remote sensing technologies are used to help collect data about the actual situation on the construction site. The most common ones are photogrammetry, videogrammetry, and laser scanning, which are more or less automated. Development of new tools and methodologies allows for automated progress monitoring, in terms of data acquisition, information retrieval, progress estimation and visualization of the results (Akinci, Kiziltas, Ergen, Karaesmen, & Keceli, 2006; Bhatla, Choe, Fierro, & Leite, 2012; Fathi & Brilakis, 2013). Typically, the As-built 4D BIM model is created in the observed time with the Scan-vsBIM approach, based on the 3D BIM model, and the data are captured in the form of point clouds. The comparison between the As-planned and the As-built models shows differences and gives a deviation of the actual performance from the planned one. In this way, the monitoring process of work progress is done on a higher level of automation. In comparison with manual monitoring, such monitoring has higher accuracy, frequency and efficiency. The results of ACPMon are the basis for decision-making, when an action is required, in order to deliver successful projects on time and on budget. A detailed insight into ACPMon is provided in references (Alizadehsalehi & Yitmen, 2018; Bosché, Ahmed, Turkan, Haas, & Haas, 2015; Kopsida, Brilakis, & Vela, 2015; Omar & Nehdi, 2016; Pătrăucean et al., 2015) where various approaches to automation of the construction monitoring process are presented and evaluated. The source (Pătrăucean et al., 2015) deals with the modeling of the As-built BIM model based on a point cloud, for both cases where the As-planned BIM model exists or not. The study (Kopsida et al., 2015) describes and outlines the advantages and disadvantages of methods with capture techniques of the actual state on the construction site, for photogrammetry, videogrammetry and laser scanning, it also provides the development of a method for processing the captured data and visualization. The study (Omar & Nehdi, 2016) explicitly provides categorization of monitoring methods and relevant data capture technologies. In the study (Pučko, Šuman, & Rebolj, 2018), the method of automated continuous construction progress monitoring is developed by using multiple workplace real time 3D scans, and hereby the proposed method overcomes many deficiencies of known existing methods, mainly due to the method of continuous point cloud acquisition. Unfortunately, different methods for automated construction progress monitoring has not yet reached the desired level of development, so the researchers strive to develop the method which would enable continuous construction progress monitoring in the real time without additional preparatory works and in a complete automatic way.

ADVANCED CONSTRUCTION PROJECT MANAGEMENT (ACPMan) The BIM approach forms the core for the construction Industry 4.0 in terms of digitization of the construction industry. Construction Industry 4.0 includes many aspects with advanced processes and technologies. The increasing production is based on prefabrication with more assembly and less traditional construction, the automation of construction processes, the 3D printing, the robotization of repetitive and dangerous work, the use of machines and equipment with autonomous operation sensors, data collection, provision of better safety on construction sites. All these provide accurate information in real time with all linked data in a central database, which leads to smoother, lower error and faster construction. To achieve successful implementation of construction projects, the BIM approach has proven to be very useful management support, since the building is first “built” digitally, each professional treatment 1286

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is taken into account and all information is interconnected in a comprehensive BIM model. The information collected in one place provides simultaneous updating in the case of changes as well as continuous accessibility to the actual state of the model for all project participants. The BIM approach enables advanced modeling, first in the design phase as the implementation of numerous analyses and simulations with the aim of upgrading a comprehensive BIM model. Further, comprehensive BIM model serves in the construction phase for the construction progress monitoring, where the As-planned and the As-built models are compared. As a result, a developing trend lies in automation of monitoring process with the use of advanced remote sensing technologies. Advanced technologies represent the integration of Industry 4.0, which can be called the Advanced Construction Project Management (ACPMan). The authors of the chapter see the big picture in the Advanced Construction Project Management (ACPMan), which can be mathematically described as the sum of partial elements of the Industry 3.0 and 4.0, which are CPMan, BIM and ACPMon, as shown in Equation 1. ACPMan  CPMan  BIM  ACPMon

(1)

Understanding of the big picture is also precisely graphically represented in Figure 5. The Figure presents individual periods of industrial revolutions as a funnel of knowledge flow over the time. The individual knowledge was deposited as sedimentation and formed the basis for new insights. The ACPMan approach has its first roots in Industry 2.0, namely CPMan. The second basis was created with the beginning of the development of the BIM approach in the late period of Industry 2.0 and with the rapid progress in the period of Industry 3.0. The latter formed the grounds for ACPMon approach, which is the third consecutive basis of the ACPMan approach. Actually, the development continues and the first beginnings for Industry 5.0 exist already. We assume that this will scale up and accelerate the flow of knowledge and enable ACPMan to run in a fully automated manner. Detailed descriptions of the ACPMan approach are given below separately for the design and the construction phase.

Description of the ACPMan Process in the Design Phase Figure 6 shows the process of advanced planning of the building during preparation for construction, that is from the pre-design phase, provision of the design documentation, implementation of the bidding process to the beginning of the construction works. After defining the project goals, the process of advanced planning begins with the setting requirements for building. The investor explains the desired level of quality of the building so that the basic idea of the design is conceived by the design professionals (architects and engineers). In the first step, the basic 3D BIM model is modeled in appropriate modeling tools. 3D BIM models are created by all required design professionals, while the LOD level increases with the advancement of the project and technical documentation. The basic model is then upgraded into 4D and 5D BIM models. This is done by the cost and time analysis in corresponding programs. The cost analysis, in terms of the whole life cycle of the building, usually covers the life cycle cost (LCC) including costs of planning, design, data acquisition (including pre-construction and construction), operation, maintenance and disposal, less any residual value (ISO15686-5, 2017). The BIM approach allows cost information to be directly and bidirectional con-

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Figure 5. The big picture for Advanced Construction Project Management (ACPMan).

nected with model elements. Therefore, when the geometry of the 3D BIM model changes, the costs associated with the element can be updated automatically. In parallel, the time analysis is carried out, where the elements from the 3D BIM model are directly related to the activities in the schedule plan. The time analysis takes into account activities, links between the activities and the assigned resources. In determining the duration of each activity, the quantity and norm of work per unit of measure play an important role. The quantities are automatically summarized from the 3D BIM model geometry. This is an essential advantage compared to the traditional approach, where manually calculated quantities lead to many options for errors. Similar to the cost analysis, the change of 3D BIM model geometry automatically modifies the information related to activities which results in the updated time needed for the implementation. The automation of updating information related to the model changes is provided for BIM models where the BIM and LOD levels are appropriate (e.g. BIM level 2 and higher and LOD 350 and above) and the interoperability is on a high-level. The results of both cost and time analyses bring a comprehensive overview of the project including geometry and information related to the cost and time plan. Results of ACPMan process during preparation for construction are the 3D, 4D and 5D As-planned BIM models, containing all relevant information for the implementation. From the perspective of ACPMan, As-planned BIM models represent the highest level of collected and interconnected information in one place for the successful implementation of the project, where quality, cost and time are optimally coordinated.

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Figure 6. The process of advanced planning of the building during the preparation for construction

Description of the ACPMan Process in the Construction Phase Figure 7 shows the process of advanced monitoring of building construction, up to the time of handover of the building in use. As-planned BIM models provide the input data, while the process of ACPMon is carried out on the site by collecting information about the actual situation, e.g. actual performance (As-built). Nowadays, remote-sensing technologies are used to acquire the required data, which enable automatic data acquisition in the form of point clouds. In doing so, first, the performed elements in the point cloud are identified, based on the 3D As-planned BIM model and the 4D As-planned BIM model in the observed time. ACPMon process is performed by using the appropriate Progress Monitoring Software and results in the production of As-built models. The comparison of As-planned and As-built models can identify discrepancies, which represent deviations from the planned implementation. Furthermore, remote-sensing technologies are tools and technologies that enable automated construction progress monitoring, especially with regard to automatic data capture, information transfer, evaluation of progress and visualization of results (Akinci et al., 2006; Bhatla et al., 2012; Fathi & Brilakis, 2013; Pučko et al., 2018). Integrating BIM models and technologies of Industry 4.0 enables the automation of the progress monitoring in real time. Thus, at specified time intervals, individual As-built BIM models can be created, representing important part of the ACPMan process during construction, and after the completion of the works, the final As-built BIM-models are obtained. Using the described ACPMan approach, construction monitoring is more effective, more transparent, and more accurate compared to the traditional way.

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Figure 7. The process of advanced monitoring of building construction in the construction phase

CASE STUDY The usage of state-of-the-art approaches for planning and monitoring of building construction with focus on the BIM approach for the purposes of the Advanced Construction Project Management (ACPMan) is discussed on the case study of the new pre-school building in the city of Maribor. First, the basic 3D BIM was implemented in the ArchiCad modelling tool. Second, the upgrade of the basic model followed in the modern Project Management Software Vico Office, namely the modeling of 5D BIM and 4D BIM models. Furthermore, the operational monitoring of the work progress on the construction site was carried out in two ways with photogrammetry and 3D scanning. A detailed description of the progress of the advanced planning and monitoring of building construction is given below.

Description of the Building The pre-school building is designed as “L” form with dimensions 11.10m×17.10m and 21.10m×9.90m. The building height is 9.47m (upper part) and 9.17m (lower part), it has two floors, the ground and the first floor. The gross area of the building is 797.40m2. Three playrooms and one space for additional activities are placed in the building for 66 children (Projekta inženiring Ptuj d.o.o, 2011). The main entrance is located on the east side of the building, the access to the wardrobe and the kitchen is on the south. The playrooms are on the south side of the building and have the access to the terrace. The geolocation of the pre-school building is in the local community Pekre, near the city of Maribor. The structural walls system is made of brick. Slabs and the foundation are made from reinforced concrete. Floor constructions against terrain are carried out on a compressed filled-up ground with waterproofing, 10cm of thermal insulation, concrete screed and finishing layer. The exterior walls are made of 30cm bricks. The internal bearing walls are brick (20cm thick) and other internal walls are dry-mounting versions. In addition, reinforced concrete bonds are made on pre-designed places, gener-

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ally every 3 meters. A reinforced concrete slab is placed above the ground floor and the first floor with appropriate finishing layers. The roof is flat, its waterproof PVC foil is welded and secured to a layer of felt. Further, steel fire stairs are on the west side. The façade of the building is a thermal insulating contact façade. All windows are wooden with thermal insulating glass. The entrance doors are made of aluminum and the interior doors are wooden, partly glazed with safety glass. The inner wall surfaces are plastered and grinded, tinted and dyed. The floor in the playrooms is finished parquet with floor heating; ceramics, floor in epoxy mortar and rubber can be found in other spaces. All rooms are heated with a floor heating system and cooled with air conditioning. Ventilation of the building is carried out mechanically with a central heat-recovery ventilation system.

Case study of the ACPMan Process in the Design Phase The basic 3D BIM model of the new pre-school building is created in the ArchiCad software. A modern BIM approach allows for parametric modeling, which in addition to geometric information and visualization provides a collection of information of all used materials for element components with their properties. So, all information is collected in one place and represented in the comprehensive 3D BIM model, as shown in Figure 8. Figure 8. A comprehensive 3D BIM model of the new pre-school building

The basic model is then upgraded into 4D and 5D BIM models during the cost and time analysis. In the case study, the cost analysis considers investment costs, representing the construction costs for the activities for construction of the building. These costs are analyzed with cost calculations for performing construction and craft works (the case study does not include the cost of installation work). However, a comprehensive cost analysis may also include other life cycle costs (LCC) that occur in the pre-construction and operational phases of the building. Since the purpose of the chapter is in the description

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of the ACPMan approach on a practical case study, involving the creation of the 5D BIM model, it is not necessary to include the full range of costs. Further, our case study in the time analysis deals with the activities for construction of the building. The activities are structured as composite information about required material, machinery, equipment and labor, shortly referred to as resources. Based on the assigned resources, the duration of each activity is calculated, regarding the quantity of work and the corresponding norms, taking into account the given implementation technology. The implementation technology itself dictates the sequence of performance, which is determined by the links between the activities. The duration of the entire project is thus defined on a basis of all interconnected activities. Since the purpose of the chapter is description of the ACPMan on a practical case study for creation of the 4D BIM model, only a rough description of activities is given, and does not provide details about the applied scheduling techniques, specificity of a selected technique, etc. The advanced use of the BIM approach includes the modern Project Management Software Vico Office software (Trimble, 2016), which enables the modeling of 4D BIM and 5D BIM models. The created basic 3D BIM model is first imported into Vico Office in the module Model Register. Secondly, all individual elements are grouped according to the element type in the Takeoff Manager module, as shown in Figure 9. Individual elements can be merged into groups, mainly due to the implementation technology and later realistic processing in the 3D BIM model. It makes sense to rename these elements (column Name in Figure 9) and assign them appropriate codes (column Code in Figure 9). The following groups of elements, representing sets of structural elements of the building, were defined: 001-Foundations, 002-External walls, 003-Internal walls, 004-Columns, 005-Slabs, 006-Stairs, 007-Roof, 008-Metal borders, 009-Stair landing, 010-External windows, 011-Internal windows, 012-External doors, 013-Internal doors, 014-Fences, 015-Steel construction and 016-Roof windows. Figure 9. Defined groups of elements in the Takeoff manager module

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As a result, groups of elements defined in the 3D BIM model are directly linked to the time-scheduling and cost estimating information. They are object models of structural elements which have geometric representation in the 3D BIM model. However, process models for non-structural elements can also be included, sometimes also referred to as non-geometric elements, such as energy costs which do not have a geometric image in the 3D BIM. Information about the geometric characteristics are generated automatically for each selected groups of elements. Figure 10 shows a group of elements of the Internal walls with precise information on individual quantities. The total amount for their implementation is 175.22m3, while the quantity in the ground floor is 92.35m3 and 82.87m3 in the first floor (marked with the red rectangles in Figure 10). Figure 10. Automatically generated geometric characteristics of internal walls with quantities in the Takeoff manager module

Cost Analysis – 5D As-planned BIM Model The module Cost Planner provides precise determination and calculation of the construction costs for each selected group of elements. Therefore, it is particularly suitable for contractors during the bidding process. The calculations are performed on unit price estimation based on pre-defined construction technology and quantities taken from the geometry of the group of elements. They utilize the norms for construction (masonry, reinforced concrete works, formworks, plaster) and craft works (façade and painting works) (Norms for construction works, 2005; “Okvirni opaž Frami Xlife - Doka,” n.d.; “Porotherm proizvodi,” n.d.) and the average price of the labor and the market price of materials per unit of measure. The analysis is conducted as values without VAT. In this way, the 5D BIM model is modeled. Figure 11 deals with the 5D BIM model for the costs calculation of individual group of elements, in total EUR 782,991.40.

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Figure 11. Presentation of the 5D BIM model as costs calculation for execution of individual group of elements in the cost planner module

Figure 12 shows the cost structure per unit of measure for construction of 20cm thick brick walls, i.e. the group Internal walls. The walls are built with the Porotherm S P + E insulation modular brick block, which are machine plastered with mortar on both sides of the walls. The calculation takes into account the manufacturer’s norms (“Porotherm proizvodi,” n.d.), the norms for transport (Norms for transports, 2005) and the price of mortar according to the price list (Indeksi za obračun razlike v ceni gradbenih Figure 12. Structure of the cost calculation for the group internal walls with calculated values for appropriate measure in the cost planner module.

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storitev (in Slovenian), 2018). The unit cost construction of brick walls is EUR 134.68 per m3 and for the total quantity (175.22 m3), total cost is EUR 23,598.91. The cost calculation is performed for all groups in the same way as for group Internal walls, which represents the process of modeling the 5D BIM model. As already mentioned, the calculation can also include non-graphic elements. The level of calculation accuracy depends on the accuracy of the initially chosen approach when the requirements for the building are made. The final result of the modeling process in the design phase is the 5D As-planned BIM model, which encompasses all the foreseen construction costs collected in one place.

Time Analysis – 4D As-planned BIM Model The time analysis is performed by activating the Task Manager module, where the activities for the construction of the building can be defined as blocks of structural elements or as technological processes. Our case study uses the pre-generated data structure in the Cost Planner module for designing a 5D BIM model. This saves a lot of time since re-recording of the information about used material, machinery, equipment and work is not necessary and the records are not duplicated. Activities for the pre-school building are listed as technological processes, where six activities are planned for construction works and thirteen activities for craft works, as shown in Figure 13. In addition, Figure 13 provides the usage of the data structure for the Internal walls from the Cost Planner module (yellow lines in the right table), where it was transferred into the Task Manager module (blue lines in the left table). This provides dynamical links between costs and time information, which is related to elements in the 3D BIM model (geometric elements in our case study). Figure 13. Defined activities for the pre-school building in the task manager module (left table) with linked structured data from the cost planner module (right table)

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The time scheduling begins with so prepared data in the Schedule Planner module. The duration of each activity is calculated first, and then the appropriate links between the activities are defined, taking into account the chosen implementation technology. The module Schedule Planner uses advanced scheduling techniques such as Critical Path Method (CPM) and Location Base Scheduling (LBS). The module Schedule Planner can also be used as stand-alone scheduling software for example in construction projects where the 3D model does not exist. Furthermore, the software offers the possibility of exporting and importing data in MS Excel and MS Project format, allowing to switch between these programs. The result of the time scheduling is shown in Figure 14 in the form of Gantt view and in Figure 15 as Flow view. A number of other detailed views of the time scheduling is also possible, for example a structural network diagram, a dynamic accompanying resource plan (a cumulative need for resource in time periods), Bill of Quantities (BoQ), etc. Figure 14. Result of the time scheduling for the case study in the form of Gantt view

In addition, simulations for the construction of a building can be carried out on the basis of the data from the time scheduling and 3D BIM model elements. Such simulations are very important for the preliminary detection of performance conflicts, which can be avoided during construction. Simulations also allow a clear display of the sequence of activity implementations and the demonstration of the used technologies. Figure 16 shows some randomly selected simulation views for construction of the pre-school building. The final result of the modeling process in the design phase is the 4D As-planned BIM model, which contains all the essential elements for timely execution, since the construction processes are optimized and collected in the form of information in one place. A detailed view of As-planned models can be provided for each group of elements in the Vico Office software by means of the multi-window view of the model, for example: the 3D view (the 3D BIM model), the Task Manager (the 4D BIM model), and the Cost Planner (the 5D BIM model) can

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be displayed simultaneously. Figure 17 shows a multi-window view of the model for Internal walls. The window view above to the right displays the quantities, the resources needed and the duration from the start to the finish dates of the activity. The total duration is 16.4 working days. The window view below shows the structure of costs and value per unit of measure, which amounts for EUR 134.58 per m3 and in total EUR 23,598.91. All this together gives a complete overview about the information for the group of Internal walls. These user-friendly visual views are particularly important for the investor or the owner as well as for the supervisor and the contractor during the construction. In addition, such multi-window view of As-planned BIM models enables the construction site management to have a complete overview in important information and to organize and procure all the necessary resources to the site on time and within the budget. Figure 15. Result of the time scheduling for the case study in the form of Flow view

Figure 16. Randomly selected views from the simulation for construction of the pre-school building

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Figure 17. Multi-window view of As-planned BIM models for the group internal walls

All described and on a practical example demonstrated project management procedures in the design phase essentially contribute to the efficient management of construction projects. The created As-planned 3D, 4D and 5D BIM models with information collected in one place and the interconnection and accessibility of all participants are represented in the context of project management as the Advanced Construction Project Management (ACPMan).

Case Study of the ACPMan Process in the Construction Phase The monitoring and control process is carried out at the site in order to obtain information about the current progress of works. Nowadays, this is still mostly done manually as visual observations and measurements. In the traditional approach for construction monitoring, data are recorded in stand-alone documentation, which is also overlooked many times. The data thus obtained do not serve the original purpose, that is providing the basis to make a decision on the measures necessary for further construction works. The BIM approach has, compared to the traditional approach, an essential advantage because data are registered in one place, which makes the direct comparison between the planned and the actual implementation. During the construction phase, the Vico Office software can be used for monitoring the progress of works. The 4D As-planned BIM model is used for time monitoring, which is updated with the actual dates for starting and finishing of activities and it results in the 4D As-built BIM model. The 5D As-planned BIM model is implemented for costs monitoring, resulting in the 5D As-built BIM model, where information about the actual costs for the construction is recorded. However, a manual data entry of actual information with BIM approach is an important contribution for the process of monitoring construction, compared to the traditional approach, but not the best possible way. Therefore, it is necessary to automate this process by implementing advanced techniques of the Industry 4.0.

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The operative monitoring during the construction of the pre-school building was carried out in two ways. One method used the photogrammetry by collecting the photographs in intervals of 20 min with a fixed camera, while the second one implemented the 3D scanning device by collecting partial point clouds in certain time periods. Both methods represent a modern approach to digitizing and automating the process of construction progress monitoring. The basic aim of such control is establishing the difference between the planned and actual work progress in real time, based on the comparison of the 4D As-built and 4D As-planned BIM models.

Photogrammetry-based Approach Monitoring of the construction of the pre-school building was provided by a fixed camera LTL Acorn 6310, which was placed onto a building in the immediate vicinity of the construction site. The camera captured photos in an interval of 20 minutes and during the construction, all together 5243 photos were captured with information on the precise date and hour. Figure 18 shows some random sequential photos. Figure 18. Selected photos captured by the camera LTL Acorn 6310

An analysis of the work progress was carried out on the basis of photo documentation. This was done manually with review of individual photos and the assessment of the current situation on the construction site. The advantage of such an approach is that this activity does not need direct insight or visiting the construction site. Even the evaluation for the past situation, e.g. already finished works, can be implemented retroactively, when the previous situation with visual observation on the construction site cannot be determined. However, the disadvantage is that only one camera was used, and photos are only taken from one perspective, and therefore the automation of the process is not feasible. In automa-

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tion of the process, photographs are required from several different perspectives, as detailed described in studies (Tuttas, Braun, Borrmann, & Stilla, 2016) and other literature dealing with photogrammetry.

3D Scanning Device-based Approach The 3D scanner Kinect v2 was tested at the construction site, using the newly developed method for automated construction progress monitoring (Pučko et al., 2018). Basically, Kinect v2 is a low-precision 3D scanner that was found to be sufficient for correct identification of the built elements (Rebolj, Pučko, Babič, Bizjak, & Mongus, 2017). The developed method envisages data capture with the help of construction workers because the scanner is installed in workers’ protective helmet. Thus, workers capture all workplaces inside and outside of the building in real time and record partial point clouds, their locations, and time stamps, as shown in Figure 19. Figure 19. Recorded partial point cloud with the 3D scanner Kinect v2

A 4D As-built BIM model is generated based on partial point clouds of appropriate quality (Eickeler & Borrmann, 2019; Rebolj et al., 2017), and is compared with the 4D As-planned BIM model, so the identification of the differences could be visually reported in virtual real time, which enables more efficient project management. Figure 20 shows a deviation from the planned construction implementation where the difference is visually presented as an actual missing door that has not yet been mounted. A detailed description and application of the newly developed method is given in the study (Pučko et al., 2018) and in the thesis (Pučko, 2018).

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Figure 20. Visual reported differences based on the comparison of the 4D As-built and 4D As-planned BIM model

Cost Monitoring - 5D As-built BIM Model The collected data on construction costs incurred at the construction site are the basis for creating the 5D As-built BIM model. The costs of actual implementation are entered in the Cost Planning module, in the same way as creation of a 5D As-planned BIM model, in cases where these costs are different from the planned one. When there is no difference, no re-entry of the same value is needed. Calculated costs from the 5D As-planned BIM model represent target costs and are recorded as a Snapshot variant, while the recorded costs of actual implementation, insofar they are different from the target costs, represent the current calculation and are saved as the Snapshot of the current variant. The latter also represents the 5D As-built BIM model. A comparison between variants can be made in the Cost Explorer module, where

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differences can be detected quickly in color graphical representation. Each color marking bases on Cost Comparison Range Settings. Besides, the comparison can also be made with a report in a tabular form. Figure 21shows a graphical presentation and Figure 22 compares the report between planned and realized costs. It can be seen that the greatest deviation is in the façade work, i.e. by EUR 64,219.63. The difference arises from the rationalization of the project, where the façade solution was changed. The original solution was envisaged as a thermally insulated ventilated suspended façade with façade panels TRESPA METEON with a total value of EUR 103,378.00. Thus, the actual implementation includes a 16 cm thick thermal insulation contact façade DEMIT with a total value of EUR 39,158.37. Rationalization of the façade work thus saved 62.12% of the calculated costs (budget costs). The change of façade solution also reduced the total value of the project from EUR 782,991.40 (5D As-planned BIM) to EUR 718,771.77 (5D As-built BIM model). Figure 21. Graphical view of the compared cost variants with the main deviation in the façade work

Figure 22. Report view of the compared cost variants with the main deviation in the façade work

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Time Monitoring – 4D As-built BIM Model Monitoring over the timing of construction based on the 4D As-planned BIM model and obtained photo documentation. The monitoring approach with the 3D scanner was performed only at certain time intervals, and not throughout the entire construction period. Nevertheless, the obtained results were used in the generation of the As-built 4D BIM model. An overview of the construction monitoring process with the Vico Office software for the Internal walls activity is given below. During the construction of the building, a number of changes were made, later agreed between project participants, but the original project documentation or the 4D As-planned BIM model were not changed. One of the changes that occurred already at the beginning of construction was the basement modification: instead of reinforced concrete strip foundation the reinforced concrete foundation slab was carried out. This led to the earlier actual implementation of the Internal walls activity. The originally planned implementation of the Internal walls was from 7 March 2017 to 17 March 2017 on the ground floor, and on the first floor from 12 May 2017 to 24 May 2017. But actual implementation took place from 3 February 2017 to 16 February 2017 on the ground floor, and from 4 March 2017 to 17 March 2017 on the first floor, which is about one month earlier. Also, the activities of External walls and Internal walls were not actually implemented in a sequential manner (as planned), but in parallel, i.e., in the partially simultaneous manner, with which the contractor accelerated the construction. The actual implementation of the activities was also evident from the photo documentation that was created during the construction. The creation of the As-built 4D BIM model is done within the Schedule Planner software in Control mode view by entering the actual start and finish dates of the activities in the original schedule plan, i.e. in the 4D As-planned BIM model. In this way, the symbol for the actual start and the symbol for the actual finish date appears in the Gantt view, as shown in Figure 23. Figure 23. Gantt view with the display of the planned and actual start and finish dates of the Internal walls (marked with the red rectangle)

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Moreover, the Schedule Planner software enables a matrix display for a clear view of the construction monitoring. When data is updated with the actual implementation dates of activities, the status of each activity is displayed with different colors. A red color represents the activity that has not started yet; yellow the activity in progress, but running late; blue the activity in progress; and green the completed activity. Figure 24 shows a matrix view of all activities with an additional open window providing detailed information on the planned and actual implementation of the Internal walls activity on the ground floor. Figure 24. The matrix view for a clear graphical view of the construction monitoring with an additional open window for the Internal walls activity

SOLUTIONS AND RECOMMENDATIONS The solution provided for the described ACPMan approach includes advanced Industry 4.0 technologies, whose continuous development opens up new possibilities for implementation. The desired level of the ACPMan approach has not yet reached the required level for direct application in practice, since individual processes are still manual. In other words, the existing methods and technologies do not enable continuous construction monitoring in real time and without additional preparatory works and thus a complete automatic monitoring method is not feasible yet. Therefore, it is recommended to monitor the development of the used technologies and the new ones, that would overcome the current problems and shortcomings.

FUTURE RESEARCH DIRECTIONS The development of Industry 4.0 and the advanced processes and technologies used in the ACPMan approach, require due to continuous improvement and new developments further research of the ACPMan

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approach by implementing the state-of-the-art CPMan, BIM, and ACPMon insights. At the current stage of development, CPMan has some lawfulness of a procedure that will not change in the future significantly, since the factors of quality, time and cost are crucial for the success of construction projects. BIM is becoming a standard in the AEC industry and its development pursues the goal of achieving the 3rd level of BIM Maturity, which means full integration with interoperable data. Furthermore, considered ACPMon approaches are experimental, performed only as individual cases, and none of them have been fully implemented in the construction practice yet. Therefore, further research is needed in this area, and the development of remote sensing technologies is expected to have the greatest impact. Consequently, additional development of the ACPMan approach is required, where the biggest challenge is the combination of CPMan, BIM, and ACPMon.

CONCLUSION Industry 4.0, together with existing advanced technologies, has a significant impact on the development of different industries, and a trend of progress has also been detected in the AEC industry. Construction Project Management (CPMan), in its traditional form, has to follow these trends and the need for modernization arose. Thus, many research efforts today are focused on the use of Building Information Modeling (BIM), which is currently considered as one of the most breakthrough innovative approaches. The greatest progress has been made in the design phase, which brings many advantages as well as changes in the existing construction practice. The main alternations and benefits are, in comparison with the traditional approach, in mutual cooperation of all participants who perform modeling of the object and process models with professional treatment, and whose information is harmonized and collected in one place and accessible to all using appropriate technology. In the construction phase, it is essential to monitor the construction and to control the progress of the work in order to implement the project effectively and successfully. The monitoring process is one of the most important and difficult tasks from the perspective of CPMan, but currently it is mostly done manually. However, an opportunity to improve this process provides the modern Automated Construction Progress Monitoring (ACPMon) approach that incorporates remote sensing technologies to collect data about the actual situation on the construction site automatically. In this way, the process of construction progress monitoring can be automated. To this end, various methods have been developed, but so far none have achieved the complete automatic way for continuous construction monitoring in real time and without additional preparatory woks. Either way, this chapter describes an Advanced Construction Project Management (ACPMan) approach that incorporates state-of-the-art CPMan, BIM, and ACPMon insights, whose combination creates a CPMan update based on integration of Industry 4.0.

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iTWO 4.0 | 5D BIM Cloud-based Construction Software. (n.d.). Retrieved July 22, 2019, from https:// www.itwo.com/en/ Kopsida, M., Brilakis, I., & Vela, P. (2015). A Review of Automated Construction Progress and Inspection Methods. Proceedings of the 32nd CIB W78 Conference on Construction IT, 421–431. McCuen, T. L., & International, A. (2008). Scheduling, Estimating, and BIM: A Profitable Combination. Morgantown, WV: AACE International; Retrieved from https://www.tib.eu/en/search/id/ BLCP%3ACN070139179/­Scheduling-Estimating-and-BIM-A-Profitable-Combination/ Mitchell, D. (2012). 5D Bim : Creating Cost Certainty and Better Buildings. RICS COBRA, 1–9. Muhič, S. (2008). Proces brez lukenj. Retrieved March 4, 2017, from https://issuu.com/cgsplus/docs/ dimensio_13 Navisworks | 3D Model Review Software | BIM Coordination | Autodesk. (n.d.). Retrieved July 22, 2019, from https://www.autodesk.com/products/navisworks/overview Norms for construction works. (2005). Ljubljana: Chamber of Craft and Small Business of Slovenia, Construction section. Norms for transports. (2005). Ljubljana: Chamber of Craft and Small Business of Slovenia, Construction section. Retrieved from https://www.ozs.si/ Okvirni opaž Frami Xlife - Doka. (n.d.). Retrieved August 6, 2019, from https://www.doka.com/si/ system-groups/doka-wall-systems/framed-formwork/frami-xlife/index Omar, T., & Nehdi, M. L. (2016). Data acquisition technologies for construction progress tracking. Automation in Construction, 70, 143–155. doi:10.1016/j.autcon.2016.06.016 Pătrăucean, V., Armeni, I., Nahangi, M., Yeung, J., Brilakis, I., & Haas, C. (2015). State of research in automatic as-built modelling. Advanced Engineering Informatics, 29(2), 162–171. doi:10.1016/j. aei.2015.01.001 Porotherm proizvodi. (n.d.). Retrieved August 6, 2019, from https://www.wienerberger.si/proizvodi/zid/ porotherm-opeka.html Project Management Institute Inc. (2000). A guide to the project management body of knowledge (PMBOK® guide). doi:10.5860/CHOICE.34-1636 Projekta inženiring Ptuj d.o.o. (2011). Kindergarten Studenci Maribor - unit Pekre, Basic Design, elelectronic file, No. 120-44-58-10. Ptuj: Projekta inženiring Ptuj d.o.o. (in Slovenian) Pučko, Z. (2018). Automated construction progress monitoring using continuous multipoint indoor and outdoor 3D scanning. University of Maribor. Retrieved from https://dk.um.si/Iskanje.php?type=napred no&lang=slv&niz0=Pučko+Zoran&stl0=Avtor&op1=AND&niz1=&stl1=Avtor&op2=AND&niz2= &stl2=Opis&op3=AND&niz3=&stl3=LetoIzida&vrsta=dok&jezik=0&vir=dk&page= Pučko, Z., Šuman, N., & Klanšek, U. (2015). Building Information Modeling Based Time And Cost Planning In Construction Projects. Organization, Technology and Management in Construction. International Journal (Toronto, Ont.), 6(1), 958–971. doi:10.5592/otmcj.2014.1.6

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Pučko, Z., Šuman, N., & Rebolj, D. (2018). Automated continuous construction progress monitoring using multiple workplace real time 3D scans. Advanced Engineering Informatics, 38, 27–40. doi:10.1016/j. aei.2018.06.001 Rebolj, D., Pučko, Z., Babič, N. Č., Bizjak, M., & Mongus, D. (2017). Point cloud quality requirements for Scan-vs-BIM based automated construction progress monitoring. Automation in Construction, 84(November), 323–334. doi:10.1016/j.autcon.2017.09.021 Rundell, R., & Stowe, K. H. (2007). BIM and Project Planning (1-2-3 Revit Tutorial). Cadalyst. Retrieved from http://www.cadalyst.com/aec/bim-and-project-planning-1-2-3-revit-tutorial-3520 Shen, Z., & Issa, R. R. (2010). Quantitative evaluation of the BIM-assisted construction detailed cost estimates. Journal of Information Technology in Construction, 15, 234–257. Retrieved from http:// digitalcommons.unl.edu/constructionmgmthttp://­digitalcommons.unl.edu/constructionmgmt/4http:// www.itcon.org/2010/18 Staub-French, S., Khanzode, A., Analyst, B., & Candidate, P. (2007). 3D and 4D modeling for design and construction coordination: issues and lessons learned. Retrieved from http://itcon.org/2007/26/ Structural, B. I. M. Software | Tekla. (n.d.). Retrieved July 21, 2019, from https://www.tekla.com/products/tekla-structures Trimble. (2016). BIM Solutions | General Contractor Solutions. Retrieved August 16, 2019, from https://gc.trimble.com/product-categories/bim-solutions%0Ahttp://­gc.trimble.com/product-categories/ bim-solutions Tulke, J., & Hanff, J. (2007). 4D Construction Sequence Planning – New Process and Data Model. Proceedings of CIB-W78 24th International Conference on Information Technology in Construction, 79–84. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.116.4602&rep=rep 1&type=pdf Tuttas, S., Braun, A., Borrmann, A., & Stilla, U. (2016). Evaluation of Acquisition Strategies for ImageBased Construction Site Monitoring. ISPRS - International Archives of the Photogrammetry. Remote Sensing and Spatial Information Sciences, XLI, B5(June), 733–740. doi:10.5194/isprs-archives-XLIB5-733-2016 Underwood, J., & Isikdag, U. (Eds.). (2010). Handbook of Research on Building Information Modeling and Construction Informatics. IGI Global; doi:10.4018/978-1-60566-928-1. Yalcinkaya, M., & Singh, V. (2015). Patterns and trends in Building Information Modeling (BIM) research: A Latent Semantic Analysis. Automation in Construction, 59, 68–80. doi:10.1016/j.autcon.2015.07.012

ADDITIONAL READING Bosché, F., Ahmed, M., Turkan, Y., Haas, C. T., & Haas, R. (2015). The value of integrating Scan-to-BIM and Scan-vs-BIM techniques for construction monitoring using laser scanning and BIM: The case of cylindrical MEP components. Automation in Construction, 49, 201–213. doi:10.1016/j.autcon.2014.05.014

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Eastman, C. M. (2011). BIM handbook: a guide to building information modeling for owners, managers, designers, engineers and contractors. Wiley. Golparvar-Fard, M., Asce, M., Peña-Mora, F., Savarese, S., Asce, M., Peña-Mora, F., & Savarese, S. (2011). Integrated Sequential As-Built and As-Planned Representation with Tools in Support of Decision-Making Tasks in the AEC/FM Industry. Journal of Construction Engineering and Management, 137(December). doi:10.1061/(ASCE)CO.1943-7862.0000371 Kopsida, M., Brilakis, I., & Vela, P. (2015). A Review of Automated Construction Progress and Inspection Methods. Proceedings of the 32nd CIB W78 Conference on Construction IT, (January), 421–431. Omar, T., & Nehdi, M. L. (2016). Data acquisition technologies for construction progress tracking. Automation in Construction, 70, 143–155. doi:10.1016/j.autcon.2016.06.016 Pătrăucean, V., Armeni, I., Nahangi, M., Yeung, J., Brilakis, I., & Haas, C. (2015). State of research in automatic as-built modelling. Advanced Engineering Informatics, 29(2), 162–171. doi:10.1016/j. aei.2015.01.001 Project Management Institute Inc. (2000). A guide to the project management body of knowledge (PMBOK® guide). Book; doi:10.5860/CHOICE.34-1636 Pučko, Z., Šuman, N., & Rebolj, D. (2018). Automated continuous construction progress monitoring using multiple workplace real time 3D scans. Advanced Engineering Informatics, 38(October 2017), 27–40. doi:10.1016/j.aei.2018.06.001 Underwood, J., & Isikdag, U. (Eds.). (2010). Handbook of Research on Building Information Modeling and Construction Informatics. IGI Global; doi:10.4018/978-1-60566-928-1.

KEY TERMS AND DEFINITIONS AEC Industry: Is a construction industry covering the architecture, engineering and construction sector, including professional treatment of a construction project from its design to construction on site. The whole life cycle of a construction project also involves the owner of the facility, especially in the operational phase, which is therefore called AECO industry. As-Built: Represents the actual situation of the building on the construction site during the construction phase. After the completion of construction, the final design project of performed works is created in the form of 2D CAD plans or of As-built BIM model in the BIM approach. As-Planned: Designates the planned variant of the construction implementation created in the design phase as a design in the form of 2D CAD plans or As-planned BIM model in the BIM approach; sometimes referred to as As-designed. Automated Construction Progress Monitoring (ACPM): Represents the execution of the activities for monitoring and controlling the work progress on the construction site in an automatic way using remote sensing technologies. BIM Libraries: Are libraries of BIM elements in the digital form, where BIM elements are parametrically modeled, providing functional, technical and logical integration into the BIM model. BIM elements have a geometric appearance and other information, usually with a high level of development (LOD).

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BIM Software: Is a software that enables BIM approach, sometimes also called BIM tools, BIM modeling software, BIM approach software, and the like. Parametric Modeling: Represents meaningful interconnection of individual structural elements, allowing interactive modification (i.e., the change of one element results in the modification of the other related element).

This research was previously published in the Handbook of Research on Integrating Industry 4.0 in Business and Manufacturing; pages 533-567, copyright year 2020 by Business Science Reference (an imprint of IGI Global).

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Prioritizing the Enablers of Construction Supply Chain in the Industry 4.0 Environment Vivek Agrawal GLA University, Mathura, India Seemant Kumar Yadav Institute of Business Management, GLA University, Mathura, India R. P. Mohanty SOA University, Bhubaneswar, India Anand M. Agrawal https://orcid.org/0000-0002-1740-0292 GLA University, Mathura, India

ABSTRACT Industry 4.0, the fourth-generation industrial revolution, is not only changing the manufacturing industry but others also, like the construction industry and the related supply chain issues. The construction industry has its own challenges (e.g., temporary work and involvement of high coordination, among others). This study is an attempt to explore the enablers to overcome these issues and prioritize them. Decisions are more complex if they are intangible, non-expressible, qualitative, etc. To overcome this problem in the present study, AHP technique is used. With the help of AHP, 4 enablers and 14 subenablers of construction supply chain are prioritized. E-supply chain management is ranked first followed by digitization, tracking and localization, and cloud computing. In the case of sub-enablers, web service technology comes at first rank whereas management information system comes at 14th rank. This study will help the managers and professionals in construction organizations in building a good setup by focusing on these explored enablers.

DOI: 10.4018/978-1-7998-8548-1.ch066

Copyright © 2021, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

 Prioritizing the Enablers of Construction Supply Chain in the Industry 4.0 Environment

INTRODUCTION First industrial revolution began in 1800s with the emphasis on mechanization and generation of mechanical power. It involved the transition from manual to manufacturing. The second industrialization in 1900s began with the use of electrification, resulting in mass production followed by third industrial revolution. The third phase of revolution started in 1960s with the use of digitization, use of microelectronics which facilitates the flexible production (Man and Strandhagen, 2017) with different production line controlled by programmable machines (Rojko, 2017). Such production systems still do not have flexibility regarding to the quantity. In today’s era, due to the development of information and communication technology, industry is compressed with increasing competition and fast changing consumers preferences. Moving ahead from Industry 3.0 which utilizes ICT tools to bring competitiveness the next revolution is being notified as Industry 4.0 This industrial revolution is termed as fourth generation revolution (Industry 4.0) (Gertler, 2003). Which emphasizes on functioning of smart factories with the help of Internet of Things (IOT), CyberPhysical system and Big data. This concept comes from Germany which involves the decentralized control of manufacturing processes with advanced connectivity features and smart automation. Industry 4.0 organizations are not only limited to the use of advanced technology but also the decreasing cost, fast production and better quantity. According to Industry 4.0 organization could result in reduction of production, quality management and logistics cost up to 30%, 20% and 30% respectively. Additionally Industry 4.0 will also be beneficial in terms of shorter supply time, mass production, more human friendly work environment, and effective utilization of resources (Kagermann et al., 2013). Despite the ever increasing application of Industry 4.0, the companies from the field of construction are not able to integrate these practices to keep pace with the counterparts like automotive and electronic sector (Hampson & Sanchez, 2014). One of the reasons could be attributed to the nature of industry itself. As, the construction value system is the result of collaboration of sub-contractors, unorganized workers and customers itself hence it is difficult to make of them innovative and tech savvy. Numerous interrelated processes and sub-processes, construction at different locations makes the construction industry more complex (Dubois and Gadde, 2002; Arayici and Goates, 2012).The output in the form of unique project requires high degree of customization, limited time, face locational challenges, coordination among the small and medium suppliers (Dubois and Gadde, 2002). Supply chain management playing an important role in increasing the productivity of construction industry (London, 2004; Prakash and Mohanty, 2014). In spite of the fact that the construction procedure is extraordinary, SCM can be helpful and successful in construction. The SCM in construction includes a number of internal and external parties which work in coordinated manner to get the work done within the stipulated time interval. The SCM can play a significant role to achieve integration between internal and external suppliers, designers, contractors, subcontractors etc. In view of that a number of researchers have reported the various factors affecting the efficiency and effectiveness of SCM in the field of construction. Some of them includes Lack of coordination, Design problems, Poor quality of materials, poor planning and control Akintoye et al., (2000), Ofori, (2000). To get the winning edge of the SCM at global level it is important to design and implement a highly coordinated supply chain at global level to get the competitive advantage. While that all sounds great, the greatest obstacle to finishing this change is that a large number of the supply chain managers as of now in administration positions are not set up to bridle the capacities of this new transformed coordi1313

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nated supply chain network. One of the biggest reason attributed to this may be that they have not been officially prepared in to process of developing a leadership position while managing the supply chain at global level. Insight taken from literature it is observed that little attention has been paid to supply chain in construction supply chain to describe the current existence (Ali et al., 2010; Bassioni et al., 2004). In this regard, this study is an attempt; 1. To explore the enablers in construction supply chain management to overcome these challenges. 2. To prioritize the explored enablers of construction supply chain management.

BACKGROUND Small construction companies have limited competencies to invest in innovative practices and advanced technology (Kraatz, 2014). Only limited numbers of companies are fully using the advanced technologies and digital planning tools. To overcome these issues, automation, digital access, connectivity and digital data are the four keys seen (Oesterreich and Teuteberg, 2016). Other associated concepts like cost reduction can be possible by using automatic work flow and robotics (Oesterreich and Teuteberg, 2016) through industry 4.0. Material cost can be reduced by use of automatic tracking system (Sardroud, 2012). Big data analytics can be used to support the decision makers to make more effective decision through increased access to information (McMalcolm, 2015). Insights taken from the literature about different enablers in construction supply chain in Industry 4.0 to overcome the challenges are documented in Table 1. Table 1. Enablers in construction supply chain management (related to industry 4.0) SN

1

2

3

4

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Enabler E-Supply chain management

Cloud computing

Digitization

Tracking and Localization

Sub enabler

Reference from Literature

E-Business

Zou and Seo, 2006; Zhang et al., 2009; Aziz and Ahmad, 2011

Extranet

Pala et al., 2016

Web portal

Chenga, 2011; Pinho et al., 2008; Cheng et al., 2010

Collaboration technology

Singleton and Cormican, 2013; Gong and Azambuja, 2013

Web services technology

Das et al., 2015

Mobile internet based CSCM

Shi et al., 2016

Intelligent transportation system

Zhou, 2008; Moon et al., 2017

Management information system

Zhou, 2008; Moon et al., 2017

3D printing

Kothman and Faber, 216

Real time SCM

Min and Bjornsson, 2008; Dallasega et al., 2016

Geographical Information System

Irizarry et al., 2013; Majrouhi and Limbachiya, 2010

Decision Support

Bao and Jin, 2014

Monitoring CSC

Irizarry et al., 2013; Getuli et al., 2016

SC Performance Improvement

Dike and Kapogiannis, 2014; Azambuja et al., 2012

 Prioritizing the Enablers of Construction Supply Chain in the Industry 4.0 Environment

E-Supply Chain Management Supply chain management involves the product flow, information flow and financial flow. Now-a-days construction industry is using the e-portals for supplying the information to their end users. They are using the web portals, because web portals are able to manage information, documents, stock etc (Ajam et al., 2010). Construction companies also using the web portals to control their fleet through controlled hubs with information technology. TenderSpace is an example of online hub, which help in construction supply chain openly, transparently and fairly to avoid conflict.

E-Business E-business is the integration between internet and supply chain. It is transforming many processes from procurement to product design and customer management. It is establishing a new rules of competition for established businesses in a miraculous way (Amit & Zott, 2001). As per the definition of e-commerce, a company who earn at least 10% of its revenue from internet transactions can be referred as a e-business firm. But as construction supply chain is consisting of many small and medium sized firms, these SMEs deals with their niche market hence they do not feel to have a global connectivity via internet. Also due to issue related to the online payment security and lack of necessary IT skilled human resource it is very rare to have e-business in construction sector.

Extranet A type of private network, which use the internet, telecommunication system, and technology to share part of information with vendors, suppliers, partners, customers or with other business securely.

Web Portal A web portal is a customized private website. To assess this login details are required.

Cloud Computing The use of cloud computing and information technology has significantly transformed the construction supply chain management. It indicates a fundamental change of utilizing the information and communication technology (ICT).Due to real time access of information is available to the organization from any location (www.xperiencedynamics.com, Madas, 2017). Cloud computing is improving the collaboration (Madhukar, 2017) in construction supply chain. Cloud computing is beneficial to business owners as it reduces the requirement for users to plan for future, and allows companies to initiate from the least and increase resources utilization. However, despite the fact that cloud computing provides great benefits the IT industry; the development of cloud computing technology in sector like construction is at its early phase. Cloud computing represents a concurrence of two major components (1). IT efficiency and (2). Business flexibility .The IT efficiency represents the situation where the modern computers are utilized with more efficiency using a competitive hardware and software components and business agility reflects a

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platform of competitive tool by adopting rapid improvements in various business processes which are important for the fulfillment of customers request effectively and efficiently in real time.

Collaboration Technology In this data can be merged which is collected from different sources. This merged data can be assessed by different parties and supply chain status can be visualized. It diminishes the risk of out dated and incorrect information.

Web Services Technology This incorporates SC professionals; those are using web portal. Through this information can be access, integrate, retrieved into a supply chain.

Mobile Internet Based CSCM Mobile internet based CSCM is helping in sharing the real time information, improve material flow, technology support, safety issues (Shi et al., 2016). The mobile terminal device and mobile terminal enabling technology can be used mutually with Radio-frequency Identification on construction sites to improve the material control.

Digitization This factor address the issues related to information, information network, logistics and traffic management. The first framework for digitization is proposed by Zhou (Zhou, 2008). Digitization can improve the coordination among different construction organization. Now a day’s customers are expecting excellent quality products and services but many organizations are not able to meet these expectations. To do so it is inevitable that companies need to go for digitization. Digitization brings transparency and coordination in the systems which assist in boosting the business performance. The incremental growth of existing business can be boosted with the help of digitization and related issues like big data analytics. Because the ever increasing process of digitization dramatically reduces the concerned transaction costs related to the collecting information and other controlling activities. Digitization also reduces the overall manufacturing cost as it has been observed that labor costs would comprise an ever smaller component of manufacturers’ cost structures as IT developments and automation progressed (Ford 2009).

Intelligent Transportation System It is an advanced application which provides innovative services related to the traffic management and modes of transport. Due to this users are well informed and make proper coordination with other.

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Management Information System Due to this information can be flow faster than before in more accurate manner at lower cost. By considering this factor the efficiency of construction organization will definitely improved.

3D Printing Also known as additive manufacturing and it is impacting supply chain. It is a process of constructing a physical thing from a three-dimensional model, by using many thin layers in chain.

Real Time SCM The real time information in construction industry is very important. With the assess of real time information a reliable demand forecasting can be obtained.

Tracking and Localization Many tracking and localization technology are available for the construction organization and they will be benefitted by using these. Some are global positioning system, automated material locating and tracking technology etc.

Geographical Information System This is a system which stores all the geographical information. It helps in understanding of what belongs where.

Decision Support Decision support system is very helpful in taking decisions. It also stores the different information which helps the professionals in taking decision optimally in adverse situations, which reduce the risk and diminishes the cost also.

Monitoring CSC In this different systems are integrate in to a special unique system. With this integrated model supply chain professionals in construction industry track of the status of supply chain and to ensure the delivery of materials, warning signals can be provided.

SC Performance Improvement This can be done regardless the type software application. Information and data can be mutually browsed without focusing to the companies. In this data can transfer and share by the standard data exchanges languages.

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 Prioritizing the Enablers of Construction Supply Chain in the Industry 4.0 Environment

MAIN FOCUS OF THE CHAPTER Indian construction market is expected to be US$180billlion by 2020. With emergence of nuclear families and urbanization the demand of houses are increased. By 2020 more than 70% of GDP is contributed by Urban areas, out of this 11% will be contributed by housing sector by 2020 (www.ibef.org). The Union Cabinet of India approved 100 Smart City Projects in August 2015. The Indian government also raised limited for FDI to 100%, and offered different schemes in Union Budget 2018-19 with near about US $9.97 billion. This will boost the construction industry in India. But the companies can get full advantages of this by construing more and more projects. For speed up of these projects, it is necessary that construction companies should focus on supply chain management enablers. Supply chain management enablers in construction industry in industry 4.0 environment are playing an important role now-a-days. Without focusing the different enablers, the advantage of Industry 4.0 cannot be taken by construction industry. Some of the challenges in the construction industry are (Riddell, T. 2016) • • • • •

Shortage of qualified workers Generational Differences Technology adoption Environmental sustainability Project complexity

Such challenges can be overcome by use of fourth generation industrial revolution. But most of the companies do not understand the power and real potential of fourth industrial revolution. For example companies are doing enough to satisfy the government norms but others very little. The technique of Building Information Modeling (BIM) is much more than the digital plan and apprehension – its full continuum should perceive the built asset through to ongoing, in-life servicing and maintenance. In other words Building Lifecycle Management (Stokoe, 2017). The use of prefabrication or offsite, modular construction, with components being produced in automated factories, shows signs of revolutionizing the housing market, enabling relatively rapid construction of low cost but high quality housing to meet social demand. By the use of digital technology, companies have potential to build more energy efficient, intelligent and sustainable building. Few companies in India, using this style of construction policy for cost cutting and time saving. While the interaction with seven senior managers (production) at Delhi based construction companies, it has been analyzed that, professionals in construction industry are aware with the industry 4.0. They are aware with the benefits of Industry 4.0 revolution, but they are not able make the priority of different enablers in industry 4.0 environment in their different construction projects.

RESEARCH METHODOLOGY Decisions are more complex, if it is based on multi criterion. Generally these criteria are intangible, nonexpressible, qualitative, and subjective. Quantification is typical. Analytical Hierarchy Process (AHP) is a technique which can be used in such situations for decision making (Saaty, 1980). AHP converts the qualitative values into metric values which enable the decision makers to evaluate the weights and make 1318

 Prioritizing the Enablers of Construction Supply Chain in the Industry 4.0 Environment

the preferences (Saaty, 1980, 1982, 2008) through a series of pair wise comparisons. AHP provides the relative importance of the criterions which help in giving better decisions. The basic structure of AHP Model (Figure 1), start with the goal at the top that segregate into sub parts which are helping in achieving the goal (Saaty, 1980, 1982, 2008). Figure 1.

Analysis The Analytic Hierarchy Process (AHP), was introduced by Thomas Saaty (1980). It deals with the complex decision making situations where the decision maker set priorities and expected to take best possible course of action. AHP reduces these complex scenarios into to a series of pairwise comparisons, and then integrating the output. In AHP, according to Dağdevirena et al., (2009), initially hierarchy structure is developed for the decision problem similar to a family tree (Albayrak and Erensal, 2004).The following procedure is referred for the formation of the problem given by Mohanty (1992): • • •

The structure of the problem should be decomposable into hierarchical structure; hence structure of any problem is more important than solution of this problem. Structuring can be attended by only few experts. They use their skills, knowledge, facts, documents etc. The experts are expertise in assigning the weights to the different criteria of the structure.

In the present study 4 enablers and their 14 sub enablers were considered on the recommendation of 5 experts’ opinion. These enablers are important because they can help the construction companies in facing the challenges and take the advantage of Industry 4.0. By using AHP, following the discussed

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 Prioritizing the Enablers of Construction Supply Chain in the Industry 4.0 Environment

steps above the pair comparison tables are prepared with the help of 5 expert opinions (1 academician and 4 from industry).

Expert Profile Two experts were more than 55 years of age; one fell in the age group of 45–55 years and two experts fell in the age groups of 35–45 years. Since this study is associated with the construction industry, experience in construction industry is also considered while selection of the experts. No expert selected had experience less than five years. Among the experts selected, two had experience of 6–10 years with Construction Company engaged in supply chain and rests were having an experience of more than 10 years. Four experts out of five are post graduate and one expert (academician) is post doctorate degree. All the experts are of engineering background in their graduation. In the second step criterions or alternatives are compared. At each level of the hierarchy structure the criterions are compared pair wise. Each pair consists of two criteria. In AHP all the pairwise comparison is based on a comparison scale consisting of nine levels (Saaty, 1980, 1982, 2008). The steps for AHP analysis are as follows: Step 1: Make the initial pairwise comparison matrix (from Table 6 to 8) for all the pairs of criterion on the basis of scale shown in Table 2. Table 2. Saaty’s 9-point scale Scale

1

3

5

7

9

2,4,6,8

Compare factor

Equally important

inadequately important

Strongly important

Very strongly important

exceptionally important

middle value

Step 2: Calculate the sum for each column

∑C ij i

Cij Step 3: Standardized each cell by Xij = ∑ iCij Step 4: Calculate the row sum by using the formula Ri =

∑X ij and Weights by Wi = i

Step 5: Calculate vector for priority by using Vi = A. Wi where i = 1,2,3,………., N Step 6: Calculate λi =

Vi and calculate λmax by averaging the λi’s. Wi

Step 7: Calculate consistency index and consistency ratio C.I. =

1320

 max  n N 1

Ri , N

 Prioritizing the Enablers of Construction Supply Chain in the Industry 4.0 Environment

C.R. =

C.I. R.I.

(should be < 0.10, where R.I. means random Index) Table 3. R. I. (Saaty, 1980) Matrix

1,2

3

4

5

6

7

8

9

10

11

Random Index

0

0.52

0.89

1.12

1.26

1.36

1.41

1.46

1.49

1.52

From the above the value of C.R. is less than 0.10, which is an allowable value. Hence, matrix is found to be acceptable due the consistency in the judgments which is in the acceptable range. Step 8: Repeat step 1 to step 7 for each expert and check the consistency ratio for each case.

SOLUTIONS AND RECOMMENDATIONS Calculations for the given problem on the basis of expert score are presented from Table 4 to 8. From the table 4 it is observed that E-supply chain management is very important enabler for in construction supply chain management, followed by digitization, tracking and localization, and cloud computing. E-supply chain management is consisting E-business, extranet, and web-portal as sub enablers. Out of these extranet is the most important sub-enabler with highest priority score i.e. 2.0617 (Table 5) which followed by E-business and web portal. Construction Company should focus on extranet and need more investment in e-business and their web portal for making more effective supply chain. Second enabler Digitization consisting of intelligent transportation system, management information system, 3D printing and real time SCM. The real time SCM is the most important sub-enabler out of the four, which followed by 3D printing, intelligent transportation system, and management information system. Tracking and digitization is at third rank as per the priority score (Table 6) which consist of geographical information system, decision support, monitoring, and SC performance improvement as sub enablers. In this SC performance improvement is the most important sub-enabler and proper care is required to this by construction organization. Monitoring, geographical information system and decision support comes at second, third and fourth rank respectively as per the priority scores (Table 7). Cloud computing consisting collaboration technology, web service technology, and mobile internet based CSCM. Out of these, web service technology is most important sub-enabler followed by mobile internet based CSCM and collaboration technology.

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 Prioritizing the Enablers of Construction Supply Chain in the Industry 4.0 Environment

Table 4. Priority score of enablers Initial Matrix F1

F2

F3

F4

F1

1

3

8.6

9

F2

0.33

1

5.8

8.2

F3

0.114

0.176

1

3

F4

0.11

0.122

0.33

1

1.554

4.298

15.73

21.2

Sensitize Matrix F1

F2

F3

F4

Prioritized Vector (PV)

F1

0.6435

0.6980

0.5467

0.4245

0.5782

F2

0.2124

0.2327

0.3687

0.3868

0.3001

F3

0.0734

0.0409

0.0636

0.1415

0.0798

F4

0.0708

0.0284

0.0210

0.0472

0.0418

Reachability matrix F1

F2

F3

F4

Prioritized Vector (PV)

Weighted Vector

Eigen Value

F1

1.0000

3.0000

8.6000

9.0000

0.5782

2.5417

4.3961

F2

0.3300

1.0000

5.8000

8.2000

0.3001

1.2971

4.3216

F3

0.1140

0.1760

1.0000

3.0000

0.0798

0.3241

4.0587

F4

0.1100

0.1220

0.3300

1.0000

0.0418

0.1684

4.0258

N

4.0000 Average Lambda

4.2005

CI

0.0668

RI

0.9000

CR

0.0743

FUTURE RESEARCH DIRECTIONS Since this study considers the enablers from the literature, future study can be conducted for exploring the enablers by conducting the interviews of the expert. Only 5 experts were considered for the analysis, other can do this study by taking more responses so that validity can be improved.Discuss solutions and recommendations in dealing with the issues, controversies, or problems presented in the preceding section.

CONCLUSION Supply Chain is a finished procedure that begins from the crude materials and the inbound coordination right to the outbound coordination and expediting the thought the table. Without a proficient production network, there would be a ton of bottleneck associated with the association.

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 Prioritizing the Enablers of Construction Supply Chain in the Industry 4.0 Environment

Table 5. Priority score of sub enablers of E-supply chain Management Initial Matrix FE1

FE2

FE3

FE1

1

0.2

1.4

FE2

5

1

3

FE3

0.8

0.33

1

6.8

1.53

5.4

Sensitize Matrix FE1

FE2

FE3

Prioritized Vector (PV)

FE1

0.1471

0.1307

0.2593

0.1790

FE2

0.7353

0.6536

0.5556

0.6481

FE3

0.1176

0.2157

0.1852

0.1728

Reachability matrix FE1

FE1

FE2

FE3

Prioritized Vector (PV)

Weighted Vector

Eigen Value

1.0000

0.2000

1.4000

0.1790

0.5506

3.0759

FE2

5.0000

1.0000

3.0000

0.6481

2.0617

3.1810

FE3

0.8000

0.3300

1.0000

0.1728

0.5299

3.0661

N

3.0000 Average Lambda

3.1076

CI

0.0538

RI

0.5800

CR

0.0928

Supply chain in construction industry is an important concept. With the development of Industry 4.0 the construction company should use the features and those enablers which can reduce the different type of cost and ultimately the production cost of a project or a unit. This study is an attempt to explore those enablers which can overcome the challenges in construction supply chain management. From the limited available study, the different enablers; E-Supply chain management, Digitization, Tracking and Localization, and Cloud computing and their 14 sub enablers are explored. With the help of AHP all the enablers were prioritized. E-Supply chain management is ranked at one with score 0.2809 followed by, Digitization, Tracking and Localization, and Cloud computing with priority score 0.1919, 0.0499, 0.0439 respectively (Table 6). Sub enablers are also ranked on the basis of their priory score. Web service technology comes at first rank whereas management information system comes at fourteenth rank. The companies are required to put enormous focus on the improvement of supply chain performance. Companies can make their supply chain either responsive or efficient with the help of developing an appropriate supply chain capability. These capabilities are very important as far as gaining the competitive advantage is concern. The techniques like digitization cloud computing big data can add more value to the supply chain performance as they all integrates the various cross functional activities and not only integrate they in fact play the role of driver in bringing the coordination among the members of supply chain. The resultant enablers like Monitoring, geographical information system and decision support system web service technology, mobile internet based CSCM and collaboration technology all are proven

1323

 Prioritizing the Enablers of Construction Supply Chain in the Industry 4.0 Environment

to be significant predictor of supply chain performance. The application of these outcome no doubt will help construction companies to bring a consistency between their competitive strategy and supply chain strategy so that they can get their strategic fit. For that purpose not it is upto the construction companies that in which form they are expected to design their supply chain so that the objective of supply chain strategies can be aligned well with the strategies of gaining the competitive advantage. Table 6. Priority score of sub enablers of digitization Initial Matrix FD1

FD2

FD3

FD4

FD1

1

3

1

0.33

FD2

0.33

1

0.2

0.14

FD3

1

5

1

0.33

FD4

3

7

3

1

5.33

16

5.2

1.8

Sensitize Matrix FD1

FD2

FD3

FD4

Prioritized Vector (PV)

FD1

0.1876

0.1875

0.1923

0.1833

0.1877

FD2

0.0619

0.0625

0.0385

0.0778

0.0602

FD3

0.1876

0.3125

0.1923

0.1833

0.2189

FD4

0.5629

0.4375

0.5769

0.5556

0.5332

Reachability matrix FD1

FD2

FD3

FD4

Prioritized Vector (PV)

Weighted Vector

Eigen Value

FD1

1.0000

3.0000

1.0000

0.3300

0.1877

0.7631

4.0656

FD2

0.3300

1.0000

0.2000

0.1400

0.0602

0.2405

3.9981

FD3

1.0000

5.0000

1.0000

0.3300

0.2189

0.8834

4.0349

FD4

3.0000

7.0000

3.0000

1.0000

0.5332

2.1742

4.0777

N

4.0000 Average Lambda

4.0441

CI

0.0147

RI

0.9000

CR

0.0163

This study will help the managers and professionals in construction organizations in building a good setup by focusing on these explored enablers. They can be more focused on those enablers and subenablers who come at the highest priority. All the factors are important but some needs more focus due to their benefits. For example: E-supply chain management can help the professionals for strengthening their SCM using electronic solution and information technology for coordination and building relationship between supplier and contractors. By the use of cloud computing construction organizations can control on duplication of data or data conflicts. It can integrate the data from contractors, suppliers, and

1324

 Prioritizing the Enablers of Construction Supply Chain in the Industry 4.0 Environment

carriers. It also provides, up to date real time information. With the help of digitization, inventory can be tracked on real time basis, traffic condition can be analyzed, by which one can control the lead time. By using the technology for tracking and localization, construction organization can reduce the experience on material stock, reduce the task duration, and also reduce the labor cost by optimum utilization of them. Table 7. Priority score of sub enablers of tracking and localization Initial Matrix FT1

FT2

FT3

FT4

FT1

1

2.6

1

0.33

FT2

0.464

1

0.2

0.14

FT3

1

5

1

0.33

FT4

3

7

3

1

15.6

5.2

1.8

5.464

Sensitize Matrix FT1

FT1

FT2

FT3

FT4

Prioritized Vector (PV)

0.1830

0.1667

0.1923

0.1833

0.1813

FT2

0.0849

0.0641

0.0385

0.0778

0.0663

FT3

0.1830

0.3205

0.1923

0.1833

0.2198

FT4

0.5490

0.4487

0.5769

0.5556

0.5326

Reachability matrix FT1

FT2

FT3

FT4

Prioritized Vector (PV)

Weighted Vector

Eigen Value

FT1

1.0000

2.6000

1.0000

0.3300

0.1813

0.7493

4.1322

FT2

0.4640

1.0000

0.2000

0.1400

0.0663

0.2690

4.0559

FT3

1.0000

5.0000

1.0000

0.3300

0.2198

0.9084

4.1332

FT4

3.0000

7.0000

3.0000

1.0000

0.5326

2.2001

4.1312

N

4.0000 Average Lambda

4.1131

CI

0.0377

RI

0.9000

CR

0.0419

The proper management of these supply chain enablers are related to the acquisition of skills to acquire as a leadership position in global supply chain management. The key skills for the purpose includes, the supply chain manager’s ability to negotiate with different stakeholders, high degree of flexibility in supply chain designing and implementation and a proactive approach to mitigate the risk associated with the supply chain management. The supply chain enablers like E-supply chain management significantly contributes in supply chain negotiation with vendors, the enabler like cloud computing and digitization provides a platform for bringing flexibility in supply chain and the supply chain enabler tracking and localization recues the supply chain risk and improves its performance. As the result the ultimate goal

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 Prioritizing the Enablers of Construction Supply Chain in the Industry 4.0 Environment

of supply chain enablers is to assist the supply chain in making it more competitive and flexible at every level. In addition, the famous 3Cs of supply chain leadership strategies i.e. Communicate, Collaborate and Change cannot be exercised without the successful implementation of these enablers. To some things up the successful implementation of these supply chain enablers can be considered as the building blocks of leadership strategies for making supply chain more flexible, competitive, efficient and effective. Table 8. Priority score of sub enablers of cloud computing Initial Matrix FC1

FC2

FC3

FC1

1

0.2

1

FC2

5

1

3

FC3

1

0.33

1

7

1.53

5

Sensitize Matrix Prioritized Vector (PV) FC1

FC2

FC3

FC1

0.1429

0.1307

0.2000

0.1579

FC2

0.7143

0.6536

0.6000

0.6560

FC3

0.1429

0.2157

0.2000

0.1862

Reachability matrix FC1

FC2

FC3

Prioritized Vector (PV)

Weighted Vector

Eigen Value

FC1

1.0000

0.2000

1.0000

0.1579

0.4752

3.0105

FC2

5.0000

1.0000

3.0000

0.6560

2.0038

3.0548

FC3

1.0000

0.3300

1.0000

0.1862

0.5605

3.0105

N

3.0000 Average Lambda

3.0253

CI

0.0126

RI

0.5800

CR

0.0218

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Madhukar, B. A. (2017). Construction Supply Chain Management. For Reeingeneering Construction Industry. Available: http://www. builtconstructions.in/OnlineMagazine/Bangalore/Pages/ConstructionSupply-Chain-Management-0114.aspx MajrouhiSardroud, J., & Limbachiya, M. C. (2010). Integrated advance data storage technology for effective construction logistics management. Academic Press. Min, J. U., & Bjornsson, H. C. (2008). Agent-based construction supply chain simulator (CS 2) for measuring the value of real-time information sharing in construction. Journal of Management Engineering, 24(4), 245–254. doi:10.1061/(ASCE)0742-597X(2008)24:4(245) Mohanty, R. P. (1992). Project selection by a multiple-criteria decision-making method: An example from a developing country. International Journal of Project Management, 10(1), 31–38. doi:10.1016/02637863(92)90070-P Moon, S., Han, S., Zekavat, P. R., Bernold, L. E., & Wang, X. (2017). Process-waste reduction in the construction supply chain using proactive information network. Concurrent Engineering, 25(2), 123–135. doi:10.1177/1063293X16667451 Oesterreich, T. D., & Teuteberg, F. (2016). Understanding the implications of digitisation and automation in the context of Industry 4.0: A triangulation approach and elements of a research agenda for the construction industry. Computers in Industry, 83, 121–139. doi:10.1016/j.compind.2016.09.006 Ofori, G. (2000). Greening the construction supply chain in Singapore. European Journal of Purchasing & Supply Management, 6(3-4), 195–206. doi:10.1016/S0969-7012(00)00015-0 Pala, M., Edum-Fotwe, F., Ruikar, K., Peters, C., & Doughty, N. (2016). Implementing commercial information exchange: A construction supply chain case study. Construction Management and Economics, 34(12), 898–918. doi:10.1080/01446193.2016.1211718 Prakash, A., & Mohanty, R. P. (2015). Understanding construction supply chain management for road projects. International Journal of Logistics Systems and Management, 22(4), 414–435. doi:10.1504/ IJLSM.2015.072747 Riddell, T. (2016). Top 5 issues facing the construction industry in 2017. Available at: https://esub.com/ top-issues-facing-the-construction-industry-2017/ Rojko, A. (2017). Industry 4.0 concept: Background and overview. International Journal of Interactive Mobile Technologies, 11(5), 77–90. doi:10.3991/ijim.v11i5.7072 Saaty, T. (1980). The Analytic Hierarchy Process. McGraw-Hill. Saaty, T. (1982). Decision-Making for Leaders. Wadsworth. Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83–98. doi:10.1504/IJSSCI.2008.017590 Sardroud, J. M. (2012). Influence of RFID technology on automated management of construction materials and components. Scientia Iranica, 19(3), 381–392. doi:10.1016/j.scient.2012.02.023

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Shi, Q., Ding, X., Zuo, J., & Zillante, G. (2016). Mobile Internet based construction supply chain management: A critical review. Automation in Construction, 72(82016), 143–154. doi:10.1016/j.autcon.2016.08.020 Shi, Q., Ding, X., Zuo, J., & Zillante, G. (2016). Mobile Internet based construction supply chain management: A critical review. Automation in Construction, 72, 143–154. doi:10.1016/j.autcon.2016.08.020 Singleton, T., & Cormican, K. (2013). The influence of technology on the development of partnership relationships in the Irish construction industry. International Journal of Computer Integrated Manufacturing, 26(1-2), 19–28. doi:10.1080/0951192X.2012.681907 Stokoe, J. (2017). The construction industry – a leader or a follower in the fourth industrial revolution? Available at: https://blogs.3ds.com/uk/construction-industry-revolution/ Xperiencegroup. (2016). Five Ways to Improve Construction Supply Chain Management. Available: http://xperiencedynamics.com/blog/30/five-ways-toimprove- construction-supply-chain-management/ Zhang, M., Wang, J., & Zhang, J. (2009, October). Research on construction supply chain management in e-business environment. In Industrial Engineering and Engineering Management, 2009. IE&EM’09. 16th International Conference on (pp. 1565-1568). IEEE. 10.1109/ICIEEM.2009.5344374 Zhou, J. (2008, October). The optimization mode for construction supply chain management based on information techniques. In Service Operations and Logistics, and Informatics, 2008. IEEE/SOLI 2008. IEEE International Conference on (Vol. 1, pp. 1054-1058). IEEE. Zhou, J. (2008). The optimization mode for construction supply chain management based on information techniques. SOLI 2008, IEEE International Conference on Service Operations and Logistics, and Informatics, 1054–1058. 10.1109/SOLI.2008.4686554 Zou, P. X., & Seo, Y. (2006). Effective applications of e-commerce technologies in construction supply chain: Current practice and future improvement. Journal of Information Technology in Construction, 11(10), 127–147.

ADDITIONAL READINGS Bogoviz, A. V. (2019). Industry 4.0 as a new vector of growth and development of knowledge economy. In Industry 4.0: Industrial Revolution of the 21st Century (pp. 85–91). Springer. doi:10.1007/978-3319-94310-7_8 Büchi, G., Cugno, M., & Castagnoli, R. (2020). Smart factory performance and Industry 4.0. Technological Forecasting and Social Change, 150, 119790. doi:10.1016/j.techfore.2019.119790 Frank, A. G., Dalenogare, L. S., & Ayala, N. F. (2019). Industry 4.0 technologies: Implementation patterns in manufacturing companies. International Journal of Production Economics, 210, 15–26. doi:10.1016/j.ijpe.2019.01.004

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Lasi, H., Fettke, P., Kemper, H. G., Feld, T., & Hoffmann, M. (2014). Industry 4.0. Business & Information Systems Engineering, 6(4), 239–242. doi:10.100712599-014-0334-4 Olsen, T. L., & Tomlin, B. (2020). Industry 4.0: Opportunities and Challenges for Operations Management. Manufacturing & Service Operations Management, 22(1), 113–122. doi:10.1287/msom.2019.0796 Oztemel, E., & Gursev, S. (2020). Literature review of Industry 4.0 and related technologies. Journal of Intelligent Manufacturing, 31(1), 127–182. doi:10.100710845-018-1433-8 Popkova, E. G. (2019). Preconditions of formation and development of industry 4.0 in the conditions of knowledge economy. In Industry 4.0: Industrial Revolution of the 21st Century (pp. 65–72). Springer. doi:10.1007/978-3-319-94310-7_6 Tortorella, G. L., Vergara, A. M. C., Garza-Reyes, J. A., & Sawhney, R. (2020). Organizational learning paths based upon industry 4.0 adoption: An empirical study with Brazilian manufacturers. International Journal of Production Economics, 219, 284–294. doi:10.1016/j.ijpe.2019.06.023 Xu, L. D., & Duan, L. (2019). Big data for cyber physical systems in industry 4.0: A survey. Enterprise Information Systems, 13(2), 148–169. doi:10.1080/17517575.2018.1442934

KEY TERMS AND DEFINITIONS 3D Printing: Also known as additive manufacturing and it is impacting supply chain. It is a process of constructing a physical thing from a three-dimensional model, by using many thin layers in chain. Cloud Computing: The use of cloud computing and information technology has significantly transformed the construction supply chain management. It indicates a fundamental change of utilizing the information and communication technology (ICT). Collaboration Technology: In this data can be merged which is collected from different sources. Decision Support: Decision support system is very helpful in taking decisions. Digitization: This address the issues related to information, information network, logistics and traffic management. E-Business: E-business is the integration between internet and supply chain. It is transforming many processes from procurement to product design and customer management. E-Supply Chain Management: Supply chain management involves the product flow, information flow and financial flow. Extranet: A type of private network, which use the internet, telecommunication system, and technology to share part of information with vendors, suppliers, partners, customers or with other business securely. Geographical Information System: This is a system which stores all the geographical information. It helps in understanding of what belongs where. Industry 4.0: This concept comes from Germany which involves the decentralized control of manufacturing processes with advanced connectivity features and smart automation. Industry 4.0 organizations are not only limited to the use of advanced technology but also the decreasing cost, fast production and better quantity. Intelligent Transportation System: It is an advanced application which provides innovative services related to the traffic management and modes of transport.

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Management Information System: Due to this information can be flow faster than before in more accurate manner at lower cost. By considering this factor the efficiency of construction organization will definitely be improved. Mobile Internet-Based CSCM: Mobile internet based CSCM is helping in sharing the real time information, improve material flow, technology support, safety issues. Monitoring CSC: In this, different systems are integrated into a special unique system. Real-Time SCM: The real time information in construction industry is very important. With the assess of real time information a reliable demand forecasting can be obtained. Tracking and Localization: Many tracking and localization technology are available for the construction organization and they will be benefitted by using these. Web Portal: A web portal is a customized private website. Web Services Technology: This incorporates SC professionals; those are using web portal.

This research was previously published in Leadership Strategies for Global Supply Chain Management in Emerging Markets; pages 147-172, copyright year 2020 by Business Science Reference (an imprint of IGI Global).

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Chapter 67

Redesign of the Workplace for Toolmakers Towards Industry 4.0 Ivana Radić Faculty of Logistics, University of Maribor, Slovenia

Simona Šinko Faculty of Logistics, University of Maribor, Slovenia

Bojan Rupnik Faculty of Logistics, University of Maribor, Slovenia

Tomaž Kramberger Faculty of Logistics, University of Maribor, Slovenia

Brigita Gajšek https://orcid.org/0000-0001-5744-7151 Faculty of Logistics, University of Maribor, Slovenia

ABSTRACT This chapter presents a workspace redesign of a toolmaker position in a tooling industry towards Industry 4.0. In general, the theory is lacking studies that would pinpoint concrete methodology to present the redesign of a company specific workplace in a way that would follow guidelines of the Industry 4.0 systematically. In this research, the authors have primarily focused on a digital readiness and identification of potential areas and tasks suitable for the implementation of enabling technologies. Collected data are based on the case study conducted in a tooling company. The result is a procedure to generate a systematic approach, a roadmap, towards Industry 4.0. To achieve the redesign of toolmaker’s workplace, the authors combined the AS-IS state analysis and use Toolbox Industry 4.0. The effects of a redesigned process manifest in reduced laborious, repetitive manual work, errors, and toolmaker workload.

DOI: 10.4018/978-1-7998-8548-1.ch067

Copyright © 2021, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

 Redesign of the Workplace for Toolmakers Towards Industry 4.0

INTRODUCTION Tooling industry is a highly innovative and labor-intensive industrial sector. Manufactured tools enable the mass production of the products in the various fields of industry. For example, in the automotive industry, there is a need to produce more thousands of pieces of different complexity and specificity to assemble only one vehicle. According to this, the manufactured tools are designed very precise to provide and satisfy the demands of the client. The margin of deviation between pre designed product and final product produced by the tools is measured in tenths of a millimeter. The tooling industry is facing the challenge in terms of precision of the tools and in terms of punctuality, to deliver the products due to date. The lead-time is due to a growing competition and innovative technologies, gradually becoming more and more constrained. According to this, the transformation of production process of tooling companies towards digitalization is essential. The opportunity in European tooling industry lays in radical innovations and implementation of smart technologies. Merely the automation of a production process does not satisfy the demand anymore, making the combination and implementation of novel technologies is necessary. The direction of a solution can be found in the concept held under the term of Industry 4.0. The Industry 4.0.is a concept that describes connectivity and inter communication between different building blocks of production process. Under its auspices, we can assemble the terms Internet of things, that enable connectivity among existing and novel technologies, and big data, that represents large amount of gathered data from where new knowledge can be extracted and further used to build an agile company. Agile company is equipped to respond in a real time to market triggers. For a planned transition towards industry 4.0, tooling companies need to identify their current state and define the potential target state, as this is the practice in other, less project-oriented companies. For this process, different methods and tools are available. One of the available tools is a Toolbox Industry 4.0, provided by the Guideline industry 4.0. The toolbox is presented as a table, with rows presenting different fields of applications and the columns that present certain development stage (Galaske, Arndt, Friedrich, Bettenhausen, & Anderl, 2018) and effective analytical tool that helps to assess current state and map the potential target states that are on the higher level of a maturity state in terms of industry 4.0 readiness. After mapping the target state, the further steps for rising the maturity state are identified. This procedure leads the user towards reorganization and improvement of examined company systematically. Redesign of a workspace is possible with the combination of existing technologies. Defined current state is a base line for planning the way to industry 4.0. From a toolbox, users can acknowledge themselves with possible further steps to more mature level on Industry 4.0 development scale. This chapter aims to present the current state, of a toolmaker’s workspace and to identify the potential activities within it that can be upgraded with new technologies that enable better production procedures and affect human workforce. This was accomplished with a combination of methods. Maturity model for assessing the digital readiness, synoptic to present the current state and the Toolbox industry 4.0. The comprehensive study was carried out with the coordination presented by the Guideline industry 4.0, which was developed by VDMA with cooperation with the Department of Computer Integrated Design (DIK) at the TU Darmstadt, the WBK Institute of Production Science and the Karlsruhe Institute of Technology (KIT). The current state was assessed based on the partially structured interview with the employees and with the business process modeling methodology. The obtained current state of a workplace is used to define potential activities within the working process that are applicable for implementation of advanced technologies of team´s choice. These technologies are collaborative robots, AGV or augmented reality. 1334

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TOOLING INDUSTRY European tooling industry is composed mainly out of small to medium enterprises, producing more than 13 Million Euro annual turnover and it holds more than 7000 companies (European tooling platform, 2019). It holds outstanding know how in its production processes and design. Historically, the tooling companies were small family businesses, where the know-how was transferring down the lineage within the families. The industry dates its origin, as independent field, back in the beginning of a 20th century (Henriques & Peças, 2013). Nowadays, the tooling industry is characterised as a small to medium enterprise, with up to 200 employees. The production process is mainly automatized but still holds a big proportion of manual, repetitive work, mostly in assembly area. The European tooling industry is confronted with Asian competition that is entering the market. Together with the challenge that the tool making industry’s biggest industrial clients are moving to the areas with the lower production costs this leads to a consequence that majority of a tooling companies are facing decrease in its competitive properties. This pushes the tooling industry into an inevitable transformation to keep up with and exceed the eastern market. This can be achieved on the account of raising the added value in terms of reducing costs on account of resources or time or to increase the value of tools (Henriques & Pecas, 2012). In the toolmaking process, the practice shows, that one of the main time consuming activity is connected with a demand for precision. The highly complex engineering procedures in manufacturing process require precise quality control of surface and dimensions of the product. This often creates the repetitive loops in the production. The manufactured tools are brought back in the production for smoothing out the deviations to fit into the client’s demands multiple times before the final tool meets the market needs. Likewise, the tools components within the production process undergo the repetitive procedure of hand polishing, to achieve the desired results of its surface as well. This task takes place in the assembly area of a tooling company and depends on the skills of a toolmaker, what makes the activity not just prone to errors and repetitive work but shows also an issue from ergonomic view and presents the opportunity for improvement.

Manufacturing Process in a Tooling Company The activities within the production process in a tooling company take place simultaneously rather than sequentially (Henriques & Pecas, 2012). For instance, when the tooling company receives the inquiry for a specific tool, the main part of a supply is technological procedure of a tool production. In addition, while the quotation coordination is still in progress, the raw material order is initiated simultaneously. The production is mainly constructed from design, manufacturing process, tool inspection, assembly and tray out in the end. The initial phase, before production process, is a detailed design with specified technological procedures of all phases in the production process. Following the design acceptance, the manufacture of a tool takes place. This is mainly a combination of milling, drilling, grinding and electro-discharge machining. In the final phases, the tools undergo inspection, assembly and try out. Figure 1 presents the overall manufacturing process.

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Figure 1. Manufacturing process of a tooling company

INDUSTRY 4.0 AND ENABLING TECHNOLOGIES Industry 4.0 was introduced during the Hannover fair in 2011 and stands for 4th industrial revolution. It represents innovative technologies in manufacturing industry and mainly referring the enabling technologies to as Internet of things, cyber physical systems and cloud computing (Xu, Xu, & Li, 2018). In the spring 2014 the VDMA, BITKOM and ZVEI published the definition of Industry 4.0. They defined it as striving towards optimizations of value chains with implementations of autonomous and flexible production. This is enabled with the accessible, real time information and networked systems that require higher stage of automation. The required automation is enabling Cyber Physical systems that are equipped with microcontrollers and actuators, sensors and communications interfaces. Cyber physical system can operate autonomously and is in the interaction with production environment. Consequently, the factory becomes the “smart factory” (Reiner Anderl & Fleischer, 2015; Kolberg & Zühlke, 2015). Technical approach towards the Industry 4.0 is based on the usage of advanced mechatronic and adaptronic control systems spread across cyber physical systems and enable connectivity based on Internet protocol. The connectivity is the basic predisposition that enables the communication between all components of the system. In the production environment cyber physical production systems capable of communicating

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with other systems and people are introduced. The interconnection among these components is possible in vertical and horizontal direction. Vertical direction is directed into two-way digital information flow from quotation, design and development phase to production activities. Meanwhile, the horizontal integration enables connectivity and communication between systems. Therefore, the digitalization and automation of the processes across the whole value chain is fundamental for further integration of Industry 4.0 concept. (Reiner Anderl & Fleischer, 2015) said that: “The benefits of industry 4.0 unfolds with a clever combination of already existing technologies” (p. 7), which describes the Industry 4.0 in core and bring Internet of things in the limelight. The internet of things is one of the core enablers of Industry 4.0. It somehow creates confusion, due to the two completely different term meanings. One stands for network oriented vision and the other holds the physical object oriented meaning. Semantically, the term stand for a worldwide network of interconnected objects, with unique address for each object based on standard communication protocol (Atzori, Iera, & Morabito, 2010). It can be said: “Cyberize the physical and physicalize the Cyber” (Fleischer, 2016). The components of the system must have the ability to exchange information and to be interconnected. Key Internet of things technologies are developing in direction where the sensors do not deliver just gathered precise data from a component but poses self - awareness and can predict remaining life time. Likewise, the machines possess selfawareness, self-prediction and can self-compare through their operators, which can lead to the ability to provide self-diagnosis. Big data, as another enabler of Internet of Thing, can provide historical descriptive and prescriptive information that can propose the understanding into what is happening in the machine or process (Gill et al., 2016). With Internet of things, other enabling technologies can provide smart, autonomous systems and manufacturing environment. In addition, more technologies can be used for implementing the Industry 4.0 idea into the manufacturing process. These technologies are additive manufacturing, automated guided vehicles, and collaborative robots. With the redesign of a workspace, the authors in this chapter limited the choice of potential enabling technologies on the once that are appropriate for implementing into the toolmakers working process. The selected technologies are Collaborative robots, Automatic guided vehicles (AGV) and Augmented reality (AR). In the final phase of the research, these technologies will be positioned as solutions for achieving the target state in the workplace redesign process.

Collaborative Robots (Cobot) The term Cobot was invented by professors Edward Colgate and Michael Peshkin at Northwestern University in Evanston, Illinois (Djuric, Rickli, & Urbanic, 2016). The collaborative robots were developed to assist human in a shared working environment (Djuric et al., 2016). Mutual, human robotic systems amplify the human productivity and reduce stress and fatigue while they boost the benefit from combination of a robotic automation and flexibility with human soft skills (Villani, Pini, Leali, & Secchi, 2018). In the industry, the collaborative robots are mainly utilized in the areas where there are repetitive tasks in the human unfavorable environment (Djuric et al., 2016). Unlike traditional industrial robots, collaborative robots are considered safe. Safety function differentiates the industrial robots from cobots. While the industrial robots are positioned in a safety cell that strictly divides the robot from a human, the collaborative robots can interact with human in the sense of collaboration or coexistence (Villani et al., 2018). In the collaborative relationship, the human and robot are interchangeably performing the working task while in the coexistence they purely share the same working environment.

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The levels of engagement between human and the robot have led to different applications of human - robot systems. In accordance to this, a comprehensive taxonomy was introduced that classifies the human – robot collaboration into eleven categories (Villani et al., 2018). Yanco & Drury (2004) classified the taxonomy categories into task type, task criticality, robot morphology, ratio of people to robots, composition of robots team, level of shared interaction among teams, interaction roles, type of human robot interactional proximity, decision support for operators, time - space taxonomy and autonomy level. Collaborative robots’ basic functions are safety and collision avoidance systems. The force sensors measure and respond to force pressure and stop the cobots activity if the critical level is reached. This prevents potential accidents and minimize the concern of danger to a human. In working processes where the majority of activities are manual repetitive tasks, like the ones in the assembly area, the cobots can enhance productivity by better precision and reduce time needed to complete the task. Table 1. Available commercial robots Manufacturer

Specifications

KUKA - iiwa

This is a lightweight collaborative robot designed for delicate assembly work. Workload: 7 -14 g Maximum Reach 800-820 mm. Controlled axis: 7

Fanuc – CR 35iA

Sole collaborative robot with the load of 35 kg. Maximum reach: 1813 mm Controlled axis: 6

ABB Yumi IRB 14000-0.5/0.5

Maximum reach: 559 mm Axis: 14

BOSCH APAS assistant

Load: 2 kg Maximum reach: 911 mm

Universal robots UR 3

axis: 3 Load: from 3 to 10 kg Maximum reach: 500 mm, 850 mm, 1300 mm

Yaskawa motorman

Axis: 15 Load: 15 kg Maximum reach: 559 mm horizontally and 845 mm vertically

Source: (Clapaud, 2015; Djuric at al., 2016)

There are various Cobots already available on the market. Based on the application field the industry offers types of robots that can resemble humans, so called humanoid type, robotic hand and more. The maximum load ranges up to 35 kg, which makes them suitable for assembly area, manufacturing and medicine, to name a few, where the assistance to human is preferable.

Augmented Reality Augmented reality is a technology that enables coexistence of a real world elements enhanced with the computer-generated images. In its definition, it combines the virtual elements with a real 3D environment and in a real time (Krevelen, 2007). Technology is not limited to a head mounted display and it can involve other sensory perceptions aside form just the vision. These include hearing, touching and

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Table 2. Applications in manufacturing Application

Description

Pick and place: Packaging and palletizing

Robot can pick an object and place it into another location. The maneuvers like positioning the object into right direction and orientation can be carried out. In the latter task, a vision system is required. This enables the robot to recognize the orientation and execute the correct rotation (Robots, 2018).

Machine tending

The robots can assist a human with supplying the machine with work pieces. These machines can be CNC, engraving machines, injection molding machines, metal stamping press (Robots, 2018).

Process tasks

The robot stands as a tool holder and carries the tool across the work piece’s surface to achieve desirable results. This feature enhances the productivity and quality. For example it can be used for welding, gluing, dispensing (Robots, 2018), etc.

Finishing tasks

These finishing tasks include polishing, grinding and deburring. Robots are able to press with the greater force on the work – piece surface in order to remove the extra material (Robots, 2018). In manufacturing industry, the assembly area where there is still manual work in place, like hand – polishing.

Quality inspections

Quality inspections take place with finished products manufactured by a precision engineering procedures (Robots, 2018). For example, in the tooling industry the manufactured piece undergoes the quality inspection to ashore that surfaces and dimensions of a piece are in sync with required specifications. This task is achievable with vision system and offers faster and more consistent output than that of a human (Robots, 2018).

smell and correspondingly haptic (touch) display, olfactory (smell) display, aural (hearing) display and gustatory (taste) display. The technology is becoming more and more spread and its variations are still growing tremendously. According to (Paelke, 2014) “AR applications have high potential to improve the user experience of applications in which users must access and interact with information that has a direct spatial relation to their immediate environment” (p. 1). In the manufacturing industry it can assist employees with provided instructions for pick and place a work piece, with the assemble instructions and it can pass the information about location of specific components or work piece in production together with technological procedures of each component or work piece. For the applications in the industrial environment, the spread of new technique in user interface (NUI) is beneficial. Compared to a classical GUI (graphical user interface) the new technique, NUI (natural user interface) gives the use of physical interaction objects in tangible user interfaces, gestures and elements of virtual and augmented reality in real time and space (Paelke, 2014). Boeing is the great example of benefiting from implementation of augmented reality technology in their manufacturing for assembly department. They are using Google glasses with Skylight enterprise software (“UPSKILL,” 2019). Previously, employees received instructions printed on a paper, which required great amount of organization and prolonged attention for the employee to follow these instructions. These working environments have negative impact on productivity, time consumption and health of an employee. The company benefited with implementation of augmented reality. They reduced a significant amount of time in assembly, improved the productivity and working environment. In the General electric case, workers use smart glasses for ordered pieces and products. They identify the product, its quantity and location in the warehouse using the smart glasses technology. In Siemens, assembly area for assembling turbines the manufacturer uses paper written instruction, technical drawings and schemes, as well. The implementation of smart glasses reduces error rate and boost productivity. (Bařák, 2018) with their AIRe Lens conducted a pilot case for implementation of augmented technology into Siemens assembly area. The glasses provide tutorial that is easy and simple. 1339

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It constitutes two phases of which first one requires form the assembler to pick the work – piece. The pieces are easy to recognize, easy to locate and easy to get the information about the right quantity (Barak, 2018). Jaguar Range Rover implemented this technology for personnel training. The app was developed in collaboration with Bosh and Re’flekt. The application enables the employee to see what lays behind the already built dashboard of a car. It exposes wiring, sensors, and other components. Furthermore, it enables interaction with these components for training purpose. The augmented reality is implemented for applications of quality control and visual inspection and maintenance. It provides sequential steps according to a procedure that user follows. The benefit is seen from reducing error rate and time reduction. To sum up, the benefits from technology are seen on all aspects in business. The company enhance value by boosting the productivity of employees and the precision, which is of great importance in complex engineering manufacturing environments. With this enabler, it significantly reduces error rates and assembling time.

Automated Guided Vehicle Automated guided vehicles (AGVs) have been recognized as a most appropriate alternative way to handle materials in the industry. They are capable of handling all kinds of raw material, semi-product and finished product among different work stations in the factory and warehouse facilities. In their work, they have proved to be effectively and efficiently (Ganesharajah, Hall, & Sriskandarajah, 1998). According to (Schulze & Wüllner, 2006) the definition of AGV is: “An AGV is simply a robot that pursues any symbol or wire in the ground, or utilizes machine vision, magnetic field or laser guidance for navigation”. The first AGV was used in the year 1953. First type of AGVs have been in use for material handling for 40 to 50 years (King & Wilson, 1991). The system in first AGV was a wire buried in the floor. AGVs were capable of detecting the wire and follow the route, outlined with that simple wire (Martínez-Barberá & Herrero-Pérez, 2010). After that, changes in global technology, have caused enormous technological improvements in AGV technology also. Changes in technology, which helps the AGV to improve are according to Russell and Wilson (2007): “multiple vehicles, navigation aids, communication hardware, and safety devices”. Next generation of AGV was slightly more advanced and smart, with the technology which was driven by microelectronic and microcomputers. In the past, widespread use of AGVs was limited by high prices, but today they are used in almost all industrial sectors. AGV systems in basic consist of some cooperative driverless vehicles, whose primary task is to transport good in the facility. But AGV vehicles could not work by itself. They are usually following a predetermined guide paths (which could be physically or virtually) embedded in the layout of the facility. The guide paths are usually coordinated by a distributed computer-based system (Ganesharajah et al., 1998). When AGVs share their work environment with humans and manually driven vehicles the safety is the most important part. AGVs must be capable of avoidance of collision with entities in environment. Typically, solution to achieve environment without the collisions and accidents is use of laser scanners, with whom AGVs could detect the presence of obstacles and re-plan its path. Still they have a lack with incapability to distinguish between the presence of human and objects (Sabattini et al., 2018). Trenkle, Seibold, & Stoll (2013) pointed out three major hazards of using AGVs: possibility of collision with person, falling down and tilting over. 1340

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Advantages of using such systems in the industry are according to (Ganesharajah et al., 1998): “increased routing flexibility, effective space utilization and reduced overall operational cost”. In the industry sector, they are popular because of their flexibility, efficiency, reliable and extendable nature and their ability for multi- tasking (Reddy Gutta, Sai Chinthala, Venkatesh Manchoju, Charan Mvn, & Purohit, 2018). AGV vehicles are just one component of the whole system. Beside the vehicles, which are the basic, such system needs stationary system control, build-lateral components and peripheral component (Schulze & Wüllner, 2006). Vehicles are led with the help of navigation systems. For the integration of the vehicles into system communication systems are responsible. The task of administrating orders, the arrangement, the drive optimization and hand interface to navigation and communication with the vehicles are the system control responsibilities. Peripheral components are elements like battery loading station and load transfer mechanisms.

Applications in Manufacturing In manufacturing environment, the AGV’s are used for supplying the working spaces, transportation between two defined locations in the place and on the specified trail, for tracking a worker for the purpose to assist with “pick and place” and more.

METHDOLOGY FOR UPGRADING THE WORKPLACE ACCORDING TO INDUSTRY 4.0 CONCEPT The theoretical background about the systematical implementation of Industry 4.0 ideas into the company are widely dispersed across multiple literature among which some summaries are presented in the following subchapter. Accordingly, the authors have gained some additional insights and practical experiences with applying those theoretical methodologies in the industry.

Theoretical Views on Systematical Implementation of Industry 4.0 The literature is limited with the concrete and systematic steps of how to benefit from the ideas of industry 4.0 concept. For this purpose, the existing methodologies can be combined into the practical and beneficial entirety. In addition, the new models and methodologies are rising up. For example, (Galaske et al., 2018) were considering the human factor readiness for the digitalized manufacturing in their research. The focus in the research is the presentation of an assessment tool Toolbox workforce management 4.0. The toolbox is designed to provide assistance to evaluate the development stage of two segments. These segments are examined from a human factor view and working environment. (Arndt, Auth, & Anderl, 2018) introduced the guideline for implementation of an assistance systems in a digital company that are human focused. Focus of the research is internal discussions within the company and the communication with the employee’s representative. The paper addresses the human factor as the main center of attention. The paper presents the specific and concrete systematical methodology of how to implement and construct flexible workspace assistance systems that will help the employee. The implementation process is divided into five segments. These are analysis phase, requirements phase, 1341

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concept phase, implementation phase and the validation phase. The challenge with the human oriented analysis opened the questions of a security of obtained personal data. The authors conclude that clarification and definition of a legal framework for intelligent worker assistance systems is urgent and vital. Table 3. Applications in manufacturing (AGV) Technology

Application

Automated vehicle used for the purpose of collaborative automated order picking (DHL [DHL], 2016).

Automated vehicle is following the worker and is able to avoid the obstacles. The worker can send the vehicle to drop the material when is fully loaded. The vehicle can have additional trailers, the steering is performed on both axis. The positive effect of the vehicle is in benefiting from the improvement of working environment from health perspective. It assists a worker and reduces the repetitive heavy tasks for the worker.

Material supply – supplying the working stations (Relay [Relay], 2017).

The worker places a demand trough a tablet. This is an instruction for the vehicle to start the execution. The vehicle is connected with the working stations and the warehouse. The command is valid in the other direction as well. Delivery from the warehouse to the working station and between working stations.

Transportation with unmanned aerial vehicle (GommeBlog. it:CarPerformance, 2018)

Application of unmanned aerial vehicle – transportation of smaller components across the production.

Transportation of produced vehicle body parts along the optional path

Spatial manipulations of products along determined path.

Moving shelves to assists the production process

Shelves storing the material are preprogrammed to assist the worker with movements towards where is needed. The orientations are the reference spot on the floor.

Transport of working desks/ equipment and bigger work pieces

Operator with special software controls AGV. The operator determines the path. The vehicle is able to detect obstacles and execute control checks, horizontally and vertically.

Open Shuttle – KNAPP (KNAPP AG, 2014).

Automatic upload and download. The vehicle performs deliveries according to a determined plan or by the orders in real time. It holds the capability of autonomous upload and download.

KARIS PRO system (KIT, 2017)

Automated vehicles are executing the working tasks individually or they have the ability to connect between each other and into flexible system of vehicles.

Auto Pallet Mover (Jungheinrich, 2015).

It is transported to predetermined locations. This enables the laser navigation technology and it makes the vehicle usable in the entire warehouse floor. It possesses the separated control system that enables infrastructure planning, vehicle coordination and optimization of a traffic flow. It has collision avoidance function, which enables the vehicle the autonomous drive as well as the manual control.

MOVEBOX automation kits - Baliyo

Solutions provided by MOVEBOX are transforming the forklift trucks into unmanned vehicles that can place the pallets on command. With navigation these vehicles do not require new infrastructure to function. This hybrid solution offers manual and automatic guidance based on the client’s needs

FiFi - BÄR Automation & KIT (BAERAUTOMATION, 2013).

FiFi is the assistant for picking that follows the ordered with virtual guidance and is responsive to human movements like hand waving. Worker places the order with hand movement. The transportation is executed autonomous.

Remotely controlled vehicle Toyota

Toyota presented a solution for remotely controlled forklift trucks. It contributes to cycle time reduction and creates easier transfer of material, which leads to increased productivity up to 20%. In addition, it reduces the energy consumption up to 10%. The safety function is achieved with collision avoidance system and automatic obstacle detection.

AGV vehicle with abilities to shift perpendicular to the direction of travel or diagonally without turning the vehicle (KUKA, 2016).

The vehicle is distinguished by its unlimited maneuverability due to its special wheel design. It possesses the ability to turn in place around the center of gravity. The controls can be manual, semi-automatic and automatic. The three trolleys in can carry mass up to 63 t.

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In their paper Employee Data Model for Flexible and Intelligent Assistance Systems in Smart Factories (Arndt & Anderl, 2016) develop the concept of an employee data modeling. Formerly the authors introduced the requirement for the intelligent worker assistance systems that needs to be obtained for the modeling. The paper presents in detail few partial models that are combined into final model as well as the prototype implementation. (Pessl, Sorko, & Mayer, 2017) revealed an implementation guideline for enterprises called the roadmap industry 4.0. As the response to a need for systematical approach toward industry 4.0 of the companies, the authors have presented the procedure to analyze the maturity level of the enterprise, to identify their target states and to set the action plant to fulfill the target state. In the paper, the researchers presented a six step roadmap industry 4.0 that can be applied to a six segments. Here, the authors focused on the human aspect. With the research, they identified importance of the embedment of the industry 4.0 ideas on a strategic level prior to implementation its ideas into the company as well as the involvement of employees from different divisions within the company in the assessment of a company’s readiness for industry 4.0 with maturity models. For the purpose of the research presented here, the authors combined the guideline industry 4.0 and process modeling methodology. Adnerl et al. (2016) with the cooperation of VDMA, wbk and Dk, introduced the guidline Industry 4.0 for small and medium enterprises. It is not a ready procedure for a concrete company but offers tools and procedurees how to implement the ideas of industry 4.0 in the selected company. They presented a toolbox that helps the companies to assess their initial stage on the path towards Industry 4.0. (Erol, Schumacher, & Sihn, 2016) proposed a three stage process model to systematicaly guide the company towards Industry 4.0 vision. The stages are envision, enable and enact. The model is based on the coinovations and strategic roadmapping and was implemented on several companies.

The Case of a Toolmakers Workplace Lesson learned from theory is that redesign of a toolmaker’s workspace demands the holistic approach, which is being developed by practitioners and academics. We tested one of alternatives on a real case of toolmaker’s workplace and recognize it as a viable one. Toolmaker is a worker who is responsible for manual metalworking, cleaning and polishing of engravings, drilling, assembly of tool positions into assemblies and assembly of assemblies into finished tools and other tasks. Some of these tasks are routine, time consuming, involving inappropriate posture or repetitive movements. There is also a lot of walking, checking the manufacturing status at neighboring workplaces, and self-handling of materials. These activities represent the potential for automation, information support and transfer to a collaborative robot. For the purpose of this research a methodology or a procedure is needed to bridge the current state because of an informed opportunity into a modern workplace, keeping in mind the ideal of Industry 4.0. Based on literature review and conclusions from theory, the process of increasing the industry 4.0 maturity level on workplace was divided into 4 phases. Initial phase is the assessment of a digital maturity in a company. This is evaluated by multiple already existing methods that are available. The assessment is actually self - evaluation and provides information about assessor’s view on the digital in the company. Second phase is recording a current state of a company with the guidance of a Toolbox Industry 4.0. For this purpose, the structural and in - depth record of the performed tasks within working process is performed. The tasks are modeled based on business process modeling technique and presented as synoptic. Together with in – depth activities or tasks there are additional criteria to fulfill that are enabling 1343

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to follow the Industry 4.0 vision. In the third phase, the activities that could potentially be replaced or reorganized with enabling technologies are identified. These tasks are repetitive, labor – intensive and demanding and are suitable for implementing existing enabling technologies for assistance to an employee in the assembly area. In the final phase, the enabling technologies are mapped to replace or enhance the value of the tasks identified in a previous phase.

Toolmakers Workplace– Assemblyline Toolmaker produces tools for the batch production of new products made of metal, plastic and other materials. His workspace is located in the assembly area of the shop floor butin reality; it extends on almost every area in the production floor. Toolmaker has to gain skills for independent planning, preparation and execution of work. Furthermore, due to the information flow across entire production area, the toolmaker needs good communication skills, cooperation and adaptation into the production environment. Work is related to creativity, innovativeness, determination, ability to concentrate and the ability to acquire new skills. Additional toolmaker’s skills are resourcefulness and professionalism that arise due to the demand for quick reactions and actions. The work requires general physical and manual skill to be able to execute from various hand - rotational positions and a sense of precision. The work is done mainly standing, with turning, bending and lifting objects. In addition, important feature that a toolmaker possesses is a good size and shape recognition. Threats that are connected with toolmakers working environment rise form the demanding physical body positions and handling a machine tools, and require obeying the safety and health regulations. His area of expertise includes productions of tools and devices, drilling and clamping preparations, dismantling, of tools, grinding, sharpening, technical treatment, welding and tool maintenance, tool control, measuring tool control and storage of a tool. Workflow smoothness depends mostly on the acquired skills of a toolmaker. Production of a tool follows the procedures that are based on the plans, drawings and technical documentation. Most often, the information of a design and technological processes are stored in CAD drawings. The information from CAD is passed down to a shop – floor’s assembly area in the form of assembling instructions, drawings and templates (Caudell & Mizell, 2003) that are printed on the paper and also available on computer.The toolmaker operates with the composition plan of a tool and with the paper that holds the information of technological procedure of each component that assemble the tool. With assembling instruction’s list, the toolmaker can plan and locate each component in the production. The components are dispersed across the shop floor for mechanical or thermal treatment. Once collected, are brought in the assembly area for the final surface maintenance and assembling. The dimensions of components are being inspected based on a manual specification list. The information flow between the toolmaker and other actors in the production is running mostly orally or in written format. The surface maintenance is done mainly manually, by hand polishing, which is a significantly time consuming activity that in time, reduces the productivity and it massively depends on the skills of a toolmaker.

Assessing the Maturity State – Maturity Models The process of transformation of the company towards the industry 4.0begins with evaluation and identification of a current state in terms of a digital readiness of various departments across the company. 1344

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Idea of Industry 4.0 is based on assumption that production processes are digitalized from the start to the end of a process. Compared to the Industry 3.0, which principle was the automation of individual machines in the production line, the Industry 4.0 is based on digitalized processes throughout the whole value chain, meaning that digital maturity of a company is a predisposition for implementation of ideas of the Industry 4.0. The literature offers more maturity models that can help to assess the current state of a company in terms of digital maturity. The maturity models are classified as theoretical models that can provide the direction of how the company’s capabilities can develop from a current state towards target state, which is usually on a higher maturity level. The maturity models represent consecutive stages of development where every next stage represents the more mature level on the maturity scale. Based on the purpose they serve different agenda (Pöppelbuß & Röglinger, 2011): • •

Descriptive maturity models serve as a tool to assess the current state of an investigated company, with given assessment criteria, to describe the current standing of a company on every stage of a maturity scale. Prescriptive maturity models include the measures for improvement on each maturity level.

In 2014 the Forrester developed the comprehensive digital maturity model that was merging their internal marketing and maturity models of e-commerce. The product was developed under the name Forrester’s Digital Maturity Model 4.0. It is combined from a set of points that helps the assessor to place the company on the digital maturity level. The maturity level is attuned based on comparison with other, competitive companies that evaluated their maturity level. This helps the companies to see their level of usage of digital and to develop the competitive strategies that will enable the client’s superior user experience and create the agility on operational level (Gill et al., 2016). The Forrester maturity model expands over four dimensions that are culture, technology, organization and insights. The culture dimension reveals the company’s readiness towards digitalization and the empowering effect on the employees. Technology dimension evaluates the use and adaptations of enabling technologies. Organizational view of assessment shows the company’s level of alignment in supporting digital strategy and its implementation. Lastly, the insight dimension evaluates the usage of costumer’s and business data in the success evaluation and inform strategy. The points that result in evaluation are summed based on the questionnaire and clustered into one of the four levels. Lowest level presents “Sceptics”. Sceptics are on the starting point towards digital as they are missing the essentials to provide a digital training, costumer experience guiding or social media marketing. Next stage presents “Adopters”. The adopters are already equipped with willingness to digitalize and invest into infrastructure that supports digital. The manufacturers, health care companies and utilities are contributing to the count in this cluster. These sectors mainly focus on the production, services, and less on the customer’s relations. To improve and elevate digital level these companies should promote marketing that is above the aims of satisfying merely the execution but instead, to focus on developing strategies to attract more customers. Following stage represents “Collaborators”. The indicator for this segment is high collaboration internally and externally, to support training and innovation with digital. Furthermore, the enhanced communication is seen between marketing and IT. Directives for this segment to progress into next stage are focused towards enhancing creativity, building brand awareness and practice data analytics. 1345

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Ultimate goal on the maturity scale presents “Differentiators” that represents digital experts. Further development for this segment is to merge the digital and physical elements completely. The questionnaire, available as part of the Forrester digital maturity model 4.0, was introduced in the tooling company and filled by the leading personnel in each department. The results were analyzed by the examiner and positioned into one of the four dimensions. The interpretations of the results led to defining the department in the company that gathered least points and have fallen into the dimension that represents lower digital maturity. This department was further examined as shown in the following paragraph.

Identifying the Current State of a Toolmakers Workflow Guideline Industry 4.0 by VDMA, WBK and DU presented a tool that can support the companies in the assessment of their current state. It delivers a list of the company’s own capabilities and ideas that could potentially be adopted for creation one’s own Industry 4.0 concept. Competences and potential technologies are integrated among layers, where each layer represents certain stage in a communication abilities and connectivity between the cyber components and physical components. Application fields presented in the Toolbox Industry 4.0 are divided into development stages where every preliminary stage presents less mature stage towards Industry4.0. This presentation allows the user to evaluate each application development stage systematically (R Anderl et al., 2015) and within various fields. Specific case of the Toolbox workforce Management 4.0 is presented in (Galaske et al., 2018). For the purpose of the toolmaker’s workspace redesign, the Toolbox Industry 4.0 was applied in the production. The authors of the Toolbox Industry 4.0 provided the development stages and application for the production purposes as follows: • • • •

•

•

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Data Processing in the Production: Where it can be determined the level of automation for data processing. It can vary from simple documentation to complete automatic process planning. Machine to Machine Communication (M2M): The machine – to - machine communications can be leveled from no communication between the machines in the production to a highest level of interconnected machines trough M2M software. Company Wide Networking with the Production: Specifies the level of connectivity and information exchange among different business units. Infrastructure of Information and Telecommunication Technologies in Production: Provides the insight of availability to implement new applications to enhance technical processes (Adnerl et al., 2016). The levels are aligned from information exchange protocol trough internet based portals with data sharing to the highest level where the suppliers and customers are already fully integrated into the process design. Man Machine Interfaces: Where the level of information exchange and cooperation between the machine and the worker is evaluated. The levels are from no information exchange between worker and a machine, use of local users’ interface, centralized or decentralized monitoring, use of mobile users’ interface and augmented and assisted reality. Efficiency with Small Batches: Due to growing uniqueness of a product the company can benefit from striving towards flexible production facilities.

 Redesign of the Workplace for Toolmakers Towards Industry 4.0

In order to apply the Toolbox Industry 4.0 to identify the current state the detailed interview and synoptic was created. Table 3 presents the in - depth view of a working process. Data were collected during the semi - structured interview and close collaboration with the head of production, project manager and toolmakers. It was supported by the guided tour around the production floor. When building the process chart the authors focused on gathering not just the activities of a worker in assembly area but they focused on the format of the input that triggers the specific activity. For example, when the activity is initiated, the important information was if the input that started following activity was oral, written or in some other format. Next step was to obtain information about the visibility of the input. Is the input delivered to the location where the following activity should take place, or the worker has to look for it on the shop - floor across the production? Table 4. In – depth view of activities executed by the toolmaker in a tooling company. Activity

Input/Initiation

Execution

Department

Data -Document/oral communication/signal/ format - Electronic format/physical format - Was delivered to a worker/ the worker has to look for it?

Material -Type - Delivery method

Tool allocation to a toolmaker

-Assembling instructions: electronic format and document in a physical form printed on paper. Electronic format is reachable on a joint computer in assembly area. - Technological procedure of components: document in paper form.

/

Executor -Human/machine/Vehicle/tool -Location - Visibility/transparency

Duration -Average time from start to finish - Disruptions/ downfalls that affect the duration

Head of production

/

-Document does not include standard components - Work flow is highly dependable on workers skills - Information is obtained with physical inspection throughout the production line. …

Task familiarization

Paper document: Toolmakers intuitively locate the components trough the production line. Communication exchange happens orally

/

Toolmaker Every worker has its own working space with a fixed desk. The instruction plan is spread ever the desk. The executed and finished technological tasks of a component are marked down manually on the document.

…

…

…

…

For this purpose, the synoptic is divided into three segments, which are activity, input or initiation and execution. Furthermore, segment input or initiation is divided on two subfields that are data and material. Section execution divides on additional two subfields, which are Executor and Duration. In the activity segment, the consecutive tasks that are in a domain of the toolmaker are presented. It is important to break down the activities on the tasks that are manageable for additional analysis that consists of identification of repetitive jobs that affect the productivity, error rate or employee’s workload. For example, in the toolmaker’s work process, all the tasks that a toolmaker does, are written in the rows in the Table 4. This means that a toolmakers working activities are marked down sequentially. 1347

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Sub segments data and material describe the input that initiated that activity. In the case of a toolmakers task familiarization the input that initiated this activity was data, due to the fact that worker received assembling instructions printed on the paper. Furthermore, this sub segment also defines in what format the input came in and if it was delivered to a worker or the worker had to look for it. The industry 4.0 vision is that instances are digitalized and visible. In this particular case, the toolmaker received the instructions at his working position and in a physical format. Under the execution segment, it is provided who is the executor of a task and the time duration of a task. This section also holds an information about threats that potentially affect the duration of execution time, which holds the opportunities for improvement. The executor of a task can be a worker, a machine or a vehicle. With the information provided in the synoptic and the use of a Toolbox Industry 4.0 the current state was marked. Combined with examined competencies the following action was to determine the target state.

Mapping Enabling Technologies with Identified Tasks - Identification of the Activities within the Toolmakers Workspace After the in depth analysis with synoptic, the activities that show utmost potential for improvement in the sense of exoneration of a worker, reduction in error rates due to repetitiveness of the tasks, enhancement of production process, were selected. The measure for improvement is increase in process efficiency and reduction of employee’s workload. The tasks that represent repetitive work, manual work with potential to automation and heavy work were extracted. With the PiTj the task for specific work position was market, where P represents sequential work position and T corresponding task. For each of identified PiTj the detailed analysis was performed based on the given criteria and provided with a solution with the most contribution to a company’s productivity performance and technological procedures. Table 5.Connecting the potential activity with enabling technology Activity with Potential for Improvement

Description of the Activity

Consequence

Potential Solution

Internal transport – implementation of collaborative robot or AGV

Determining the function of the activity and attributes of the activity.

The consequence that the potential solution creates (reduced error rate, automation, reduction of a workload, etc.)

Enabling technologies as a solution.

Cleaning and polishing of a component’s surfaces after mechanical treatment – implementation of collaborative robot

…

…

…

Based on this systematic approach the company is able to create a roadmap with illustrative directions of how to achieve the desired results. Starting with the identification of core challenges and following the developed deciding criteria, the company can identify the potential enabling technology to implement into its working environment.

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CONCLUSION In this chapter, the authors present the implementation of the Industry 4.0 concept into tooling company. Firstly, the important predisposition for implementation of Industry 4.0 ideas is to create awareness among the participants in the company. Secondly, the literature offers few guidelines that can assist the companies on their way towards implementation of the idea, which are generalized and can be applied into chosen company. In this chapter it was demonstrated how to start implementing elements of Industry 4.0 into a tooling company systematically. Starting with awareness, the self-assessment of digital maturity was executed. This was done with Forrester maturity model 4.0, which offers initial insights about the employee’s view on the assessed department of the company. Based on this initial evaluation the company can identify the department that is least digital and create guidelines for further evolvement. In addition, to identify the current state another tool was used, the Toolbox Industry 4.0. This was performed simultaneously with synoptic, that offered in depth analysis of individual activity within toolmaker’s working process that assists the examiner to determine the current state correctly. What is more, with gathered information about every individual activity within the working process, the researchers could identify repetitive, demanding tasks that could potentially be replaced or upgraded with enabling technologies that gradually constitute the concept of Industry 4.0 in a company. With the provided solution, the company could be able to lessen the toolmakers workload and boost the productivity.

FUTURE WORK PROJECTION So far, only four phases of the methodology for redesigning a toolmaker’s workspace in a tooling industry towards Industry 4.0 have been designed. They are roughly described above. In theory, at first glance, the solutions seem to have been described, but this is not the case. The theory proposes quite a few tools, methods and models, but unfortunately, every proposal has only a partial area of application in real business environment. For example, maturity models indicate the current level of industry 4.0 maturity for a certain researched company, but the result, the level of maturity with generic tips, is of little use to companies. They are missing concrete suggestions about what to do and where they have a bottleneck in the whole system. At the time of the B2B integration and seamless supply chains, the scope of the system, which is mastered by single identity, expanded significantly from just one production hall to all that concern at least three companies, namely supplier, manufacturer and customer. The complexity arises from the size of the system for managing and rapidly evolving technologies. New technologies are coming to market daily, but not sufficiently tested. Developers and producers heavily rely on the new version, which will be on a market in a very short time. The new version eliminates the weaknesses of the previous version, which were recognized during use in practice and in only a small scope from testing phase. This causes many problems for companies, especially if there are many shortcomings, which require immediate replacement. Unfamiliarity with the technology and bad experiences from its use are a cause of restraint on the side of companies. Additionally, employees mostly do not have time to follow the technological development because they are focused on their core business. That is why we evaluating developing of the methodology for redesigning a workspace in production company towards Industry 4.0as needed and important. So far, we know how to evaluate the maturity of a company using Industry 4.0 maturity model. The company thus gets the basis for benchmarking and access to very rough guidelines for its further develop1349

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ment. In case of dissatisfaction with its current maturity, company must proceed with a thorough study of its business processes, including a list of resources for their daily operation. It is impossible to avoid examining each activity or job separately. This requires rating tables with clearly defined criteria and weights. We designed some but they need validation, detailing and refinement. A decision model should be considered to help narrow down the pool of potential technologies based on known from practice.

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Reddy Gutta, P., Sai Chinthala, V., Venkatesh Manchoju, R., Charan Mvn, V., & Purohit, R. (2018). A Review on Facility Layout Design of An Automated Guided Vehicle in Flexible Manufacturing System. Materials Today: Proceedings, 5(2), 3981–3986. doi:10.1016/j.matpr.2017.11.656 Relay. (2017). Robot Deliveries in Logistics Facilities. Retrieved from Youtube website: https://www. youtube.com/watch?v=dMz1luRHzUg Sabattini, L., Aikio, M., Beinschob, P., Boehning, M., Cardarelli, E., Digani, V., ... Fuerstenberg, K. (2018). The PAN-robots project: Advanced automated guided vehicle systems for industrial logistics. IEEE Robotics & Automation Magazine, 25(1), 55–64. doi:10.1109/MRA.2017.2700325 Schulze, L., & Wüllner, A. (2006). The approach of automated guided vehicle systems. 2006 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2006, 522–527. doi:10.1109/SOLI.2006.328941 Trenkle, A., Seibold, Z., & Stoll, T. (2013). Safety requirements and safety functions for decentralized controlled autonomous systems. 2013 24th International Conference on Information, Communication and Automation Technologies, ICAT 2013. 10.1109/ICAT.2013.6684063 UPSKILL. (2019). Retrieved from https://upskill.io/landing/request-demo/ Villani, V., Pini, F., Leali, F., & Secchi, C. (2018). Survey on human–robot collaboration in industrial settings: Safety, intuitive interfaces and applications. Mechatronics, 55, 248–266. doi:10.1016/j.mechatronics.2018.02.009 Yanco, H. A., & Drury, J. (2004). Classifying human-robot interaction: An updated taxonomy. Conference Proceedings / IEEE International Conference on Systems, Man, and Cybernetics. IEEE International Conference on Systems, Man, and Cybernetics, 3, 2841–2846. doi:10.1109/ICSMC.2004.1400763

This research was previously published in the Handbook of Research on Integrating Industry 4.0 in Business and Manufacturing; pages 492-511, copyright year 2020 by Business Science Reference (an imprint of IGI Global).

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Ceramic Industry 4.0:

Paths of Revolution in Traditional Products João Barata Technological Centre for Ceramics and Glass (CTCV), Portugal & University of Coimbra, Portugal & Polytechnic Institute of Coimbra, Portugal & Miguel Torga Institute (SMT), Portugal Francisco Silva Technological Centre for Ceramics and Glass (CTCV), Portugal & University of Minho, Portugal Marisa Almeida Technological Centre for Ceramics and Glass (CTCV), Portugal

ABSTRACT Industry 4.0 presents new challenges for traditional sectors of the economy, for example, the production of ceramic products. This chapter reveals how traditional ceramic industries can (1) assess, (2) plan, and (3) execute Industry 4.0 adoption. The findings are based on the Portuguese ceramic sector. Three interrelated dimensions of the fourth industrial revolution are studied, namely, (1) digital ecosystems, (2) security and safety, and (3) digital sustainability. Industry 4.0 is not restricted to high-tech products and cannot be addressed by one-size-fits-all solutions. Moreover, it requires cooperation within business ecosystems. The authors propose a model for Ceramic Industry 4.0 and accessible guidelines for managers involved in global supply chains. This chapter suggests emergent research opportunities for (1) sectorial maturity models, (2) data quality and regulatory compliance, (3) cyber-security and risk management, and (4) an integrated vision of sustainability in the digital era.

INTRODUCTION Industry 4.0 is changing traditional sectors of the economy (Brettel & Friederichsen, 2014). The impact of the forth industrial revolution is particularly relevant in small and medium sized enterprises (SMEs) with high levels of manual work. This is the case of ceramic companies that export the majority of their production and must be prepared to compete at a global scale. The ceramic industry from the European DOI: 10.4018/978-1-7998-8548-1.ch068

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 Ceramic Industry 4.0

Union (EU)-27 accounts for 23% of global ceramics production. According to the Eurostat, it represented a production value of 28 billion Euros in Europe and over 200.000 direct jobs in 2015. Ceramic industry could be divided in ten major sub-groups: bricks and tiles, floor and wall tiles, sanitaryware, pottery & tableware, refractories, abrasives, clay pipes, expanded clay, porcelain enamel, and technical ceramics. All these ceramic industry subsectors are energy intensive, namely due to the drying and firing processes, which involve firing temperatures between 800 and 2000 ºC. The manufacture of ceramic products is a complex interaction of raw-materials, technological processes, people, and economic investments. It includes the transport and storage of raw materials, ancillary materials and additives (e.g. deflocculating agent – sodium silicate for preparation of raw materials), preparation of raw materials, shaping, drying, surface treatment, firing, and subsequent treatment (Quinteiro, Almeida, Dias, Araújo, & Arroja, 2014). Complexity of the production process is diverse and also the market requirements are different for each ceramic industry sub-group. Yet, the entire sector is affected by the fourth industrial revolution. There are new technological opportunities for ceramic production. Recent examples include the use of mobile technologies in maintenance and product traceability (Barata, Cunha, Gonnagar, & Mendes, 2017), additive manufacturing, 3D printing, and simulation platforms (Smit, Kreutzer, Moeller, & Carlberg, 2016). However, Industry 4.0 in mineral non-metal manufacture raises many challenges for managers. We subscribe to the view of Oesterreich and Teuteberg (2016, p. 136) about the “urgent need for the development, understanding and assessment of frameworks, business models, reference models and maturity models for Industry 4.0 implementation with focus on technology, people and processes”. Industry 4.0 assessment models tailored for specific sectors of the economy will be essential. Other challenges include the creation of digital competencies (Prifti, Knigge, Kienegger, & Krcmar, 2017), the development of digital ecosystems (Andersen & Ross, 2016), improvement of work practices, and sustainable development (Chen et al., 2015). Moreover, there is an urgent need to identify and deploy pilot cases to guide the major changes towards industry of the future. This chapter addresses Industry 4.0 in traditional sectors and specificities of mineral non-metal production in Portugal. The next section presents the background of our research. Afterwards, we identify challenges and opportunities in three key dimensions for the ongoing industrial revolution in ceramic, namely, digital ecosystems, safety and security, and digital sustainability. Next, we present the results of a field study and propose strategic recommendations. These developments emerged from a 120 participants’ workshop that mobilized the entire industry. The chapter concludes revealing future research directions in the scope of digital transformation of ceramic production.

Background Industry 4.0 is gaining increasing attention by researchers worldwide. A keyword search made in Google Scholar using a combination of the terms “Industry 4.0”, “Industrie 4.0”, and “Fourth Industrial Revolution” reveals a constant growth in the recent years, especially since 2013. There are several databases available for scientific research (e.g. Scopus, EBSCO, B-on …), but we decided to start with Google Scholar because it presents a broad search result of both academic and practitioners contributions. Figure 1 illustrates this trend.

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Figure 1. Evolution of Google scholar results about the topic of Industry 4.0

Industry 4.0, Industrie 4.0, or Usine du Futur are examples of the terms used to identify a priority for Europe: Industry digitalization. The examples include the digital single market (European Commission, 2016c), the mobility of business processes within the entire supply chain and the upgrade for an integrated digital world with profound socio-technical implications. The term Industry 4.0 was initially coined as a reference for high tech policies of the German government. However, digitalization and cyber-physical systems are still in an immature state, especially in traditional sectors of the economy, for example, ceramic and glass manufacturing. Later, the German government include other policies in parallel to Industry 4.0, namely, sustainability, nanotechnology, and internet-based services, requiring an integrated approach by managers. As stated by Prof. Klaus Schwab, “…we are in the midst of the Fourth Industrial Revolution, which will affect governments, businesses and economies in very substantial ways. We should not underestimate the change ahead of us…” (Schwab, 2015). In November 2016, Jean-Claude Juncker reinforces the idea that “…digital technologies are going into every aspect of life. All they require is access to high speed internet. We need to be connected, our economy needs it, people need it…” (European Commission, 2016a). In his speech we can identify several important figures, for example, 90% of the professions will soon require digital qualifications, online commerce represents a saving of eleven billion Euros, and there is a priority to support cloud initiatives and the Internet of Things (IoT). Three main goals are identified: (1) improve connectivity, (2) create a better context for business, and (3) promote growth and employment. The European Commission mobilizes fifty thousand million Euros of public and private funding for industry digitalization. Moreover, the agenda aims at the creation of new competencies for the digital era (European Commission, 2016b). According to Smit et al. (2016), the scenario in each country and each economic sector differs and it is possible to identify specific requirements for Industry 4.0 implementation, as presented in Figure 2. The standardization is a top priority, referring to the open systems and platforms needed to connect the different elements of supply chains. Next, it is necessary to redesign business processes (Vidgen & Wang, 2006). It is also necessary to create new business models supported by information systems. Mobile and cloud also poses new challenges for ciber-security. Finally, the study also points to the importance of research investments, social aspects, and a legal framework.

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Figure 2. Requirements for Industry 4.0 – Results presented by BITKOM, VDMA and ZVEI in 2013, considering 278 companies (Acatech, 2013; Smit et al., 2016)

And what happens in the case of traditional products that represent a significant part of the world economy? For example, in construction “only 19% of engineering and construction companies have advanced data analytics capabilities” and “it simply won’t be possible for companies to achieve advanced digitization without making a step change in investment, given the continued rapid progress anticipated by companies who are already leading” (PwC, 2016). There are several barriers to consider, as presented in Figure 3. Figure 3. Barriers for industry digitalization: Global Expert Survey 2016 by McKinsey (Bauer et al., 2016)

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There are several barriers that managers face to implement Industry 4.0, including organizational aspects, confidentiality, and integration in the supply chain. These barriers are particularly relevant in traditional sector of the economy, mostly supported by small and medium sized companies; more difficulties to cooperate with universities in advanced research; and fewer resources to invest in technological innovation. As a consequence, traditional sectors must joint efforts with industry associations to (1) diagnose their Industry 4.0 maturity level, (2) create a tailored roadmap according to the needs and opportunities of the supply chain, and (3) implement pilot projects to guide their Industry 4.0 efforts. The importance of pilot projects is also highlighted by Bauer et al. (2016) “providing the right implementation support both for an initial pilot, and for scaling the efforts across different sites is crucial to succeed”. The next section presents specific challenges for traditional products that we identified in our contacts with Portuguese ceramic industries.

INDUSTRY 4.0: THE CHALLENGE FOR TRADITIONAL PRODUCTS This section addresses three key dimensions for Industry 4.0 research in ceramic industry: (1) digital ecosystems, (2) safety and security, and (3) digital sustainability. The first dimension includes technological aspects of Industry 4.0 and specific guidelines for industry digitalization. Next, we approach the social perspective of Industry 4.0, considering the new risks for people but also opportunities to improve work conditions. Lastly, we address the sustainability element that is so crucial to ceramic industries, namely, the environmental and energy elements of Industry 4.0.

Digital Ecosystems Digitalization is determinant to Industry 4.0 (Smit et al., 2016; Zhou, 2013) and involves social (Degryse, 2016; Prifti et al., 2017), technical (Leyh, Schäffer, Bley, & Forstenhäusler, 2017), and organizational (Weill & Woerner, 2015) challenges. According to Weill and Woerner (2015), companies should use digital technologies to increase their knowledge about the consumers and lead the creation of digital ecosystems involving multiple business partners. A vision of business ecosystem was initially suggested by Moore (1996) and is inspired in biological ecosystems. In this context, businesses cannot be planned and managed apart from the environment. Organizations evolve trough symbiotic relationships between the business and other elements of the ecosystem, for example, their partners, customers, and suppliers. As a consequence, business innovation requires moving beyond the organizational borders and managers are advised to plan an ecosystem approach to digital success (PwC, 2016). As stated by Bharadwaj, El Sawy, Pavlou, and Venkatraman (2013) in “a digitally intensive world, firms operate in business ecosystems that are intricately intertwined such that digital business strategy cannot be conceived independently of the business ecosystem, alliances, partnerships, and competitors”. There are important pillars of industry 4.0, namely, cloud computing, mobile connectivity, social aspects, big data and associated analytics, and innovation accelerators such as robotics, additive manufacturing, or the internet of things (Brettel & Friederichsen, 2014; Lasi, Fettke, Kemper, Feld, & Hoffmann, 2014; Smit et al., 2016). Big data means that large amounts of digital data can be analysed to assist the business strategies. Cloud computing and mobile devices are essential to create digital platforms that connect people and businesses around the globe. Smart sensors and location-based technologies are 1357

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generating new digital data in real time that can be used to assist robot operation and support human decisions. 3D printing is now changing prototyping of new products and also the production process in many industries. There are several contributions that explain how each pillar of industry 4.0 can contribute to digital ecosystems. Big data will be essential for additive manufacturing with ceramics, one of the most common materials for this form of manufacturing (Wang & Alexander, 2016). Cloud and mobile can support the development of new MES (manufacturing execution systems) platforms, more accessible and tailored for small and medium sized ceramic companies (Barata, Cunha, Gonnagar, & Mendes, 2017). Internet of things can assist energy management in ceramics, for example, building ceramics (Wang, Huang, Chen, Liu, & Xu, 2016) where the energy contributes for an important part of the final product cost, namely it can represent till 30% of the total cost. Huson and Hoskins (2014) studied 3D printing for concept models and ceramic artworks. Each pillar can contribute for the creation of digital ecosystems (Andersen & Ross, 2016; Weill & Woerner, 2015) in the perspective of (1) the digital infrastructure (e.g. Wang & Alexander, 2016) or (2) digital services. Moreover, it is possible to find studies that use a combination of industry 4.0 enablers to propose new solutions for ceramics (e.g. Wang, Huang, Chen, Liu, & Xu, 2016). The list is vast, but technologies are only one part of the equation. According to several authors (Acatech, 2013; Brettel & Friederichsen, 2014; Lasi et al., 2014; Smit et al., 2016), industries must evaluate its maturity, create a comprehensive digital strategy and implement pilot projects to evolve in their Industry 4.0 efforts. The next section addresses possible tools and approaches to evaluate industry 4.0 readiness and prepare a roadmap.

Design-Time of Industry 4.0: Assessing Maturity Levels and Establishing the Strategy There are several maturity models for Industry 4.0, however, the existing proposals are recent and there is a lack of proposals that completes the procedure model of development for transfer, evaluation, test, and maintenance (Becker, Knackstedt, & Pöppelbuß, 2009). In fact, some models are still under development, for example, FIR (2017), INTRO4.0 (WP5) Eureka project “Introduction strategies of Industry 4.0 methodology and technology for SMEs” to end in 2018 (KIT, 2016), COTEC maturity model for Portuguese industry, or IPH Hannover (IPH, 2017). Table 1 presents a list of academic and practitioners’ models that are available to assess Industry 4.0 and establish a digital strategy. The studies presented in Table 1 highlight the importance of assessment for Industry 4.0. There are also other studies focusing specific technologies, for example, a reference model and roadmap for Internet-of-Things in manufacturing (Soldatos, Gusmeroli, Malo, & Di Orio, 2016), industrial internet (Menon, Kärkkäinen, & Lasrado, 2016), and cyber-physical systems (Westermann, Anacker, Dumitrescu, & Czaja, 2016). Assessing is the first step, then, it is necessary to take actions to go digital, as presented in the next section.

Run-Time of Industry 4.0: Pilot Projects in the Ceramic Industry After evaluating Industry 4.0 readiness, organizations must implement their digital strategy. Nevertheless, there are challenges for SMEs. A study promoted by the European Parliament recognizes that one obstacle to the participation of SMEs in the supply chain of Industry 4.0 is the “…capacity to run pilot projects to test out Industry 4.0 mechanisms and potentially limited access to facilities to test advanced solutions…” (Smit et al., 2016). Other barriers to develop Industry 4.0 projects in SMEs include the 1358

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lack of awareness about technologies, high investments required, the need for specialized IT staff, and the dependency from big companies (Smit et al., 2016). We also found these evidences in the ceramic industry, requiring significant efforts from governments and associations to put Industry 4.0 in the managers’ agenda.

Table 1. Models to assess Industry 4.0 maturity Industry 4.0 Assessment Model

Model Description

Model Stages

(PwC, 2017)

An online self-assessment model for industry 4.0 created by a global consulting company. It suggests to (1) conduct the online self-assessment, (2) identify needs for action, and then (3) benchmark against other companies

Includes four stages (I Digital Novice, II Vertical Integrator, III Horizontal Collaborator, and IV Digital Champion) and six dimensions, namely, (1) Business Models, Product & Service Portfolio; (2) Market & Customer Access; (3) Value Chains & Processes; (4) Information Technology (IT) Architecture; (5) Compliance, Legal, Risk, Security & Tax; and (6) Organization & Culture

(Rockwell Automation, 2014)

The Connected Enterprise Maturity Model is a practitioner’s model proposed by Rockwell Automation, a leading company in industrial automation. The model offers guidelines to implement advanced networks of operations technology (OT) and information technology (IT)

Five stages (from 1-assessment to 6-collaboration)

(Isaka, Nagayoshi, Yoshikawa, Yamada, & Kakeno, 2016)

A maturity model for production systems developed by Hitachi. It suggests the use of image analysis as a sensing technique

A plant at level 1 uses data for visualization of their site. Level 2 connection, allows product traceability and level 3 analysis, work automation and process optimization. The following stages are 4 measurement, to identify and solve production bottlenecks, 5 prediction, and the most advanced 6 symbiosis, where resources are optimized and production plans coordinated with company stakeholders

(IMPULS, 2017)

Industry 4.0 readiness self-assessment commissioned by the IMPULS Foundation of the German Engineering Federation (VDMA). It is a comprehensive model that addresses social, organizational, and technological aspects

There are six readiness levels ranging from 0 – insignificant Industry 4.0 activities to 5 - top performers

(Leyh et al., 2017)

The System Integration Maturity Model Industry 4.0 (SIMMI 4.0) considers four dimensions to assess the IT system landscape – vertical integration, horizontal integration, digital product development, and crosssectional technology criteria

Five stages that start at Stage 1 – Basic digitization level and reaches a maximum of 5 - Optimized full digitization.

(Knoke, Missikoff, & Thoben, 2017)

Collaborative Innovation Capability Maturity for virtual manufacturing enterprises

Five stages aligned with the Capability Maturity Model (Paulk, Curtis, Chrissis, & Weber, 1993)

(Schumacher, Erol, & Sihn, 2016)

Model that includes social, technical, and organizational dimensions to assess Industry 4.0 readiness in manufacturing. These authors considered a total of nine dimensions, each one calculated as a weighted average of different items (62 in total). It is a comprehensive model with radar charts to visualize data and identify improvement priorities

The stage is assessed as a continuous result from 1 to 5.

(Ganzarain & Errasti, 2016)

Focuses on the process of change in diversification strategies. It is necessary to (1) define a vision, (2) establish a roadmap, and (3) implement Industry 4.0 projects ensuring training and risk management

The first stage is 1 – Initial (inexistent industry 4.0 vision) and can reach a maximum of 5 – detailed transformation of business model.

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A tailored roadmap for Industry 4.0 implementation must involve the creation of a digital infrastructure, digital business services (Immonen, Ovaska, Kalaoja, & Pakkala, 2016) and digital business processes (Vidgen & Wang, 2006). The need to complement infrastructure investment (e.g. cloud platform, IoT) with new services and processes becomes clear with the LEGO case study presented by Andersen and Ross (2016). While the initial focus was in the creation of new software platforms and infrastructure acquisition, recent advances include the creation of new digital services accessible to customers and business partners. Yet, a roadmap that expands company borders also requires implementation mechanisms of trust, a key element to ensure cooperation between elements of the supply chain (Grzybowska, Kovács, & Lénárt, 2014) and integration, which is an essential aspect for Industry 4.0. Portuguese ceramic industry already started to implement Industry 4.0 but the context of ceramic production poses particular problems, as presented in Figure 4. Figure 4. Going digital in traditional manufacturing: The challenge of numerous products

Figure 4 present the moulding phase of the ceramic production (on the left) and a plant area that mixes moulds used in the process (bellow, on the right) and products that are still under development process. This case is specific to the table and ornamental ware ceramic subsector (other production processes such as tiles of technical ceramic have different characteristics) but it can be used to illustrate the (1) multiple product references simultaneously under production, (2) the fragile characteristics of the product (consistency of the ceramic material) in all the stages (e.g. presenting difficulties to use robots), (3) the low cost of each unit, posing difficulties, for example, in the use of traceability devices such as RFID tags, and (4) the highly manual process that is mostly supported by paper records in SMEs. In spite of the difficulties that are evident in traditional products such as ceramic, Industry 4.0 is not restricted to high tech industries. In fact, ceramic industries are emerged in global supply chains and must implement systems that adhere to the digital ecosystem needs. Moreover, the complexity to directly implement Industry 4.0 technologies (e.g. robots, simulation, mobile technologies) demands for new

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cooperation efforts between the industry, the university, ceramic associations and technological centres. The result of this cooperation is already visible and new pilot projects are under development. Table 2 presents examples of pilot projects and ideas developed in the Portuguese ceramic industry. The examples presented in Table 2 are not exhaustive; its purpose is to illustrate examples of small scale projects that are accessible to traditional industries. Lessons learned include (1) the need to joint efforts between multiple entities, (2) think about digital ecosystems and not merely internal applications of technology or isolated B2B – Business-to-Business channels, (3) explore pilot projects for horizontal/ vertical integration, increase trust amongst supply chain elements, and introduce improvements that redesign business processes. It seems very appropriate to quote Michael Hammer in this case: “don’t automate, obliterate” (Hammer, 1990).

Table 2. Industry 4.0 in the ceramic industry: pilot projects for traditional products Industry 4.0 Pilot Project

Description

Mobile Manufacturing Execution Systems (mMES)

This project started by CTCV – Technological Center for Ceramics and Glass, aims at the creation of a cloudbased MES for mobile devices. The main purpose of this system is to assist small ceramic industries (that use paper records in almost all the production stages) in digital production records allowing real-time information to the company partners (including suppliers and customers). The use of mobile technologies reduces the financial investment and simplifies the system adoption

Cloud Laboratory Information Management System (cLIMS)

Ceramic products require (internal/external) testing to ensure that product complies with regulations and customer requirements. Although large companies usually have laboratorial data digitalized, small companies continue to use disconnected spreadsheets and paper. Cloud-based LIMS allow to integrate data from external laboratories with the company data, also having specific interfaces for customers and to follow nonconformance actions

Mould digitalization

Moulds are one of the main tools for ceramic production but highly demanding of storage space. Each mould is specific to a product reference and companies consider them a valuable asset to keep. For example, if a customer asks for additional quantities, the ceramic company must use the same mould or create exact replicas. We can find cases of moulds kept for decades that are never needed again. What if we digitalize the moulds and then recreate them when needed using 3D printing devices? The idea is already under development but present difficulties, for example, there are complex moulds with multiple parts that make the “digitalization” – “printing” process costly, requiring algorithms to decide which moulds are economically viable to digitalize – recreate or keep in its materialized form

Digital energy management using simulation, cloud and IoT

Energy management systems are not new. However, most of the existing systems mainly acquire data and raises alerts of energy consumption. Simulation and integration with the production lines will provide additional functionalities to current energy management systems that now include affordable sensors and actuators

Big Data for Marketing and Internationalization

Analysis of Big Data can provide valuable information for designers. The design requirements change across the globe and over time. The potential of Big Data is now being used to assist design trends and market trends (e.g. potential increase of construction in specific countries that justify new local factories or new internalization actions)

Sensing Ceramics

Traditional products such as ceramic tiles can include sensors (e.g. temperature) to assist intelligent houses. It is possible to include new materials in ceramic, for example, sensors and solar systems, as we detail later in this chapter. The incorporation of new elements in ceramic will generate added volumes of digital information

Traceability systems

The use of QR codes in ceramic can assist production control, quality control, and provide digital information to the consumer of traditional products. Recently, a Portuguese ceramic company inserted QR codes in the final product to present specific details that may increase product value (e.g. lot information and quality characteristics, and a video about the production of that specific product reference)

Ceramic Industry 4.0 Maturity model

The creation of a sectorial maturity model for ceramic industry is under development in Portugal, involving universities, companies and the ceramic industry association. The project aims at the development of a tailored model that includes a portfolio of solutions for each ceramic subsector

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The social and environmental aspects are equally important in ceramic industry 4.0, for example, to ensure the protection of human workers in an increasingly automated production setting and protect the environment. This is the challenge that we address in the next section.

SAFETY AND SECURITY Industry 4.0 will promote changes on the way occupational safety and health (OSH) is considered nowadays. If some aspects are predictable, like safety aspects related with the increasing of robots in workplaces, others questions arising are more difficult to predict or could be speculative. In the later, it could be mentioned the psychological impact of disruptive changes in work or the decrease of human error as cause of accidents. Additionally, the dissemination of new technologies in manufacture such as additive manufacturing and nanotechnologies, occurring side-by-side with digitalization, are also factors affecting OSH. In Table 3 some aspects related to Industry 4.0 and their possible impacts on OSH are presented. Table 3. Industry 4.0 technologies/enablers and their relation to occupational safety Industry 4.0 Technology/Enabler

Impact on OSH

Robotization

Collaborative robot safety Human training relating to robots Decrease in manual work Hazardous tasks performed by robots

Internet of things

Cyber-security related to safety of cyber-physical systems

Complex embedded software

Hackers or malware as cause of malfunction leading to accidents

Big-data

Increase of capacity to deal with OSH knowledge

Sensors (widespread use)

Workplace environment continuously monitored

Augmented reality

Reduce hazards in maintenance tasks OSH information and warnings in real-time

Digitalization can contribute to increase safety and health in workplaces, reducing heavy tasks, eliminating hazardous operations and creating new prevention opportunities, like sensors or training tools. On the other hand, new risks will appear or existing ones will increase, particularly, those related with the robotization and increasing de-humanization of the work. A third category of impacts are the new challenges to OSH, in this particular, the security-safety relation. In the following items, the possible negative impacts and challenges are discussed.

Security of IT Systems and Relation With Safety The Smart Factory concept (Lucke, Constantinescu, & Westkämper, 2008) poses challenges related to the safety and security. These factors are considered critical to the success of the manufacturing digitalization and, consequently, of business success, since neither processes nor products should represent a risk to persons (including workers and consumers) or environment (Acatech, 2013). In ceramic industry,

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process safety is a key element and should not be jeopardized by IT security breaches. It is necessary that IT systems are secure against misuse and unauthorized access to prevent modifications or destruction. Besides industrial piracy or information confidentiality, issues like sabotage or terrorist acts are crucial. Even unintended acts could cause accidents if equipment controls are improperly accessed (Acatech, 2013). As a result of integration between Information and Communication Technologies (ICT) and Industrial Control Systems (ICS) vulnerability to cyber-attacks is an workplace safety issue (Steijn, Vorm, & Luiijf, 2016). Besides the known possible risk emerging from Industry 4.0 there are also other unknown risks that could emerge (Steijn et al., 2016). From the previously exposed, it is clear that the security-safety relation is more complex with Industry 4.0.

Interaction/Integration Human-Robots in the Workplace Robots are already present in industry, operating in isolated cells, both with physical and/or virtual barriers, usually avoiding the contact with workers. Considering the increase of robots in manufacturing, and most of all the increasing interaction human-robots as one of the main characteristics of Industry 4.0 (Erol, Jäger, Hold, Ott, & Sihn, 2016; Zhou, 2013) the occupational safety aspects are crucial (Fryman & Matthias, 2012). New risks are identifiable while robots will be no longer confined to a location or operating inside a protective cage (segregation paradigm) and collisions between robots and people or other hazardous events could occur (Bicchi, Peshkin, & Colgate, 2008). In a recent study, Dutch researchers identified a set of threats and vulnerabilities related with collaborative robots: change of task, unforeseen situations trust in machines, shared responsibility, regulatory gaps, non-compliance and cyber security (Steijn & Luiijf, 2016). In the same research several control measures are suggested to face the risks arising from the foreseeable interactions. The basis for safety rules and future regulations are the three laws of robotics1 defined by Isaac Asimov in 1942 (Magruk, 2016), although those could be adapted or complemented (Steijn & Luiijf, 2016).

Nanotechnologies In parallel with digitalization of industry it is also expected the increase of the use of nanomaterials. Ceramic industry is already one of the industrial sectors using several nanomaterials (DECHEMA/ VCI, 2011) to achieve products with improved proprieties, in particular photo catalytic ceramic tiles and bactericide sanitaryware (Chen & Poon, 2009; van Broekhuizen, van Broekhuizen, Cornelissen, & Reijnders, 2011). Since a nanomaterial is, in general, more hazardous than the bulk form of same chemical compound, special care should be considered during its use. There are numerous studies in nanotoxicology field pointing to possible harmful effects of nanomaterials to human health and environment (Bleeker et al., 2015). Considering the uncertainty related with nanomaterials, it is important to act with specific concerns on OSH. In short, nanotechnology should be side-by-side with nanosafety (Savolainen et al., 2013) and occupational risk management is crucial (Technical Committee ISO/TC 229, 2012). Information about OSH aspects related to nanotechnology and, in particular, exposure to nanomaterials is available from several international organizations, among others the European Commission and National Institute for Occupational Safety and Health (NIOSH) in the United States of America (European Commission, 2013; NIOSH, 2009).

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There are several methods for exposure and risk assessment methods available, both based on qualitative (Vervoort, 2012) and quantitative methodologies (Duarte, Justino, Freitas, Duarte, & Rocha-Santos, 2014), and the tiered approach for exposure assessment should be followed (Environment Directorate OECD, 2015). Recommendations for occupational risk control during nanomaterials handling are also published (Cornelissen, Jongeneelen, van Broekhuizen, & van Broekhuizen, 2011; NIOSH, 2013), being highlighted the hierarchy of controls (Technical Committee ISO/TC 229, 2012) presented in Figure 5. Figure 5. Hierarchy of controls in nanotechnology indicating the order to be followed (Technical Committee ISO/TC 229, 2012)

Additive Manufacturing The risks related to additive manufacturing depend most of all of the technology used. Emission of particles and/or vapours from the used materials are concerns common to other manufacturing technologies and the risk will depend, not only of the process but most of all, the hazardous nature of the materials. Considering different additive manufacturing technologies (Afshar-Mohajer, Wu, Ladun, Rajon, & Huang, 2015; Wong & Hernandez, 2012) it is possible to identify several risk factors, such as high temperatures, lasers of different types and hazardous chemicals (both in vapour or particulate form). Since the use of additive manufacturing technologies in ceramic industry is already a reality, in particular 3D printing of models and it is expected to increase, special attention should be given to these aspects. In recent research about exposure to both ultra-fine particles (UFP) and volatile organic compounds (VOC’s) there were found exposures of concern (Afshar-Mohajer et al., 2015; Yi et al., 2016).

Psychosocial Aspects and Risks It is expected that jobs will change and new jobs will emerge with the technological evolution (World Economic Forum, 2016). These changes will raise new challenges related to OSH, not only in consequence of differences in tasks and operations but also the way existing tasks will be performed. In

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fact, workers will interact with new interfaces and deal with increasing information. It is expected that physical demand of work will drop but psychological stress will increase (Gabriel & Pessl, 2016). The emotional and mental stress may raise with increasing flexibility in work and a diminution of communication and cooperation between employees since interaction between humans and machines will increase (Degryse, 2016; Gabriel & Pessl, 2016). Also, the constant work performance evaluation could be cause for increase of stress (Degryse, 2016). On the other hand, employees’ work-life balance could improve (Gabriel & Pessl, 2016).

Safety-By-Design Considering that process automation has been an increasing safety factor (Fadier & De la Garza, 2006), it is expected, and it is important, that safety aspects will be considered during design of Industry 4.0 factories. For the last decades, several researchers in the Safety Science field are calling the attention for the need to improve safety in workplaces considering the hazards in the design phase, developing safer equipment’s, processes or products (Fadier & De la Garza, 2006; Hale, Kirwan, & Kjellén, 2007). NIOSH, launched a campaign to reduce risks in workplaces called Prevention Through Design (Schulte, Rinehart, Okun, Geraci, & Heidel, 2008) and more recently, several researchers and institutions raised the importance of safety-by-design in the nanotechnologies field (Morose, 2010; Silva, Arezes, & Swuste, 2016). Furthermore, it is relevant to consider the concept Security by Design in development of IT systems (Acatech, 2013), for the reasons already mentioned. This section highlights the importance of combining technical and social aspects in the paths of the ongoing industrial revolution. The next section reinforces this need, including the sustainability element that affects the entire society.

Digital Sustainability The implementation of Industry 4.0 implies a digital sustainability strategy, which aims to minimize environmental impacts while improving operational measures and supporting sustainable growth. Cyberphysical systems require the analysis of ceramic production life cycle in order to optimize the economic, social and environmental risks and opportunities, with the purpose of reduce environmental impacts in all the life cycle at the previous design stage. Additionally, it is necessary to improve the operational efficiency of ceramic processes in order to reduce the depletion of natural resources, pollution and associated ecological impacts. On the other hand, it is expected to acquire more accurate and real-time information and data regarding environmental aspects and impacts on ceramic manufacturing. According to the European Commission, the construction sector is considered the highest energy consumer in EU, accounting for almost 40% of the total energy consumption and contributing almost 36% to the EU’s total greenhouse gas (GHG) emissions and produces 15% of the total industrial waste. Among the most commonly used construction materials, cement and ceramic materials are two of the most energy intensive construction materials. Ceramic products are one of the oldest building materials and generate a series of environmental impacts over their life cycle (Almeida, Dias, Demertzi, & Arroja, 2016; Quinteiro et al., 2014). Portugal is a country with a long tradition in ceramics, both in production and consumption, and is ranked as one of the top European manufacturers of ceramic products due to the high quality of raw materials. The Portuguese ceramic industry produces a variety of products adapted to building works, such as bricks, covering materials, flooring tiles, etc. This industry is responsible 1365

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for a number of environmental aspects like energy consumption (energy intensive), gaseous emissions (CO2, NOx, HF, HCl, heavy metals, particulate matter, etc), liquid emission (suspended solid, chemical oxygen demand, heavy metals) and wastes (broken ware, packaging waste, plaster moulds, sludge, etc). Assessing the environmental impacts of the different types of ceramic products has become crucial to improving the environmental performance of this sector. Such assessment can be achieved through Life Cycle Assessment (LCA) studies based on ISO 14040 (ISO, 2006a) and ISO 14044 (ISO, 2006b) standards, applied to the different stages of a product’s life cycle. Ceramic products have significant impacts during the manufacturing process, namely the firing stage of production is one of the most relevant in terms of environmental impacts. But the type of ceramic raw materials, the kiln operation conditions, electricity consumption, raw materials used and type and distance of transport are key elements that justify the variability in impacts like global warming (climate change), ozone layer, abiotic and fossil resources depletion, eutrophication, acidification, and photochemical oxidation (Almeida et al., 2016; Quinteiro et al., 2014). Cerame-Unie, representing the ceramic industry in Europe, stresses that resource efficiency requires a Life Cycle Assessment (LCA) approach that takes into account all stages of the product, including its durability, lifespan and reduction of resource consumption over the use phase. Although LCA is a recognized instrument to assess environmental impacts in industry including ceramic one, it requires a lot of environmental data, major collected manually, validation of the quality data and it is very time consumer (Almeida, Barata, Dias, & Arroja, 2015). Industry 4.0 and digital systems provide a new approach in which physical production processes and ICT grow more closely together (Gabriel & Pessl, 2016). Embedded sensors, systems, mobile devices and production facilities are integrated and able to communicate with each other via the internet, in order to monitoring and controlling the ceramic process in a transparent way. The cyber-physical systems represent a further evolution from the existing embedded systems. The same priority can be identified in direct digital manufacturing (Chen et al., 2015). On the other hand, the digital systems and the construction and operation of robots will have new environmental aspects and risks, namely the new materials that may be hazardous and potential emissions to environment (e.g. particles and volatile organic compounds (VOC’s)). These aspects must be monitored and controlled. Table 4 presents examples of possible Digital Sustainability Strategies that can be applied in the ceramic industry to achieve a sustainable development. The integration of digital sustainability strategies is a key dimension for sustainable ceramic industry and the development of “smart ceramic products”, changing to a dynamic process that entails continued improvement, diversification and industrial upgrading, and technological eco-innovation throughout the value chain.

DEVELOPING CERAMIC INDUSTRY 4.0: STRATEGIC RECOMENDATIONS A workshop involving 120 participants from ceramic industry was scheduled to February 2nd 2017, three days after the announcement of Industry 4.0 strategy by the Portuguese government. The morning session had presentations from governmental entities, Industry 4.0 experts, and industry associations. The afternoon aimed to evaluate the perception of maturity in specific dimensions of Industry 4.0 (e.g. vertical integration, competencies) and understand priorities for key processes of the industry (e.g. energy 1366

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management involving IoT and predictive algorithms). A mobile app was created to allow interaction between researchers and the workshop participants, as presented in Figure 6. Table 4. Examples of digital sustainability strategies applied in ceramic process and products Digital Sustainability Strategy Ecodesign or design for sustainability

Key Aspects Sustainability Life cycle thinking

Description Life cycle thinking – aims at the prevention of environmental impacts throughout their life cycle while eco-innovation and new business opportunities are encouraged and potential cost savings arise Digitalization of quarries (clay, feldspar, Kaolin, etc) in order to have precise information on the quantity and quality and the best way to explore the quarry and at same time the environmental recovery

Selection of lowimpact materials

Digitalization of quarries Incorporation of waste on a circular economy perspective

Reduction of the material use

Reduction in products thickness

Reduction of the environmental impact in the production phase

IT network systems High efficiency process and resource control Innovations and ceramic materials

The use of by-products by the industry or as raw material for other industries allows cost reduction facilitating the supply of raw materials and the elimination of waste deposit. Main constraints pointed out are related to quality control due to the not homogeneous composition – use of sensors and embedded systems Incorporation of industrial waste for the production of ceramic tiles, in a way that the by-product quality and quantity is continuously monitored and interact with production line in order to avoid defects. For example, a ceramic tile can reach up to 80% of recycled material by weight while retaining the strength and versatility (example: see InEDIC, 2011) There are several examples of tiles with reduced thickness and mechanical performance in buildings. IoT can contribute in this purpose, reducing tile thickness from 12 mm to 4 mm (Light ceramic floor tile by Revigres is one example, with resource efficiency through the all life cycle) The optimization of the industrial production process throughout Industry 4.0 and digital sustainability can lead to a reduction of energy, CO2 and other combustion pollutant emissions, as detailed information on each point of the ceramic production process, resource (e.g. clay, sand, feldspar) and energy use can be monitored and optimized over the entire value chain Controlling consumptions (e.g. electricity, natural gas consumption) as well as emissions (gaseous emission, liquid emission) in main unit process like spray-driers, driers and kilns in a continuously optimized IT network system Innovation regarding new bricks with high thermal, mechanical and acoustic performances that improves the energy performance of the building or multifunction products. Example: SolarTiles – Integrated photovoltaic into ceramic products for high efficiency for building coatings (roof and facade claddings) incorporating thinfilm photovoltaic cells (COMPETE2020, project 3380)

Reduction of the environmental impact in the use phase

Eco-innovations for energy performance or multifunctionality

Transport and logistics

Information systems for the distribution of ceramic products Smart mobility, smart logistics

New information systems for the distribution phase of ceramic products will make “smart mobility” and “smart logistics”

Optimizing the installations of endof-life systems of ceramic materials

Information systems for installation and dismantling

The installation of ceramic products into buildings (e.g. brick or ceramic tile) or the final disposal of products can be simplified and optimized

New materials like ceramic tiles with phase change materials (PCM) to improve thermal characteristic and energy efficiency in buildings. Project examples include ThermoCer developed by Cinca and CTCV in Portugal (QREN, project number 23143) or Selfclean (QREN 21533) - Ceramic coating with self-cleaning properties, purifying functions, high efficiency and durability, by modifying its surface with nanostructured photocatalytic materials

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Figure 6. Industry 4.0 Workshop: Mobile app support (extract)

The mobile app included the possibility to evaluate company maturity on the topics presented and to define the strategic priority of the organization, for Industry 4.0. key processes were selected (e.g. energy management, digitalization) and a discussion about possible solutions occurred (e.g. IoT for energy management, Big data for marketing). The results of voting were used for the debate and to define strategies to develop Industry 4.0 in ceramic industry, presented in Figure 7. Figure 7. Industry 4.0 Workshop: Strategic priorities for the ceramic industry (extract)

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The most disrupting topics of robotization and additive manufacturing were not considered as top strategic priorities for the ceramic industry. The workshop participants were more enthusiastic about the creation of a digital services cloud, energy management solutions, new marketing strategies using Big Data (e.g. design trends for ceramic products). The workshop discussion allowed us to confirm that: • • • •

Not all technological components associated with Industry 4.0 are relevant for their digital strategy; A higher maturity stage does not mean that the component has low priority (e.g. the participants considered to be well prepared in terms of energy management, but this topic is so important for ceramic and glass industries that they still consider it a top priority for all Industry 4.0 actions); The majority of respondents considered themselves in a positive stage (above 5 in a scale ranging from o to 10) but our field studies show a less developed scenario; To be successfully, Industry 4.0 in traditional sectors of the economy should not have a mere technological focus. It is necessary to consider social and organizational aspects, for example, safety and sustainability that we also addressed in this chapter.

As a result, we defined a strategic model towards Ceramic Industry 4.0. Our purpose with this high-level representation is to (1) highlight the importance of cooperation between different elements of the supply chain in traditional products and (2) include social, organizational, and technological elements in the company roadmap for industry 4.0. We used the model to communicate the results of our workshop with the industry. The model suggests that managers must define a multidisciplinary team and create pilot projects that address five interrelated dimensions (1) the industry context (e.g. relation with stakeholders, horizontal integration), (2) people (e.g. safety, competencies), (3) industrial process (e.g. energy reduction via IoT, digitalization via 3D printing), (4) Industry 4.0 Technologies (e.g. cloud Figure 8. Strategic model for the development of Ceramic Industry 4.0

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platforms and augmented reality systems), and (5) information/data (e.g. data quality and protection. The model is outlined in Figure 8. The model presented in Figure 8 suggests that digitalization involves several (internal/external) stakeholders of the organization. Therefore, managers should involve different experts in their pilot projects, develop internal competencies for Industry 4.0, and cooperate with their customers, suppliers, and research institutions. Industry 4.0 complexity does not recommend “Industry 4.0 ready solutions” offered by single suppliers. The model also suggests developing pilot projects within the ceramic process supply chain. For example, (1) using IoT to monitor raw material inventory directly by the suppliers, (2) adopting new robots for manufacturing, (3) use Big Data potential to identify trends in global markets (e.g. design trends that are so crucial to create products that adhere to architecture movements), (4) implement augmented reality to support product selection by the end users or cloud platforms to simplify data integration. Industry 4.0 technologies will generate large amounts of information/data and use a plethora of technologies (e.g. sensors, algorithms, standards) that evolve faster than ever. More data also represent additional responsibilities such as data protection measures and responsible use of resources (e.g. energy reduction). Therefore, academic partnerships with the industry are essential to get access to emerging developments and lead the industrial revolution. The simplified model offers guidelines for industrial managers. First, it recommends creating knowledge networks with multiple stakeholders. Horizontal and vertical integration is not possible to achieve in isolation from the supply chain partners and regulatory bodies. Second, it highlights the need to address multiple points of the value chain, once again, involving internal and external stakeholders, for example, when drastic changes in the process creates the possibility to decentralize production in multiple small units around the globe. Third, Industry 4.0 enablers are complex and require specialized staff in the organization. Examples include new skills in Industry 4.0 technologies and a professional structure to deal with information/data (e.g. protects data vulnerabilities, comply with regulations, generate value with the data, for example, with data scientists). The non-strict vision of industry 4.0 as a mere technological investment can help managers to avoid what Arvidsson, Holmström, and Lyytinen (2014) named as “… strategic blindness: organizational incapability to realize the strategic intent of implemented, available system capabilities…” Next, we explain the avenues for future research that captured the attention from the managers in the Portuguese ceramic industry.

FUTURE RESEARCH DIRECTIONS Digital ecosystems can determine the survival of the factories of the future. Current research in this area is changing the landscape of production processes, using decentralization (Brettel & Friederichsen, 2014) and new platforms connecting multiple elements of the supply chain. To compete in global markets, companies must prepare their digital infrastructure and create digital services and processes to comply with the requirements of their customers. There are also challenges that include data quality (essential in integrated systems), data protection and privacy, and regulations that improve interoperability between different systems. Current maturity models to assess Industry 4.0 are too generic to be useful and most of these tools are still under development. It is necessary to create tailored maturity models for different sectors of the economy (Barata & Cunha, 2017). This evidence emerges when we address specificities of each 1370

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economic sector; one approach to Industry 4.0 in automotive or aeronautical does not necessarily represent an optimum solution in traditional sectors of the economy. Future research is needed to test the effectiveness of the maturity models in practice, the managers’ adherence and the utility of the tools to assist roadmaps implementations - not merely as a diagnosis tool. There are many challenges and opportunities in the security and safety fields in the ceramic industry. Cooperative robot safety and cyber-security are already identified as relevant issues. Also, the additive manufacturing occupational risks and nanotechnologies need special attention from the OSH researchers. The development of sensors to monitor chemical, physical and biological contaminants in workplace environment and to monitor workers biological parameters is another relevant area of future research. The uncertainty related to the role human work in manufacturing industry in the future poses questions in research fields like ergonomics and human factors or psychosocial risks. The new professions arising from the industrial changes will need new OSH paradigms. The emergence of the Internet of Everything (connected objects and people) will generate vast amounts of data that will help us understand more about the way we interact with each other and improve sustainability in ceramic processes. Moreover, the increasing number of sensors and connected devices puts energy management on the top of researchers’ priorities. The challenge to digital sustainability is to identify transformation potential including ways of interacting in a manner that promotes the reduction of the energy consumption, gaseous and liquid emission and waste prevention. On the other hand, relevant research, education, and political constraints (e.g. limiting energy production up to the precise needs of consumers, forecasts of natural events or disasters, preventive maintenance) regarding the sustainability impacts of Industry 4.0 will have to be developed. If sustainable practices are seen as value added to the ceramic process that will act as drivers to sustainable development and eco-innovation, On the contrary, if they are merely seen as a cost burden or a constraint to business and innovation, they will not be successful. New economic and social models will be developed based on a key principle of sustainability.

CONCLUSION Industry 4.0 affects all sectors of the economy, yet, traditional manufacturing industries face additional challenges and require specific solutions. This chapter addresses three key dimensions of Industry 4.0 in the context of ceramic production, namely, digital ecosystems, safety and security, and digital sustainability. In each case we include examples that are under development in industry, progressively changing the business strategy, the social, and the technological landscape of ceramic production. Additionally, strategic recommendations for Ceramic Industry 4.0 are provided. The guidelines emerged from a recent workshop with 120 participants and preliminary results of research projects in ceramic industry. There are also limitations to our study that must be stated. Industry 4.0 is a vibrant area of research and many other examples could be important for traditional products. The challenges and recommendations are well supported by theory and practice in the Portuguese ceramic industry, but each sector of the economy can have specificities to consider. For this reason, it is advised to tailor generic models to the need of each economic sector and also to the need of each company. The strategic model presented in this chapter aims to assist industry managers to evolve in Industry 4.0, ensuring that a comprehensive approach is selected, yet, it is a simplification of the complex reality of Industry 4.0. This chapter can assist researchers in the identification of new opportunities for the industry of traditional products. Small changes can have a major impact in less developed industries. For managers, this 1371

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chapter presents a multidimensional perspective of Industry 4.0 challenges in ceramics, real examples that can inspire pilot projects, and strategic recommendations for the vision of Ceramic Industry 4.0.

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PwC. (2016). Industry 4.0: Building the digital enterprise - Engineering and construction key findings. Retrieved from https://www.pwc.com/gx/en/industries/industries-4.0/landing-page/industry-4.0-buildingyour-digital-enterprise-april-2016.pdf PwC. (2017). Industry 4.0 - Enabling Digital Operations Self Assessment. Retrieved from https://i40self-assessment.pwc.de/i40/ Quinteiro, P., Almeida, M., Dias, A. C., Araújo, A., & Arroja, L. (2014). The Carbon Footprint of Ceramic Products. In Assessment of Carbon Footprint in Different Industrial Sectors (Vol. 1, pp. 113-150). doi:10.1007/978-981-4560-41-2_5 Rockwell Automation. (2014). The Connected Enterprise Maturity Model (CIE-WP002-EN-P). Retrieved from http://www.google.pt/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact =8&ved=0ahUKEwjvs5Dm75vSAhVIbBoKHS5VDTUQFgglMAA&url=http%3A%2F%2Fliterature. rockwellautomation.com%2Fidc%2Fgroups%2Fliterature%2Fdocuments%2Fwp%2Fcie-wp002_-en-p. pdf&usg=AFQjCNFIehm Savolainen, K., Brouwer, D., Fadeel, B., Fernandes, T., Kuhlbusch, T., & Landsiedel, R. … Pylkkänen, L. (2013). Nanosafety in Europe 2015-2025: Towards Safe and Sustainable Nanomaterials and Nanotechnology Innovations. Helsinki: Finnish Institute of Occupational Health. Schulte, P. A., Rinehart, R., Okun, A., Geraci, C. L., & Heidel, D. S. (2008). National Prevention through Design (PtD) Initiative. Journal of Safety Research, 39(2), 115–121. doi:10.1016/j.jsr.2008.02.021 PMID:18454950 Schumacher, A., Erol, S., & Sihn, W. (2016). A Maturity Model for Assessing Industry 4.0 Readiness and Maturity of Manufacturing Enterprises. Procedia CIRP, 52, 161–166. doi:10.1016/j.procir.2016.07.040 Schwab, K. (2015). Will the Fourth Industrial Revolution have a human heart? Retrieved December 9, 2016, from https://www.weforum.org/agenda/2015/10/will-the-fourth-industrial-revolution-have-ahuman-heart-and-soul/ Silva, F., Arezes, P., & Swuste, P. (2016). Systematic design analysis and risk management on nanoparticles occupational exposure. Journal of Cleaner Production, 112, 3331–3341. doi:10.1016/j.jclepro.2015.11.001 Smit, J., Kreutzer, S., Moeller, C., & Carlberg, M. (2016). Industry 4.0 - Study for the ITRE Committee. Retrieved from http://www.europarl.europa.eu/RegData/etudes/STUD/2016/570007/IPOL_ STU(2016)570007_EN.pdf Soldatos, J., Gusmeroli, S., Malo, P., & Di Orio, G. (2016). Internet of Things Applications in Future Manufacturing. In Digitising the Industry. Internet of Things Connecting the Physical and Virtual World (pp. 153–183). River Publishers. Steijn, W., & Luiijf, E. (2016). Emergent risk to workplace safety as a result of the use of robots in the work place. No. TNO 2016 R11488. TNO. Steijn, W., Van Der Vorm, J., & Luiijf, E. (2016). Emergent risks to workplace safety as a result of IT connections of and between work equipment. No. TNO 2016 R11143. TNO.

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Technical Committee ISO/TC 229. (2012). ISO/TS 12901-1: Nanotechnologies — Occupational risk management applied to engineered nanomaterials — Part 1: Principles and approaches. Geneva: ISO. van Broekhuizen, P., van Broekhuizen, F., Cornelissen, R., & Reijnders, L. (2011). Use of nanomaterials in the European construction industry and some occupational health aspects thereof. Journal of Nanoparticle Research, 13(2), 447–462. doi:10.100711051-010-0195-9 Vervoort, M. B. H. J. (2012). A comparison of risk assessment methods in order to determine the risk of occupational used nanomaterials in a research environment. NSPOH. Vidgen, R., & Wang, X. (2006). From business process management to business process ecosystem. Journal of Information Technology, 21(4), 262–271. doi:10.1057/palgrave.jit.2000076 Wang, J., Huang, J., Chen, W., Liu, J., & Xu, D. (2016). Design of IoT-based energy efficiency management system for building ceramics production line. In Proceedings of the 2016 IEEE 11th Conference on Industrial Electronics and Applications, ICIEA 2016 (pp. 912-917). 10.1109/ICIEA.2016.7603712 Wang, L., & Alexander, C. (2016). Additive Manufacturing and Big Data. International Journal of Mathematical, Engineering and Management Sciences, 1(3), 107–121. Retrieved from http://ijmems. in/assets/2.-ijmems-16-015-vol.-1,-no.-3,-107–121,-2016.pdf Weill, P., & Woerner, S. L. (2015). Thriving in an Increasingly Digital Ecosystem. MIT Sloan Management Review, 56(4), 27–34. doi:10.1287/isre.1100.0318 Westermann, T., Anacker, H., Dumitrescu, R., & Czaja, A. (2016). Reference Architecture and Maturity Levels for Cyber-Physical Systems in the Mechanical Engineering Industry. In International Symposium on Systems Engineering (ISSE) (pp. 1–6). Edinburgh, Scotland: IEEE. 10.1109/SysEng.2016.7753153 Wong, K. V., & Hernandez, A. (2012). A Review of Additive Manufacturing. ISRN Mechanical Engineering, 2012, 1–10. doi:10.5402/2012/208760 World Economic Forum. (2016). The Future of Jobs. Yi, J., LeBouf, R. F., Duling, M. G., Nurkiewicz, T., Chen, B. T., Schwegler-Berry, D., ... Stefaniak, A. B. (2016). Emission of particulate matter from a desktop three-dimensional (3D) printer. Journal of Toxicology and Environmental Health. Part A., 79(11), 453–465. doi:10.1080/15287394.2016.116646 7 PMID:27196745 Zhou, J. (2013). Digitalization and intelligentization of manufacturing industry. Advances in Manufacturing, 1(1), 1–7. doi:10.100740436-013-0006-5

KEY TERMS AND DEFINITIONS Ceramic Industry 4.0: The ongoing initiatives for digital transformation in the Portuguese ceramic sectors of the economy. The roadmap includes social, technical, and organizational changes that are necessary to compete in global supply chains.

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Digital Ecosystem: Socio-technical system inspired in natural ecosystems that connects a group of companies/people/things via digital platforms. It requires a digital infrastructure and digital services to interact with external parties of the organization. Similarly to natural ecosystems, sustainability and safety are critical aspects. Digital Sustainability: The opportunities raised by digital transformation to meet the sustainability goals and reduce the carbon footprint. Life Cycle Assessment: Assessment of the environmental impacts applied to the different stages of a product’s life cycle. Maturity Model: A tool used to assess the current state of an organization in a specific context of analysis. This type of models is also used to communicate best practices and guide organizational improvements. Nanosafety: The different techniques, tools, and approaches related to the safety of nanotechnology. It involves the policies, standards, and research needed to ensure the proper development and use of nanomaterials in the factory of the future. Safety-By-Design: The use of methods in early stages of the product life cycle to minimize hazards and comprehensively improve health and safety.

ENDNOTES 1



First Law: a robot may not injure a human being or, through inaction, allow a human being to come to harm. Second Law: a robot must obey the orders given it by human beings, except where such orders would conflict with the First Law. Third Law: a robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.

This research was previously published in Technological Developments in Industry 4.0 for Business Applications; pages 278303, copyright year 2019 by Business Science Reference (an imprint of IGI Global).

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Chapter 69

Industry 4.0 in Cultural Industry:

A Review on Digital Visualization for VR and AR Applications Antonios Kargas https://orcid.org/0000-0001-6157-1761 National and Kapodistrian University of Athens, Greece Dimitrios Varoutas https://orcid.org/0000-0002-9101-6221 National and Kapodistrian University of Athens, Greece

ABSTRACT This chapter enlightens how Industry 4.0 is gradually implemented in Cultural Industry. Even though Industry 4.0 started from manufacturing, it soon expanded to less technologically consuming industries, such as the Cultural, creating new opportunities especially in the field of Virtual Reality and Augmented Reality technologies. Taking into account existing research on Industry 4.0 and its main technologies and existing research and projects on Cultural Heritage’s aspects related with the 4th Industrial Revolution, the chapter investigates how Industry 4.0 is implemented into Cultural Sector from a technological point of view, but moreover to investigate its potential role.

INTRODUCTION The 4th Industrial Revolution, also known as Industrial 4.0 has already made its presence noticeable. It gives its signs through a growing tension of digitizing everything, alongside with technologies that aim to effectively capture and quickly analyze amounts of real-time data, in order to deliver various types of meaningful information. Following this, technological research targets on developing accurate, easy to

DOI: 10.4018/978-1-7998-8548-1.ch069

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 Industry 4.0 in Cultural Industry

implement / use and cost-effective technologies, such as artificial intelligence, analytics, internet of things’ technologies, automation, machine learning and others (Liao, Deschamps, Loures, & Ramos, 2017). As a result of its ongoing evolve it is difficult to give a widely accepted and holistic definition of what Industry 4.0 will become. At present, it seems more like a “marriage” between the physical world/ sciences with digital technologies. Digital technologies offer new ways of interconnection with “physical”, effective data collection and wise systems capable to interpret the gathered data for a more holistic, informed decision making (action back to the physical world). This procedure is challenging the current way that professionals and scientists think, work and use data, to provide added-value services/ products and new business models. From the other hand, the physical world is not only the use case of digital technologies but moreover an endless source of inspiration for creating virtual worlds or to augment the physical one (Roblek, Meško, & Krapež, 2016). Even though Industry 4.0 has started from manufacturing, soon has been expanded to several areas, such as supply chain, resources’ industries and energy, transportation, healthcare, and more others. Even cultural sector, usually a less technology consuming industry, has already started to implement Industry 4.0 technologies, such as Augmented Reality (AR) and Virtual Reality (VR) technologies, which have rapidly emerged the last years creating a dynamic environment with great opportunities in “3D reconstruction” of cultural monuments and historical cities, that do not longer exist or have been modified (merely or totally). Taking into account that back in 2000, 3D visualization of cultural content was just a tool to digitally replace physical (e.g. damaged or missing) artifacts (Novitski, 1998), it is notable that digital technologies have nowadays developed enough sophisticated tools to create realistic objects and environments (Ch’ng, 2013) in order to offer a much richer user experience. The idea of visualizing three-dimensional (3D) context is gaining pace from both technological and cost-developing aspect, while a wide range of usages is now established. From research and educational orientation to entertainment and business purposes (Greengrass & Hughes, 2008), 3D models can visualize historic artifacts but moreover can “time-travel” users to historical places/cities/ buildings. Virtual Reality and Augmented Reality technologies can embody users in a specific historic period delivering a feeling of how daily life was, in various aspects from walking to the city, or visiting a temple, to more complex tasks such as politics and war. The interesting on such approaches and technologies is revealed from a series of research projects funded from the European Union the last 15 years. These projects mainly focused on three distinct areas of interest: 1. archaeological excavations where the public access is limited and there is limited physical content to Be Actually viewed, 2. historical places that do not longer exist or have been modified and 3. Monuments, sculptures and artifacts with limited access or no longer existing. Existing research coming from these projects reveals a growing interest in model developing for existing and a smaller for non - existing cultural heritage’s artifacts/monuments/ buildings. The main target is to create Virtual Reality or Augmented Reality objects for presentation (Münster & Koehler, 2016), while a more holistic approach is needed to converge with Industry’s 4.0 philosophy. Current research aims (a) to expand cultural sectors’ personnel on “Industry 4.0” by presenting its general framework (Background Section). In order to understand the existing impact of “Industry 4.0” on the cultural sector (b) the most used technologies will be discussed and (c) a series of already 1380

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implemented projects will be revealed. Finally, (d) a technological scenario will be presented targeting on expanding used technologies in museums and archaeological places following the perspectives of the fourth industrial revolution.

BACKGROUND OF INDUSTRY 4.0 Each one of the last 3 centuries brought an industrial revolution, capable to transform the whole economic landscape, in terms of (a) jobs created and abolished, (b) technologies and processes massively introduced and (c) an overall societal change. Starting in the late 18th century, the First Industrial Revolution gradually transformed agrarian and rural societies into the industrialized and urbanized world. Steam power engines led to electricity’s implementation and to more sophisticated machinery which replaced manual labor. The Second Industrial Revolution began in the late 19th century and is known as “Technological Revolution”. Inventions reshaped not only the industrial zone, but moreover everyday life. Light bulbs, telephone, cars and new transportation means made life easier and interconnected people – cities – countries, while trade extended its geographical boundaries (Lukac, 2015). All these become feasible alongside with new sources of energy, such as gas, oil and massive electricity’s production. In the industrial era, Henry Ford implemented a whole new process (moving assembly line), introducing the meaning of mass production and economies of scale to the rest of the world. The Third Industrial Revolution has its origins in the rise of information technologies and the widespread of electronic systems as a means for high-level automation. Telecommunication systems, microprocessors and of course personal computers introduced not only a different way of producing and (co) working but moreover a new way to live. These were the basis for the Fourth Industrial Revolution, the so-called Industry 4.0 which can be epitomized as the convergence of the separating lines between digital, physical and biological by using cyber-physical systems (CPS) and dynamic data processing (Petrelli, 2017) and leading to significant “game” changes not only in production but moreover to distribution, use and disposal of good and services (Lu, 2017). As a concept, Industry 4.0 raises its origins from German Federal Government which initially announced it in 2011 (Kagermann et al., 2013) as a key initiative for the development of the whole national economy (Roblek et al., 2016). Since then a growing number of researchers and professionals focused on the topic (Lu, 2017), describing various of its aspects and principles (Bauernhansl, ten Hompel, & Vogel-Heuser, 2014). Even though the growing interest it is accepted from researchers and key promoters (e.g. Industrie 4.0 Working Group and Plattform Industrie 4.0) that there is not a wide accepted definition (Hermann, Pentek, & Otto, 2016) but a rather evolving vision, some selected scenarios and many promising technologies (Bauernhansl et al., 2014; Plattform Industrie 4.0, 2014). It is worth mentioning that research initiatives in the topic raised funding of more than 200 million euros from German governmental bodies (Drath & Horch, 2014), while first recommendations were only published in April 2013 (Kagermann et al., 2013). At the same time comparable ideas gained interest in different areas (geographical and thematic), even though a different but similar definition is given: (a) “Industrial Internet” is promoted by General Electric (Evans & Annunziata, 2012) and “Advanced Manufacturing” is funded by U.S. Government (President’s Council of Advisors on Science and Technology, 2014), while similarly used terms are “Smart Industry” (Davis, Edgar, Porter, Bernaden, & Sarli, 2012;

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Wiesmüller, 2014) and “Integrated Industry” (Bürger & Tragl, 2014). All these reveal the expansion of Industry 4.0 as far as it concerns its goals and features. Taking into consideration one of the first scenarios (Wahlster, 2013) about how Industry 4.0 will transform social life and business environment, the reader can see how multiple users/consumers personal choices are transformed to an optimized process for the supply chain or the packaging. Sensors, actuators and microprocessors can be used to transform objects into smart objects. These objects are not only digital devices, but common, everyday objects augmented with the above-mentioned digital technology. The IoT connects smart objects that have common goals with an online database which tracks and collects data from the real world through Cloud Computing technologies. These data can be used in various ways including (indicative): (a) to change smart objects’ behavior (new goal to serve), (b) redesign a product / service according to users measured needs/desires in a decentralized procedure that can be dynamically reconfigured when needed (Löffler & Tschiesner, 2013), (c) creating the framework for bespoke design focusing on values and experiences of users instead of typical production optimization. These features create the vision of products/services with embedded knowledge, capable to rearrange their characteristics, production, and distribution (with minimal human intervention) according to their tracked lifecycle and customer use (Hermann et al., 2016). Industry 4.0 aspire to keep mass production’s best elements (e.g. reliability and quality) and transform its process in order to meet mass customization on demand (Petrelli, 2017) creating unique designs (e.g. product/services’ characteristics according to grouped customers’ needs) to offer different user experiences.

“INDUSTRY 4.0” IN CULTURAL SECTOR Current State of Industry’s 4.0 Implementation in the Cultural Sector Museums and archaeological places have not remained unattached from the arrival of technologies such as the World Wide Web and the development of Web Pages (and social media in a second stage). Even though their adjustment on new technologies has not been as rapid as it could, museums soon enough realized the potentials of digital technologies on reaching a wide public and increase their attractiveness. Following this perspective, digital technologies were mainly used as a (a) low-cost, (b) fast and (c) effective (user-friendly) communication tool (Sylaiou, Liarokapis, Kotsakis, & Patias, 2009). Mobile phones and tablets changed the way “users” were educated to use interactive technologies in their everyday life, including visits to cultural places. Such an evolvement created the need for more dynamic and interactive applications (Hin, Subramaniam, & Aggarwal, 2003) rather than the static, promotion context used until then (mainly text and photos). Especially smartphones seem to have radically changed both hardware features and software capabilities (Kim et al., 2014), offering a variety of new services and potentialities to cultural organizations. Such changes enabled museums and archaeological organizations to think about reinventing themselves in more digital involving ways. Their main interest lies in how to (Loumos, Kargas, & Varoutas, 2018): 1. To reuse its exclusive digital content, 2. To exploit its content for mass markets at a global level and 3. To further enrich users’ experience.

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Following these aspects, museums and archaeological organizations nave been proved eager to adopt technologies that promote their context and create a rich environment for end users. Such technologies have been proved to be Augmented Reality (AR) and Virtual Reality (VR) technologies. How these technologies better serve the cultural sector can be described via the virtuality continuum that is described in Figure 1 and range perceived “experience” from real to virtual environment. Figure 1. Virtuality continuum

(Muikku & Kalli, 2017)

From the real, physical world from the one side, someone can be placed to the other side where the virtual environment exists. The mean to fill this gap is a variety of existing technologies. Starting from the physical world, someone can experience Augmented Reality’s potential by using mobile devices (such as smartphones and tablets) or wearables (such as special glasses). This kind of technology aims to expand the physical world by adding rich content. For example, it has been used to give a digital sense of how merely destructed historical buildings could be (e.g. 3D models of not existing, or merely existing ruins on archaeological places). Its use is heavily associated with the physical place itself, meaning that users cannot take advantage of this technology in their own place but instead they can reach the content only by visiting the archaeological place. In a more complex situation, Augmented Virtuality comes to bring to a virtual environment, digital content from the real world. This case is more often experienced in popular, widespread games where virtual worlds are embodied with accurate information, representations, reconstructions or historical data coming from the real world. Both these cases give the user a clear statement about the “world” that he exists. Real world (augmented with virtual data) or virtual world (including data from the real world), have clear borders in terms of environment and content. The borders between real and virtual are fused when the user gets in Virtual Reality (Virtual Environment) where a pure artificially created environment tries to move the user in a new world, totally different from the known and the used graphics lead him to deepen his experience in the borders of forgetting the real world. Technologies from the virtual reality field aim to represent movement in the real world, while research targets on giving the user as many senses as possible in order to deepen the experience. All these technologies created a dynamic environment capable to mix reality with virtual elements or bring virtual worlds much closer to reality. Following this perspective, Mixed Reality can be defined as the total spectrum of varied mixes between “reality and virtuality”.

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Both Virtual Reality and Augmented Reality Technologies have been subject of huge, research investments coming from market leaders such as Google, Facebook and HTC. Their implementation cost has been greatly reduced in the last two years, while it is expected in the next 5 years the proposed cost - reduction and their ease of use will permit to reach mass adoption. Museums and archaeological sites have already proved willing to adopt these technologies as a result of their advantages in aspects such as (a) access, (b) content and (c) user’s experience. Starting from access it is worth mentioning that smartphones already support applications for such emerging technologies. As far as Augmented Reality applications are concerned the main access requirements consist of the existence of a mobile device and high - speed internet. Smartphones released museums and archaeological places from investing on tablets, where limitation existed on the number of devices required and power – charging problems. Nowadays cultural places are mainly concerned about internet capacity, while each user can download and use the application from its own device. VR’s main advantage is the possibility to reconstruct and make accessible to visitors, sites that no longer exist or sites that are inaccessible. Accessibility obstacles can be physical, geographical, legal or even matters of choice (e.g. cultural items belonging to private collections). Moreover, the content has been the second reason leading museums and archaeological places in using Virtual Reality and Augmented Reality applications. Having a vast amount of information, knowledge and expertise in many fields, cultural organizations realized the usefulness of reuse these assets in order to expand their digital presence. This kind of digital content (VR and AR technologies) is quite impressive in terms of graphics and design details. Finally, the users’ experience and their satisfaction from VR and AR technologies is the last main reason for implementing these technologies. It is not only about visual quality and integration of human senses into the digital virtual world, but moreover, it has to do with the interaction and the fancy result. The following Figure presents the main characteristics of Virtual Reality and Augmented Reality technologies. Figure 2. Main characteristics of virtual reality and augmented reality technologies

(Loumos et al., 2018)

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Cultural Projects With “Industry’s 4.0” Technologies Existing literature discussing projects and applications on cultural sector originate back to 2000 while it can be divided into (a) generic articles concentrating mainly on theoretical and methodological research and (b) specific case studies from research on the field. The first attempts were concentrated on how interactive technologies could be implemented in museums (Grinter et al., 2002; Sparacino, Davenport, & Pentland, 2000), or interactive exhibits (Bowers et al., 2007; Brown, Maccoll, Chalmers, & Galani, 2003). From the other hand, case studies mainly incorporate (a) archaeological excavations where VR and AR technologies offer “view” in sites totally ruined or with limited access, (b) historical sites heavily modified or ruined and (c) monuments/artifacts no longer existing. SHAPE (Hall et al., 2002) was one of the first projects mainly used as an educational tool on archaeological artifacts and their history. It soon followed the 3DMURALE project aiming at the development of 3D multimedia tools for archaeological places, creating the framework for encoding, reconstructing and visualizing the ruins (Cosmas et al., 2001). Ename 974 was another project taking his name from the archaeological site of Ename, while it focused on multimedia and virtual reality technologies usage for the reconstruction of historical remains (Pletinckx, Callebaut, Killebrew, & Silberman, 2000). The ARCHEOGUIDE project (Augmented Reality-based Cultural Heritage On-site GUIDE) was one of the first Augmented Reality implementations, providing AR reconstructions of ancient ruins based on user’s positioning and real-time image rendering (Stricker, Stricker, Dähne, Seibert, & al., 2001). Other VR and AR applications coming from the cultural sector is the development of an ancient theater (Valtolina, Franzoni, Mazzoleni, & Bertino, 2005), an AR guide for navigation to exhibition and to support basic information about the cultural items (Miyashita et al., 2008), the recreation of Ancient Rome and its main documented buildings (Circus Maximus, Colosseum, etc.) (Dylla, Muller, Ulmer, Haegler, & Frischer, 2009). The Archeomatica Project (Sangregorio, Stanco, & Tanasi, 2008) is another interesting case presenting Minoan civilization and culture. The project revealed the significance of 3D models in the process of archaeological interpretation, while the project involved cooperation between experts of information technology and archaeological research. Moreover, the results indicate that digital upgradeable archives with 3D models is an important alternative to traditional graphic and photographic documentation (Sangregorio et al., 2008). Another important project involves the development of Augmented Reality versions of archaeological artifacts from Syracuse, Italy and testing on mobile devices (Stanco, Tanasi, Gallo, Buffa, & Basile, 2012). The novelty of the proposed work lies on involving non – technical staff in the philosophy and use of virtual archaeology. Augmented Reality guides and applications have been developed for several other historical places, including among others Arbela in Iraq (Mohammed-Amin, Levy, & Boyd, 2012), Sutton Hoo in the United Kingdom (Angelopoulou et al., 2012), the Temple of Debod (Gutierrez, Molinero, Soto-Martín, & Medina, 2015) and Knossos in Greece (Galatis, Gavalas, Kasapakis, Pantziou, & Zaroliagis, 2016). From a Virtual Reality perspective some indicative projects include a 3D virtual museum tour of the Santa Maria Della Scala (Fineschi & Pozzebon, 2015), 3D models for the Prague (Prague (City of), 2018), Hamburg (Kersten, Keller, Saenger, & Schiewe, 2012) and Marsal, Aire sur la Lys and SaintOmer (Chevrier, 2016). Moreover, in some cases, researchers put emphasis on recreating buildings and offer the virtual experience to the user, with an indicative paradigm coming from the no – longer existing Geguti Palace (Ferrari & Medici, 2017).

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All these studies, as well as other core applications for Augmented Reality in Cultural Sector (D’Auria, Mauro, Calandra, & Cutugno, 2015; Damala, Hornecker, Van Der Vaart, Van Dijk, & Ruthven, 2016), have been proven to be: reconstruction of buildings / artifacts, provision of supplementary data and user’s experience enrichment. From Virtual Reality’s perspective, the cultural sector uses its technologies mainly for virtual reconstruction, educational purposes and the development of virtual museums (Gonizzi Barsanti, Caruso, Micoli, Covarrubias Rodriguez, & Guidi, 2015; Pietroni, Pagano, & Rufa, 2013). Small organizations have not the financial capabilities and often have a fear on making large investments in AR without a proof of concept due to the risk of failure (Tom Dieck & Jung, 2017), even though results indicate that AR investments can have a positive impact in various aspects regarding visitor satisfaction, market – attractiveness, learning involvement and word-of-mouth effects (Tom Dieck & Jung, 2017). Authors had their own experience from 3D reconstructing of various historical buildings and monuments coming from the 19th century in Nafplio City. Their experience came from a European Commission co-funded project in the framework of PA 2007-2013 which was implemented by the “V. Papantoniou” Peloponnesian Folklore Foundation (project’s beneficiary) in Nafplio. As part of the project, a series of 3D reconstructions were developed, based on original architectural drawings of buildings and monuments as it were back in the 19th century. Figure 3. Venetian palace

(Kargas, Loumos, & Varoutas, 2019)

Drawings, paintings, gravures, maps, images, photographs and historical documentation were used in order to develop 3D reconstructions. Each one supported differently the whole procedure and supported the final result. Drawings, gravures and paintings were useful in revealing unknown details for existing buildings/monuments or even give facts about the whole urban structure of the location. Historical research to existing documentation, literature and bibliography revealed interventions mainly conducted to historical buildings. Maps give evidence about buildings’ interconnection and how the city’s social structures were shaped. Finally, images and photographs (mainly coming from early 20th century) supported the overall findings or even permitted external interventions recognition. In order to achieve a realistic 3D reconstruction, the principles of diachronic reconstruction were used (Guidi & Russo, 2011; Micoli, Guidi, Angheleddu, & Russo, 2013) via a three – dimensions survey in order to understand buildings/monuments (a) current state (existing versus not any – more existing buildings/monuments), (b) interventions coming from different ages (Venetian, Ottoman and Greek rules) and (c) conducted historical research.

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Figure 4. Fragkoklissia church (Kargas et al., 2019)

It is worth mentioning that 3D reconstructions were developed as part of an educational game. Gamifying cultural heritage can become a fascinating methodology for introducing Galleries, Libraries, Archives and Museums (GLAM) into “Industry 4.0”, while opportunities exist on reusing digitized content. Such a tension can be further facilitated as a result of (a) the existing large – scale digitization that cultural sector has in terms of content (Sotirova, Peneva, Ivanov, Doneva, & Dobreva, 2012), (b) already solved operational problems such as standards for metadata descriptions (Bontchev, 2012) and (c) technological advantages in areas such as Virtual Reality and Augmented Reality (Loumos et al., 2018).

How to Successfully Implement Industry 4.0 to Cultural Industry Industry 4.0 aims to deliver “fundamental improvements to the industrial process involved in manufacturing, engineering, material usage and supply chain and life cycle management” (Kagermann et al., 2013) by enabling the communication between people, machines and resources. The whole idea is based on (a) embedding networks and computers to physical process, in order: (a1) to support unique identification, (a2) to collect, store and analyze data and finally (a3) to create networks from physical processes to computation (e.g. structured information) and vice versa (e.g. processes reengineering) (Lee, 2008). This side of Industry 4.0 is the so-called Cyber-Physical System (CPS) and it aims to the fusion of physical and virtual world (Kagermann, 2014). Another side is the (b) integration of Internet of Things (IoT) into various manufacturing and business processes/operations in order to allow “things” such as mobile devices, sensors, RFID and actuators to (b1) interact and (b2) cooperate to (b3) reach common goals (Giusto, Iera, Morabito, & Atzori, 2016). Industry 4.0’s main goal at the time is to achieve the integration between physical, machinery and devices (CPS) with networked sensors and software (IoT), creating complex but accurate systems capable to predict, plan and control societal and business outcomes (Industrial Internet Consortium, 2013). By incorporating both business and societal aspects to the Fourth Industrial Revolution’s outcomes the boundaries of expected change elevate to business sector that lie out from manufacturing (such as

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transports and logistics), while in societal level aspects of everyday life (such as cultural and tourism activities) will embed technological elements and change how both services providers and consumers think and act. By accepting so, authors recognize that gradually Industry 4.0 can and will find apply to any business sector and societal activity where internet and embedded systems can serve as a backbone to integrate physical objects, human actors, intelligent machines, production lines and processes in order to develop a new agile, networked and intelligent value chain (Schumacher, Erol, & Sihn, 2016). In order to successfully implement Industry 4.0 to the cultural sector, it is important to understand the existing environment. First of all, terms such as “digital strategy” is not far from what museums are already developing. Starting from a point where “digitization” was the key element of such a strategy, museums passed to point where more and more internal procedures are based to digital content. For example, nowadays organizing an exhibition, evaluating an asset or even the choice for an acquisition can be done in both physical and digital level. Moreover, many museums gave their digital archives to the public either via friendly – user display system (e.g. digital catalogs), either by developing promotional, digital application for the web or mobile devices. Internet art has become popular, leading museums to create unique digital collections as supplementary content to their physical exhibitions. Communication tools and social media have already started to play a significant role the in cultural sector’s operation. Cultural organizations can instantly learn more about their public and its preferences, during a visit or afterward. Moreover, they can adjust cultural content according to each visitor characteristics (from demographic characteristics to more complex one like interests and education) creating a more personalized communication or even experience. By creating such a link, personalized communication will permit to a future level the adjustment of visitors’ experiences (both physical and virtual visitors) according to their needs by combining cognitive and emotional content. Communication tools can play the role of the “mean” for big data analysis and user profiling according to visitors’ preferences and opinions ex – ante, during the visit and ex – post. By such a procedure, visitors are transforming to followers, while cultural organizations can share knowledge and activities to a larger audience according to its preferences. These preferences can involve actions and information about cultural aspects that until recently were excluded from communication tools. Everyday life in a cultural organization has valuable information for the audience, bringing them closer to where exhibitions are developed, artwork is generated or the environment were cultural content in cultivated. Exchanging ideas about the personal meaning of culture permits the involvement of large audience to areas that non – experts were excluded, leading to expand audience’s involvement with cultural artwork and their hidden (or not) meanings. Digital tools facilitate the “popularization” of cultural forms that where more conservative or has a silo – based logic. It is worth mentioning that already exist museums with internal departments capable for audio and video production, in order to enrich their communication means. Following this perspective, virtual reality and augmented reality applications are developed (mainly with external cooperations), while a new role is emerged for cultural organizations in game design: (a) developing serious / educational games for internal use and (b) play an advisor’s / expert’s role and content provider (reusing digitized cultural content) for popular game that aim to have an historical background. All the above, involves the reuse of content and knowhow, giving life to cultural artworks via alternative ways. Gaming can promote audience’s active participation and interaction among users but it should be taken into account that museums have a strong tendency to protect their role as places of cognitive development rather that “social places” (Simon, 2011).

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All these applications, content and games’ design even though seem to be something distinct from traditional, cultural organizations main activities, it involves a rather important element. It involve activities that are heavily related with the production of new cultural content, leading to new forms of digital culture. Creating “culture from culture” is a rather important tension, while the development of new, digital culture should not estrange audience from the cutltural artwork itself. That is why, technology should be present but also discreet, not distracting visitors from cultural artifacts but “responsive” to their needs and “engaging” (visitor to visitor, visitor to cultural artifact). Digitazation can deliver cultural content anywhere at anytime, but the unique and authentic, conserved artifact can provide notions and powerful emotional involvement. That is the element that museums can offer via Industry’s 4.0 technologies. An experience filtered through a screen could be delivered anywhere, while physical and emototional interconnection with artifacts can only take place in museum’s physical place. Last but not least it should be mentioned that digital technologies will have a significant impact on managing and operational processes taking place in cultural organizations. Even though cultural and educational result will keep playing a significant role, financial and administrative procedures will gradually become more and more important. Indicators and a real time (standardized) evaluation system are of great importance, especially in terms of visitors’ satisfaction and personalization of services. By developing an accurate information collection and management system, a variety of new and innovative services can be provided regarding diverse situations such as preparing exhibitions, enriching visitors’ experience and organizing cultural events.

FUTURE RESEARCH DIRECTIONS Even though cultural sector seems less relevant with Industry 4.0 comparing with other industries, during the last 5 years already existing technologies, such as Virtual Reality (VR), rapidly emerged creating a dynamic environment with great opportunities in “3D reconstruction” of cultural monuments and historical cities, that do not longer exist or have been modified (merely or totally). Cultural sector adopted digital technologies (e.g. Virtual Reality, Augmented Reality) which have developed enough sophisticated tools to create realistic objects and environments (Ch’ng, 2013) in order to offer a much richer user experience, while the idea of visualizing three-dimensional (3D) context is gaining pace from both technological and cost-developing aspect. Further research is needed in order to create more holistic scenarios evolving all the value-chain of culture industries (not only visualization technologies), following the results of Münster and Koehler (Münster & Koehler, 2016) that revealed that current use is most commonly used for presentation. Moreover a series of challenges arise related to Industry 4.0 on (a) how it will be implemented in different societal aspects (from a cultural perspective), (b) how mature cultural organizations and social stakeholders are to adopt the required changes, (c) how there will be a successful transition from a technology-driven innovation to a strong application development and (d) what the whole procedure of change will bring regarding the relationship between the cultural sector – visitor (Lasi, Fettke, Kemper, Feld, & Hoffmann, 2014; Löffler & Tschiesner, 2013; Zhou, Liu, & Zhou, 2015). It should be taken into account that only recently the wide public started to accept that cultural sector (especially museums and public galleries) should have a role more extended than “culture and education”, involving “business operations” and a profit-oriented value chain.

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CONCLUSION Research on Industry 4.0 and its implementation in Cultural Sector will gather research and professional interest the next, few years. Authors believe that there will be a large discussion on on how cultural sector can depart from current interest on “isolated” VR and AR applications to a more holistic approach. At the current state such applications seem to be the “target” instead of the “mean” to a more radical transformation. That means that immpersive technologies will have to be implemented in a larger variety of daily operations, rather than the current, just for “presentation” approach (Münster & Koehler, 2016). Expanding VR technologies and AR technologies usage is essential for cultural sector in both terms of perceived usefulness and ease of use among end-users (Loumos et al., 2018), while a well targeted development can expand cultural organizations’ role as “social places” (Vayanou, Ioannidis, Loumos, & Kargas, 2019). In order to create a roadmap of successful implementation, cultural organizations and museums should evaluate their maturity to expand technological usage in their daily operation. Such a procedure involves understanding 4 key elements: (a) current operation where action take place nowadays (observation), (b) why success is following some actions (understanding), (c) what actions should be prepared (preparation) and (d) what new should be given to end – users massively and in an autonomous manner (forthcoming actions). Figure 5 presents the above approach by involving different technologies and logics that come from Industry 4.0 in a step – by – step implementation scenario. Figure 5. Industry 4.0 to cultural sector

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KEY TERMS AND DEFINITIONS 3D Reconstructions of Historical Buildings / Monuments: A rather widespread technique in cultural sector of representing not any more existing building / monuments or to reveal how it would look like in the past. By using three dimensions these cultural artifacts can be used either with virtual reality technologies or with augmented reality technologies. In both cases, the final result targets to enrich user’s experience. Augmented Reality (AR): Augmented Reality (AR) is the technology that integrates digital information with the real world in a way that enhances graphics, sounds and 3D objects over the natural objects. AR technology focuses on the enriched experiences of the users, presenting visual information complementary of the natural environment through users’ devices. The digital augmented content interacts with the user actions as most of the time the AR content is touchable and quite responsive on user’s input. Cultural Sector: A large variety of industries, also called under the term “cultural and creative industries”. The term is used to describe a wide variety of organizations and private companies enabling in an even wider list of activities including (representatively): Museums, galleries and libraries, IT, software and computer services, Architecture, Advertising and marketing, Crafts, Design (product, graphic and fashion design), Film, TV, video, radio and photography, Publishing, Music, performing and visual arts. Cyber Physical System (CPS): A whole process enabling the communication between people, machines and resources. The whole idea is based on embedding networks and computers to physical process, in order: (a) to support unique identification, (b) to collect, store and analyze data and finally (c) to create networks from physical processes to computation (e.g. structured information) and vice versa (e.g. processes reengineering). Industry 4.0: A “marriage” between the physical world / sciences with digital technologies. Digital technologies offer new ways of interconnection with “physical”, effective data collection and wise systems capable to interpret the gathered data for a more holistic, informed decision making (action back to physical world). Internet of Things (IoT): It is the integration of Internet into various “manufacturing – business – everyday” processes / operations in order to allow “things” such as mobile devices, sensors, RFID and actuators to (a) interact and (b) cooperate in order to (c) reach common goals. Virtual Reality (VR): Virtual Reality (VR) is the technology that creates 3D scenes, places and worlds where users, through headset devices are connected and participating in. These environments are computer generated, capable to interact with users’ actions and allow them to discover fantastic worlds by using most of their senses as living in the real world. VR experiences depends on system’s capabilities, as the visual quality is directly related with the graphics rendering hardware and the simulation software.

This research was previously published in Impact of Industry 4.0 on Architecture and Cultural Heritage; pages 1-19, copyright year 2020 by Engineering Science Reference (an imprint of IGI Global).

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Impact of Industry 4.0 in Architecture and Cultural Heritage:

Artificial Intelligence and Semantic Web Technologies to Empower Interoperability and Data Usage Claudio Mirarchi https://orcid.org/0000-0002-9288-8662 Politecnico di Milano, Italy

Beniamino Di Martino https://orcid.org/0000-0001-7613-1312 Università della Campania Luigi Vanvitelli, Italy

Alberto Pavan https://orcid.org/0000-0003-0884-4075 Politecnico di Milano, Italy

Antonio Esposito https://orcid.org/0000-0002-2004-4815 Università della Campania Luigi Vanvitelli, Italy

ABSTRACT Building Information Modelling (BIM) is recognized as the central mean in the digitalization process of the construction sector affecting both the technological and the organizational levels. The use of information models can empower communication capabilities thus addressing one of the main development directions of industry 4.0. However, several issues can be highlighted in the representation of objects through information models especially in the case of existing and/or historical buildings. This chapter proposes an extensive analysis of the use of BIM for existing assets exploring the recent development in the area of machine learning and in the use of ontologies to overcome the existing issues. It will provide a structured presentation of existing works and of perspectives in the use of ontologies, expert systems, and machine learning application in architecture and cultural heritage focusing on communication and data use in digital environments along the industry 4.0 paradigm.

DOI: 10.4018/978-1-7998-8548-1.ch070

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 Impact of Industry 4.0 in Architecture and Cultural Heritage

INTRODUCTION The industry 4.0 paradigm born in the manufacturing and is slowly entering in the construction sector producing deep changes in this industry. Product Lifecycle Management (PLM), Internet of Things, Cloud Computing, Big Data, 3D printing, Cyber Physical Systems, Augmented Reality, Virtual Reality, Human Computer Interaction are only some examples of technologies and principles that are highly impacting in the construction industry. Nevertheless, in general terms, industry 4.0 refers to both the increasing digitalization and automation of the manufacturing environments and the creation of digital value chains to enable communication between products, their environment and business partners (Lasi, Fettke, Kemper, Feld, & Hoffmann, 2014). Hence, industry 4.0 implies the progressive digitalization of an industrial sector focused on improved automation and communication capabilities, including in these last all the interested parties as well as the products (buildings, infrastructures, etc.). Building Information Modelling (BIM) is recognized as the central mean in the digitalization process of the construction sector affecting both the technological and the organizational levels. The use of information models can empower communication capabilities thus addressing one of the main development directions of industry 4.0. In fact, information models are characterised by the aggregation of both geometrical and non-geometrical information in composing objects (Eastman, Teicholz, Sacks, & Liston, 2011) defined according to a shared semantic structure (Pauwels, Zhang, & Lee, 2017). However, the complexity of the construction products (buildings, infrastructures, etc.) as well as their uniqueness can limit the communication processes due to the difficulties in defining a standard semantic structure able to embrace all these peculiarities. This issue is intensified by the lack of geographical and organisational proximity that characterise the construction supply chain (Dallasega, Rauch, & Linder, 2018). In this context, the concept of interoperability, i.e. the process to exchange and use information between two or more systems (IEEE, 1990) represents a crucial aspect. Since 1995, the BuildingSmart consortium (formerly known as International Alliance for Interoperability until 2008) is working on the Industry Foundation Classes (IFC), a common data model to represent and describe building processes (Laakso & Kiviniemi, 2012). Nowadays, IFC represents the reference standard to share information models in the construction sector. However, several studies demonstrated its limits and highlighted practical issues in its application in the industry. Moreover, due to the abovementioned product complexity, the semantic structure defined in IFC models is limited and is not able to comprehend all the elements and all the information required during both the development and the maintenance of a real estate. Some researchers explored the use of expert systems and machine learning technologies applied to IFC models to augment the organisation proximity pushing a standardised communication supported by the machine (Bloch & Sacks, 2018). However, these studies are bounded to the IFC semantic structure and can address the underlined issues only partially. The difficulties in representing the semantics of buildings is highlighted in the case of existing buildings and in particular with reference to the historical ones. The development of BIM applications in the maintenance of historical buildings has demonstrated an increasing interest in last years (Logothetis, Delinasiou, & Stylianidis, 2015; Megahed, 2015; Volk, Stengel, & Schultmann, 2014). The experimentations developed on the Albergo dei Poveri in Genova (Musso & Franco, 2014), the Basilica of S. Maria di Collemmaggio in l’Acquila (Oreni, Brumana, Cuca, & Gergopoulos, 2013), the Masegra-castle in Sondrio (Barazzetti et al., 2015) and the Dome in Milan (Fassi, Achille, Mandelli, Rechichi, & Parri, 2015) are only some examples. These studies underline the difficulties in the semantic representation of historical elements in building information models. For example, the representation of complex 1398

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elements like the spires of the Milan Dome, required the integration of three information sources, namely a 3D model, an external database and a photographic catalogue (Fassi et al., 2013). However, the geometrical model does not embed object classification and rule-based relations between objects and all the information are managed through external means without a shared ontological structure. In other research is pointed out that commercial BIM software are not ready to be used for existing (in particular historical) buildings (Fregonese et al., 2015). This is related both to the complex geometry of some objects and to the difficulties (and sometimes to the impossibility) to provide a correct semantic representation of objects in a BIM environment. Anyway, the case of historical buildings demonstrates the need of specific approaches pushing the development of dedicated Level of Development (LOD) (UNI, 2017b) and dedicated methods in the management of aggregated sources of information (Della Torre, Mirarchi, & Pavan, 2017). Due to these limitations, starting from early 2000s researchers start to explore the use of semantic web technologies to enhance the information exchange processes (Pauwels et al., 2017). Nowadays, there is an active research community working on this area with an increasing number of studies related to semantic web and other related technologies such as graph databases (Ismail & Scherer, 2018). Thanks to the empowered data accessibility guaranteed by semantic web technologies, some researchers start the exploration of automated systems to optimise the communication phases e.g. through a rule-based approach (Farias, Roxin, & Nicolle, 2018). Starting from the presented context, the proposed chapter aims to explore the recent frontiers in the use of semantic web technologies in the construction sector focusing on their impacts in the communication processes along the industry 4.0 direction. The chapter will highlight the existing open issues and the main research directions in this area. Moreover, it will include the presentation of recent research works developed by the authors focused on three main points: 1) the automated recognition of information objects and their aggregation in a hierarchical structure through expert systems based on ontological models and/or machine learning approaches; 2) the exploration of requirements and proposal to expand and relate existing ontologies in order to address specific requirements in the use of information; 3) possible applications and impacts produced by the use of semantic web technologies e.g. in the optimisation of communication means, and in the definition of human-machine interactions. In conclusion, the proposed chapter will provide a structured presentation of existing works and of perspectives in the use of ontologies, expert systems and machine learning applications in architecture and cultural heritage focusing on communication and data use in digital environments along the industry 4.0 paradigm.

BACKGROUND Among the different aspects that can be related to the Industry 4.0 paradigm, this chapter focuses on one area of communication that is the use of ontologies and machine learning applications to empower the management of information along collaboration processes. Following this direction, in the background section the basic concepts of machine learning and ontologies are presented in general terms to provide a shared basis of understanding. Moreover, a brief presentation of BIM and of its use for existing buildings is provided to pave the way for the next sections of the chapter focused on the existing issues in this area.

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Ontologies and Machine Learning In the context of computer and information sciences the terms Semantics, Ontology, Artificial Intelligence and Machine Learning are often found together, and in many cases confusion may arise because of misinterpretations of their meaning. Indeed, the concepts of Ontology and Machine Learning are really intertwined, as they both have roots in the areas of Knowledge representation and Information extraction and retrieval, with a strong focus on automation. However, while many points of contact are present between these two concepts, they are still to be considered separated and individual areas of study, which can be beneficial to one another, but that move in parallel.

Machine Learning The term Machine Learning (ML) denotes a very complex set of concepts, as it evolves with time and technology. ML is a science, stemmed from the broader Artificial Intelligence (AI), of which it can be seen as an application. However, while AI tries to mimic human abilities, ML focuses on the specific capability of machines to learn and improve from experience without being explicitly programmed, either by automatically analyzing data manually provided to them, collected from the surrounding environment, obtained through the interaction with external systems or human beings. In a very simplified and basic view, the objective of ML is to design and implement algorithms that receive input data and use statistical analysis to predict an output, while updating outputs as new data becomes available. The primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly. The processes involved in ML are very similar to the ones found in Data Mining and Predictive Modeling: indeed, Pattern Recognition can be considered as one of the first attempts to modern ML. As of today, the application of ML techniques is widespread, and people come in contact with it without even noticing practically every day. Many people are familiar with the fact that, when shopping or researching online, they are often proposed ads which are related to their most recent researches or purchases. That is because a Recommendation Engine has analyzed their search history and made predictions on their future researches, personalizing the online ads in real time. The areas of application of Machine Learning greatly vary, as they are not limited to personalized marketing. Fraud detection is another field of application of machine learning techniques, where the behavior of user is analyzed and classified, using a set of characteristics, in order to determine the presence of malevolent actors in a system. Natural Language Processing (NPL) is also quite common, as text comprehension and its translation from one language to another is becoming a relevant topic, due to the increase of documents and texts shared online. How Machine Learning Works Machine Learning algorithms can be roughly divided into two main categories: •

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Supervised Algorithms require an external expert, a data scientist, to provide both input and corresponding desired output, together with a feedback on the accuracy and general reliability of predictions during the whole algorithm training process. The data scientists have to specify which data features the model should take in consideration to develop its analysis and consequent prediction, as not all the characteristics of the input data are deemed necessary or of interest.

 Impact of Industry 4.0 in Architecture and Cultural Heritage

By analyzing the input data and desired output, supervised algorithms infer a function which, applied to the training data, returns the requested output. Every time the output does not match the desired one, the algorithm modifies the function to correct, or at least minimize, the error. Only after the training process is complete, the algorithm can be applied to completely new data. •

Unsupervised Algorithms work very differently, as they are not trained over known data by scientists, but they analyze not labeled and non-categorized inputs, in order to automatically discover hidden relations and patterns. There is no correct output to be identified, but only not explicit correlations and schema, which can be later used to identify and categorize new incoming data. These algorithms are more complex than supervised ones and require huge amounts of input data to efficiently operate. That is why their development has been boosted only recently, due to the advent of Big Data technologies. Besides these two main classical categories, two more can be considered today:

• •

Semi-supervised Algorithms fall in between supervised and unsupervised categories, as they use both labeled data for training (small sets), and uncategorized data to discover correlations (generally huge sets of data) Reinforcement machine learning algorithms operate through a continuous interaction of the algorithm with the surrounding environment. During training, the machine acquires new data continuously and the produced results are checked, in order to detect errors. A system of rewards and errors is enforced, so that the algorithm can evolve by learning which is the best behavior in all occasions. This kind of training is common with Agent based environments. Whichever category the specific algorithm belongs to, a ML technique always involves three main steps:

1. Representation, in which data and their characteristics are modeled according to specific standards which can be understood by the algorithm. 2. Evaluation. This is the main phase of the algorithm, in which the data analyzed to discover the scoring function or the categorization pattern to be applied. 3. Optimization, in which the scoring functions and patterns are checked against the desired results or new incoming data and are corrected in order to minimize errors. Several algorithms exist for machine learning. Some of them can be used in both unsupervised and supervised modes. Decision Trees are generally used with supervised training. They analyze data and build a tree-shaped model which develops paths that, starting from a specific input, arrive at a desired output. Figure 1 on the left reports an example of a decision tree model, with a highlighted decision path. K-means clustering is used to group data into a specified number of categories, according to a set of previously determined characteristics. It can be used with both supervised and unsupervised methodologies, as the groups can be determined at run-time with read data, or beforehand, through training. However, the number of clusters and the characteristics of the data to be analyzed need to be determined before.

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Figure 1. Example of decision Tree (left) and of neural network (right)

Neural Networks are deep learning models which exploit large amounts of training data to identify correlations and learn to process new information. Their functioning is modeled after human brain’s neurons, from which their name directly derives, with activation functions associated to calculation nodes, the neurons, which determine if a specific condition has been fulfilled and if the chosen path is correct. Figure 1 on the right reports an example of a three-layered neural network.

Ontologies The first and most commonly accepted definition of ontology has been provided in (Gruber, 1993): An ontology is a description (like a formal specification of a program) of the concepts and relationships that can formally exist for an agent or a community of agents. This definition is consistent with the usage of ontology as set of concept definitions, but more general. Following this definition, an Ontology can be described as a formal description of knowledge, presented as a set of concepts, data entries or entities having a specific meaning within a domain of knowledge or discourse, together with the relationships that exist among them. In a very simplified view, an ontology can be also seen as an encyclopedia of terms, each with a specific and well-known meaning within a domain of knowledge, where each term is connected to one or more other terms by specific relations. In order to enable an effective description of the knowledge domain and its complete modelling, an ontology has to define a set of base concepts, or representational primitives, that are typically embodied by a set of classes identifying meaningful categories, attributes (or properties) associated to such classes, and relationships explicitly existing among classes or their members. Restrictions, rules as well as axioms are considered part of the ontology, and are generally expressed as particular relationships. However, different ontology expression languages can provide ad-hoc structures to represent these concepts. As a result, the knowledge represented by ontologies not only becomes shareable and reusable, but it can be also easily expanded and extended by discovering new relations among classes and their individuals. This characteristic marks the fundamental difference between ontologies and other knowledge representation models, such as taxonomies: the world depicted by a taxonomy is static, with well-established hierarchies which will not change over the time (unless new categories are manually added to the hierarchy); the domain represented by ontologies can evolve to include new concepts, relations and attributes, even automatically. There currently exist two main types of ontologies: Upper Ontologies and Domain Ontologies.

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Upper Ontologies collect knowledge and terms which can be applied to more than one domain, but that encompass multiple areas of knowledge and can be freely applied in all of them. They contain general information that can be applied to multiple situations. As an instance, the Basic Formal Ontology (BFO) (Arp, Smith, & Spear, 2015) is an Upper level ontology that is designed for use in supporting information retrieval, analysis and integration in scientific and other domains. Domain Ontologies are instead focused on a specific domain of knowledge, and it contains concepts and terms related to that specific area. This means that the same term could have a different meaning in different ontologies, as they would represent an independent concept in each of the considered area. As an instance, if we considered the term “Memory”, it would acquire different meanings in the ontologies of “Games”, “Neurology” or “Computer Science”. Many domain ontologies exist, ranging from ones focused on Medical aspects, such as Diseases and their symptoms (Schriml et al., 2011), to those referring to Information Technologies, such as (Di Martino, Cretella, & Esposito, 2013) which focuses on Cloud and interoperability aspects. Figure 3 reports an excerpt of such an ontology (only the main classes have been reported). Figure 2. A Cloud services ontology

An Example of Ontology The best way to get a grasp of what an ontology is and how it can be used to represent a domain, is surely through a very simple example. Let’s suppose a University ontology is needed to represent elements and entities belonging to an Academia. The University ontology will include classes of concepts, such as :Staff, :Professor, :Students, :Course or :Department (this kind of notation is quite common when referring to elements of an ontology). The elements we have introduced refer to a general class,

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but not to an instance. Of course, ontologies may contain instances of classes, that is real individuals. For example :Einstein is an instance of the class :Professor while :Physics is an instance of :Course. It is important to stress that instances of different classes may represent the very same individual in an ontology, unless this is explicitly stated via a :disjoint property asserted on the involved class. Editors supporting the creation of ontologies generally add the disjoint attribute to all new created classes, unless differently specified, but this is not a rule. One of the fundamental properties involving classes and instances which can be found in an ontology, is the :subclass relation. A class C is a subclass of a class C’ if each instance of C is also an instance of C′. In other words, the set of instances of C is a subset of the set of instances of C’. If we state that the class :Professor is a subclass of the class :AcademicStaff, we are merely stating that all professors are part of the Academic Staff of the University. As already mentioned, ontologies can express relationships among instances or classes, which enrich the knowledge base. A typical relation which may exist in a University ontology is the :TeachesIn(X,Y) property, which represents the fact that X teaches in Y. Using this relation on the instances we have just mentioned, we could say that Einstein teaches Physics by stating :TeachesIn(:Einstein,:Physics) This is a relation existing between two specific individuals. However, we could express the same relation between the classes : AcademicStaff and :Course: :TeachesIn(:AcademicStaff,:Course) This relation not only assesses the fact that a member of the Academic Staff can teach in a Course, but it also imposes a restriction on the actual individuals that can participate in the relation. As an instance, if the relation :TeachesIn(:DepartmentOfMathematics,:History101) was assessed, the ontology would be inconsistent (we are implying that :AcademicStaff and :Department are disjoint classes) and any logical engine would complain about it. Now, let’s suppose that a new relation is assessed, :TeachesIn(:Curie,:Chemistry). Even without explicitly assessing the facts, any logical engine would automatically infer that :Curie is an instance of :AcademicalStaff and that :Chemistry is an individual of :Course. Inference enables the discovery of new knowledge from simple facts, and can be used to extend existing properties to new individuals: as an instance, if all individual belonging to the Academical Staff were allowed special access to certain University facilities (i.e. private laboratories or offices), then this capability would be automatically transferred to :Curie just because of the :TeachesIn relation in which it is involved. Ontology Languages: RDF and OWL The concepts represented by ontologies need to be somehow expressed, preferably in a machine understandable language which enables automatic reasoning, querying, knowledge extraction and extension. The notation used in the previous section is very useful for human readers, but it is not suitable for machines, especially when the knowledge is to be shared through the Semantic Web. While several candidates have been proposed in the literature, the Web Ontology Language (OWL) (McGuinnes & Van Harmelen, 2004) represents the current standard for Ontology expression. OWL is built upon another well-known and widespread language for ontology description, the Resource Description Framework Schema (Miller, 1998) (RDFS), which it uses as a base and extends to provide more expressiveness. On the Web, everything that can be addressed is considered as a Resource and as such, it needs a unique identifier (URI). As an instance, if the University ontology was located at the (fictional) address

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http://web.academicexample.com/University then the:Professor class would be identified by the complete URI http://web.academicexample.com/University#Professor. Primitive elements of the RDFS and OWL languages are all identified by URIs: the namespace for RDFS would be the URI http://www.w3.org/2000/01/rdf-schema#, while for OWL it would be http:// www.w3.org/2002/07/owl# . For brevity, rdfs: and owl: are generally used to refer to such namespaces. RDFS and OWL both represent knowledge as triplets, in the form ⟨a P b⟩, that expresses the fact that b is a value of property P for the subject a. So, the fact that:TeachesIn is a property can be represented by: ⟨:TeachesIn rdf:type rdf:Property⟩. Basically, the fact that Einstein is a Professor that teaches Physics, can be state via the triplets: ⟨:Professor rdf:type rdf:Class⟩, ⟨:Einstein rdf:type Professor⟩,⟨:Course rdf:type rdf:Class⟩, ⟨:Physics rdf:type:Course⟩, ⟨:Einstein:TeachesIn:Physics⟩. RDFS offers properties to specify relationships’ domains and range also, but it lacks a syntax to express Restrictions. This is where OWL becomes useful: as an instance, the fact that a Professor cannot be a Student, or that a Department cannot be part of the Academical Staff can be expressed via the two triplets: ⟨:Professor owl:disjointWith:Student⟩, ⟨:Department owl:disjointWith:Academical Staff⟩. Other interesting facts which can be assessed via OWL are the symmetry of properties (owl:SymmetricProperty) or their inversion (owl:InverseProperty).

Relation Between Machine Learning and Ontologies Ontologies and Machine Learning share the goal of creating intelligent systems that emulate human capacities, in particular reasoning, validating, and predicting. Data and knowledge analysis and representation have been greatly affected by both Ontology development and Machine Learning algorithms, and they have influenced each other considerably. Due to the fact that ontologies model even complex domains, and that their creation and filling require a lot of effort, Machine Learning has been successfully applied to (semi-automated) ontology learning, alignment and annotation, and even duplicate recognition (Doan, Madhavan, Domingos, & Halevy, 2004; Maedche & Staab, 2001). ML techniques, such as Natural Language Processing in particular, can provide support to the creation of ontologies, if applied to a consistent number of documents or online resources (Di Martino, Esposito, D’angelo, Marrazzo, & Capasso, 2016). Conversely, as clean and structured data are often required by ML algorithms, ontologies have been repeatedly utilized as input, given their capability to represent facts, rules and relations and point out discrepancies. Ontologies have been specifically exploited in the medical field, as the entry point for complex Deep Learning algorithms aiming at discovering new correlations between symptoms and maladies and suggest doctors the most correct course of action (U.S. Patent 7,899,764, 2011). In this context, the construction sector started exploring the use of both ontologies and machine learning algorithm to empower interoperability and collaboration trying to overcome the issues related to the complex structure of the construction sector products (buildings, infrastructures, etc.). Of particular interest - according to its component complexity - is the case of existing buildings that requires a specific analysis starting from the use of BIM with reference to existing buildings, up to the introduction of ontologies and automated means to optimize the information flow as presented in the following.

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Building Information Modelling for Existing Assets The identification of a unique definition of Building Information Modeling (BIM) is still an unresolved issue (Ciribini, Bolpagni, & Oliveri, 2015). Nevertheless, comparing several references available in the scientific literature, including (BSI, 2013; Eastman et al., 2011; ISO, 2016), the procedural character of BIM and its extension to the whole life cycle of the building is clear. The information models that are generated during BIM processes can be seen as a means to transfer and manage information during the different phases of the construction process from the early concept phase to the operation and management one. Hence, the introduction of BIM in the construction sector needs to consider its development in relation to existing buildings considering, among other, management, renovation and restoration activities (Akbarnezhad, Ong, Chandra, & Lin, 2012; Cheng & Ma, 2012). Experiments developed on some important monuments (as listed in the introduction), have shed new light on the need to use BIM for historical buildings while raising new issues dictated by the radical differences between the design/construction processes and those of conservation/restoration of the built heritage. Among the main issues, the correct interpretation of the information along the life cycle of the models and the correct interpretation of the information content development are of central importance. These themes are directly related to the need of creating a shared ontology that is able to include the complex elements that can be find in an historical building. The increase in geometric and non-geometric information associated to digital objects and generally related to the acronym LOD with its different interpretations, must be read in terms of process (BIMForum, 2016; BSI, 2013; ISO, 2018; UNI, 2017a) and must be integrated into the wider process of the manufactured products including the analysis of the required information for the specific uses at the different stage of the process as identified in the recent concept of Level of Information Need (LOIN) (ISO, 2018). In the following will be proposed an in-depth analysis of the current framework of reference for the main LOD scales developed worldwide, focusing on their application in maintenance and conservation of the built heritage.

Definition of LOD for Digital Objects The most consolidated references in the international field are the USA and UK LOD: • •

USA LOD have been introduced in the AIA Document E202™-2008: Building Information modeling Protocol (American Institute of Architects, 2008). They are defined annually in the LOD specifications of the BIM Forum (BIMForum, 2016); UK LOD have been introduced in PAS 1192-2:2013 (BSI, 2013). They are defined in the BIM Toolkit of the National British Standard – NBS (NBS, 2015).

The USA LOD are called Level of Development and their definition includes the geometric characteristics of the element - element geometry - and the information associated with it - attribute information. The scale includes 6 levels of LOD: 100, 200, 300, 350, 400, and 500.

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The UK LOD are called Level of model definition and are given by the union between geometric characteristics - Level of (model) detail - and non-geometric characteristics - Level of (model) information. There are two scales: • •

Scale of LOD (definition): brief, Concept, (developed) Design, definition (Production), built and Commission (installation), Handover and close out (As constructed), operation and in use; Scale of LOD (detail), LOI (Information): (0), 1, 2, 3, 4, 5, 6.

In this context must be included the recent ISO 19650-1 (ISO, 2018). It tries to overcome the need of a specific scale with the use of LOIN, i.e. a “framework which defines the extent and granularity of information”. The concept of LOIN on the one hand clearly identify the need to define specific requirements according to specific deliverables. However, on the other hand, it works at a very high level and does not provide effective means of applications. Hence, LODs are still used as references in the industry identifying a shared identification of information needs for the different phases of the process.

LOD for Existing Buildings In stable real estate markets such as mature economies (e.g. Europe, USA, Canada and parts of Asia), the construction sector is increasingly interested in consolidated urban fabrics and interventions to reuse the existing buildings (Mill, Alt, & Liias, 2013; Penttilä, Rajala, & Freese, 2007). From the renewal or replacement of existing ordinary goods to the conservation or renovation of monumental or environmental assets. The starting condition is therefore represented more and more by an information model of the existing asset that illustrates, according to various levels of age and because of the type of good (ordinary or monumental/environmental), the factual state of the manufactured product by reason of the objectives of the purchaser and the consequent objectives and uses of the requested model. This model is, in most cases, the result of significant activities due to the lack of documentation previously produced in terms of BIM and, therefore, a model that can serve as a starting point to develop the necessary updates and enrichments (Arayici, 2008; Armesto, Lubowiecka, Ordóñez, & Rial, 2009; Attar, Prabhu, Glueck, & Khan, 2010). In the USA and UK systems above mentioned (BIMForum, 2016; BSI, 2013), the issues related to the content of information that have to be defined in information models related to the existing buildings is indirectly managed as detailed in the following sections. LOD 500 USA (2016) In the USA LOD system by IAI, the assignment to describe the elements composing existing buildings is acquitted by the level 500, that represents the last level for the USA LOD scale with reference to the design process. The LOD 500 identifies the representation of the “verification” on the places of the state of fact of any element of an immovable good. Differentiations are not explicit neither in terms of ancientness of the interested entity, being able himself/herself/itself to treat both of built elements or installed in once recent (as-built) both in once past, neither in terms of importance, or less, in historical sense/artistic/ architectural or environmental of the elements themselves or of the work that contains them.

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LOD 500: The Model Element is a field verified representation in terms of size, shape, location, quantity, and orientation. Non-graphic information may also be attached to the Model Elements. The objects [elements of the model] constitute the verified representation on the places in terms of dimension, location, quantity and orientation. These elements can contain and can be related to not graphics information. BIMForum interpretation: Since LOD 500 relates to field verification and is not an indication of progression to a higher level of model element geometry or non-graphic information, this Specification does not define or illustrate it. Since LOD 500 refers to the representation of existing objects and does not propose a progression of the geometric and/or the not geometric information association to the objects, the LOD Specifications do not illustrate neither they define examples for this level. The LOD Specifications of the BIM Forum are divided in two parts. Part I defines the objects geometric (Element Geometry) while Part II identifies the informative attributes that must be associated to the geometric objects identified in part I. LOD As Construction/Is Use (LOD 5 – LOI 5/6) UK In the British Standard’s LOD UK system (BS; PAS and toolkit) the task of describing the existing elements is defined on multiple levels. The first reference is at the model level in the LOD read as Level of model definition (LOmDf) from PAS 1192-2:2013 (BSI, 2013). The PAS 1192-2 reports also a description of LOD read as model detail (LOmDe), and LOI read as model information (LOmI): LOmDe/LOmI Handover and close-out: […] The as-constructed model shall represent the asconstructed project in content and dimensional accuracy. NOTE In addition is all the manufacturer’s maintenance and operation documentation, commissioning records, health and safety requirements, the final COBie information exchange, as-built models in native format and all relevant documentation. The as-built model constitutes the representation of the project realized in terms of information and dimensions. In addition, can be enclosures the documents of maintenance and exercise, the requirements of health and safety, the information of exchange in COBie, and every other meaningful documentation. LOmDe/LOmI Operation and in-use: […] At the in-use stage, the object’s information shall be updated with any supplementary information such as maintenance records or replacement dates, and objects that have been changed or replaced with different equipment shall be updated accordingly. In the in-use stage, the objects information can be updated with any additional information according to maintenance or renovation activities. The objects must be updated and/or replaced with the new one having the required set of information. The LOD scale is not defined in PAS 1192-2 but have been included in the BIM Toolkit (NBS, 2015) where for several objects are identified the Level of Definition according to the specific Level of Detail and Level of Information (in this case, in contrast to the PAS 1192-2 the reference is to the objects instead of the models). Level of Definition (LODe) in the BIM Toolkit: Scale: 2 (concept stage), 3 (developed design), 4 (technical design), 5 (construction). LODe 5, Construction; […] to provide sufficient information for construction/installation of the appropriate products.

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Figure 3. LomDf – Level of model definition – from Figure 20 of (BSI, 2013)

The maximum level of information of geometric nature is completed in the construction. There is not a level defined for the operation phase, neither a specific level for the existing assets. Level of information (LOI) in the BIM Toolkit: Scale: 2 (concept stage), 3 (developed design), 4 (technical design), 5 (construction), 6 (operation and maintenance).

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LOI 5: What is typical in the construction phase; The prescribed manufacturer products that meet the generic product specification. LOI 6: What is typical for operation and maintenance; The key properties to be transferred into an asset database. Among others, the attributes to include for every product are for example: bar code, expected life of the component manuals of use and maintenance, and starting date of the guarantee. LOD F and LOD G Italy In the Italian context through the UNI 11337-4 (UNI, 2017b) it is defined a specific LOD for the life cycle of the buildings: LOD G: The objects express the updated virtualization of the actual state of an entity at a specific time. It is a historical representation of the passage of the useful life of a specific production system updated with respect to that which was originally implemented/built or installed. The quantitative and qualitative characteristics (size, shape, location, orientation, cost, etc.) are specific to the life cycle of a previous state. It annotates each individual (and significant) management, maintenance and/or repair and replacement work carried out over time, and records the level of any degradation in progress. It is clear its descriptive direction in contrast to the design one proposed in other LOD scale according to the nature of actions on existing assets. It is also interesting the additional specifications provided for the geometry of the LOD G (LOG G) in Appendix C of UNI 11337-4 i.e. for new intervention, the LOD G must be coherent with LOD F with updates, while for maintenance works on existing buildings LOD G can start from LOD C or D according to the specific need of the work. The appendix therefore introduces a concept of evolution of the information when the information itself followed a specific development in the process: conception, design, construction, delivery; LOG G as an update of the LOG F. While in the case of existing buildings (management and maintenance processes) where it may happen that the previous information processes (design and construction) are unknown a possible non-sequential disclosure shall be considered. Starting from limited information, LOD G can refer to an information scale equal to a LOD C or D. The costs and efforts required to create a building information model is directly proportional to the LOD level required for the different components of the model. LOD that must be defined according to the functionalities (objectives and uses) specific to the model (Volk et al., 2014). The approach proposed in the UNI 11337-4 allows the development of scalable processes according to the specific needs that may arise in the context of application.

MAIN FOCUS OF THE CHAPTER The background analysis proposed in previous sections highlight two sides of the digital transformation in the construction sector. On the one hand, the development of machine learning algorithms and their support (or vice versa) through shared ontologies presents several advantages in the definition of effective communication processes. On the other hand, the existing references and standards devoted to identify a common language in the construction sector as well as the instruments that can be used to develop building information models are limited in scope when analysed in the context of existing buildings (especially in the case of historical ones).

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Unfortunately, the software that can be used to develop building information models (BIM authoring tools) seems to be thought for new construction and for the management of technologies along with the concept of industrialized components. However, as already stated in the background, in old markets such as the European one, the majority of the work in the construction sector can be related to activities on existing buildings and/or on the demolition and reconstruction of existing areas (that usually include the need to correctly represent the existing assets). Generally, the works developed on existing assets require the definition of a first survey to acquire the required information. In the context of digital technologies this is usually done through point clouds or photogrammetric technologies to optimise both time and precision of the data acquired. Moreover, this first data model must be translated in a building information model enriching the elements (points and/or solids) according to the specific classes that is the semantic structure embedded in the specific BIM authoring tool. However, in this translation activity and consequently, in general, in the development of building information models for existing assets can be highlighted at least two critical areas as following discussed.

Object Information Content According to the design nature of existing references explored in the background, the information required for the LOD that should be used in the case of existing buildings are in many cases not coherent with the objectives of the work and/or with the information available for the specific asset. For example, in the case of historical assets many information that characterize LOD 500 USA or LOD 6 UK are usually not available and/or not useful in the case of maintenance works. These attributes are for example the name and address of the manufacturer or installer, the product bar code, the date of start of warranty, and so on. While other attributes such as the date of the last restoration work and its real extent, the presence of not original material, the level of reliability and depth of the executable investigations, etc. are not considered in the existing LOD scale. Hence, at the state of the art it is hard to find a coherent reference to identify the correct set of information that must be integrated in the case of existing assets. Moreover, due to the nature of existing BIM authoring tools, the standard information fields usually embedded in these tools are based on new buildings and cannot be removed causing a myriads of empty fields that may produce model quality issues (Mirarchi & Pavan, 2019).

Object Representation The translation activity between points clouds and building information models has been widely studied in the literature according to the need to automate the process and reduce the required time. The manual modelling of a facility starting from a point cloud scan is a time consuming activity that often hinders the use of this technologies and techniques in the practice (Tang, Huber, Akinci, Lipman, & Lytle, 2010). To create the building information model, geometrical and topological information of building elements has to be gathered and completed with semantic property/attribute information (Volk et al., 2014). Thus, in the conversion process from point cloud to building information model can be identified two main tasks, i.e. the geometrical modelling task and the semantic modelling task. The last includes the identification of object’s category, materials, properties and relationships between components (e.g. intersection and connection) (Tang et al., 2010). Three main approaches can be listed according to the literature namely 1) data-driven approaches based on the extraction of building information from captured and processed data, 2) model-driven approaches based on the definition of a predefined structure 1411

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according to topologic relations and/or constraints used to match with captured data through knowledge or contextual information, and 3) other approaches including tagging and manual identification (Volk et al., 2014). Some experiments propose combined approaches including the classification of objects based on context information starting from a point cloud without semantic information (D. Huber et al., 2011; Xiong & Huber, 2010) and the complete recognition of surface objects like walls in occluded environments (Oliver & Huber, 2010), and the use of statistical approaches to handle uncertainty in the reconstruction of building interiors (Furukawa, Curless, Seitz, & Szeliski, 2009). Nevertheless, the conversion between a point cloud (or another data structure representing the existing building) and a building information model is limited due to the limits embedded in the BIM modelling tools where the model is created. On the one hand, the semantic structure embedded in BIM authoring tool is limited and does not contain all the classes required to correctly identify the objects composing the existing assets. Hence, the conversion process often brings to information models where the objects are not associated to their natural class and/or are associated to a generic class that does not represent the specific rules and relations of the real object. This limitation is highlighted in the case of historical buildings where the component objects (for example the spire of the Dome in Milano) cannot be related to a specific class in the BIM authoring tool. On the other hand, existing buildings may have natural elements (wood beams or pillars) with not homogeneous shape or elements with a complex state of damage (for example diffused cracks on the surface). The representation of this information in the model usually requires huge efforts and brings to results that are not able to represents the real state of the art as reported in the point clouds.

SOLUTIONS AND RECOMMENDATIONS The difficulty in identifying a correct level of information mixed with the issues related to the not homogenous dictionary associated to historical building objects can cause limitations in the interoperability and in the communication between machine to machine and machine to human because there is the possibility to interpret information that are not based on a shared ontology. Hence, the need to move to machine learning and expert systems able to capture the knowledge of the sector and translate the information in a richer environment where machines can communicate easily and pave the way for the introduction of Industry 4.0 logics. It is possible to explore solutions acting on two levels. The first one is the procedural level, that is the definition of processes able to limit the existing issues. The second one is the technological level that is the identification of future directions of research to define the required instruments such as machine learning algorithms and ontologies to effectively apply the proposed processes.

Procedural Level It is possible to describe the process from the generation of the point cloud to the development of the design of the maintenance activities as follows. Starting from the point clouds, the information model of the existing asset can be generated combining the geometric information with the not geometric one derived from a survey of the materials and combining the two in a dedicated database as proposed in Figure 4.

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Figure 4. Generation of the geometric model starting from the point clouds

The resulting model can be used to integrate the information derived from a diagnosis of the damages that may be identified on the specific asset enriching the model itself to obtain a model of the state of damage (Figure 5). Figure 5. Generation of the model representing the state of damage of the asset

The resulting models can be used as a starting point to develop the required design according to the different phases identified in the process. Figure 6 reports an example focused on the preliminary design phase that can be expanded to all the other phases, including as an input the models obtained from the precedent phase as well as the models of the existing asset that always have to be checked against the designed ones. The process here described highlights the need to create a specific set of activities in order to combine the geometric information from the point cloud and the not geometric information in a structured information model (not limited to the one generated by BIM authoring tools). Nevertheless, this configuration does not solve the issues highlighted in the previous section. To deal with the representation of objects it is necessary to consider the integration of all the information in a shared Common Data Environment where each piece of information have to be combined and coordinated with the others. The models of the existing asset generated in the previous passages of the process are for their nature limited in precision. Hence, the point cloud cannot be seen as an element that die after the conversion process but must be intended as a set of information that have to be used in combination with the generated model (Figure 7). 1413

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Figure 6. Design development starting from the existing models

Figure 7. Information derived from information models and point clouds and other sources

Thus, considering the information model not as the result of a BIM authoring tool but as the combination of information contained in a collaboration environment such as a CDE, the issues related to the representation of objects can be partially limited. The real configuration of the existing asset is contained in the point clouds while the information model constitutes a geometric reference to identify the development of the different design phase. Figure 8 shows an example of a combined query between the information model and the related point cloud highlighting how combining the information it is possible to obtain a specific representation of the state of the art (if required), while the development of the information model does not require a huge effort in terms of geometric modelling.

Machine Learning and Ontologies Combining the information in a digital environment, such as a CDE, that is external to the specific BIM authoring tool used in the development of the information model, paves the way to explore the use of different data structures to overcome the issues related to the semantic representation of the historical objects. In fact, while BIM authoring tools are based on a rigid semantic structure, CDEs may be based on ontologies creating a more dynamic system able to represent complex objects such as those constituting historical buildings. This approach can provide a shared structure to promote the development of applications able to affectively use the data contained in building information models. The Authors recently developed an automated system to identify and recognise objects contained in building informa-

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Figure 8. Combined query between an information model and the related point clouds

tion models with reference to the ifcOWL ontology. The proposed system comprises several components as following listed: • • • • • • • •

An IFC building representation, generated using a BIM modelling tool; An IFC-to-OWL component, that converts an IFC file into an ontology; An ifcowl based semantic representation of the building; An OWL-to-PL component, that performs the conversion of an ontology into Prolog facts; A Prolog based semantic representation; An inference rules repository; A Prolog based inference engine that performs the recognition of objects and produces new prolog facts representing the recognized entities, thus populating the prolog based building representation; A knowledge base enricher that translates the new facts inferred by the inference engine into OWL components and enriches the ifcOWL representation.

This experimentation demonstrated the ability of an expert system to recognise objects contained in a building information model according to an ontology that contains more classes than the one defined in the specific BIM authoring tool used to develop the information model, thus demonstrating the applicability of the proposed approach. On the other hand, the study highlighted the limitation of the existing ontological structure requiring the need to create an ad hoc extension of the ifcOWL ontology. Thus, to correctly apply the proposed approach two line of future research are envisioned. First, the development of automated system based e.g. on machine learning, expert systems, etc. able to recognise model objects. Second, the progressive development of specialised ontologies able to represent the elements composing historical and existing buildings.

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CONCLUSION This chapter explored the state of the art in the introduction of BIM principles in the case of existing and/ or historical buildings analyzing the existing issues and limitations from both the technological side and the procedural one. The chapter starts with a precise explanation of basic concepts related to machine learning and ontologies. This introduction creates a shared knowledge around these arguments that are not always known in the construction sector research area. Thus, the identification of these concepts in simple terms can help the reader in understanding the analysis proposed in the rest of the chapter. In the background are also explored the international references that nowadays constitute the basis of knowledge in the development of building information models with specific reference to their application to existing buildings. Starting from this background, the chapter analyses the existing issues according to the difficulties in the correct representation of existing objects through building information models. Finally, the chapter explore possible solutions including the use of digital collaboration environments and their combination with machine learning application and ontological structure to overcome the existing issues.

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Fregonese, L., Achille, C., Adami, A., Fassi, F., Spezzoni, A., & Taffurelli, L. (2015). BIM: an integrated model for planned and preventive maintenance of architectural heritage. In 2015 Digital Heritage, Vol. 2, Sept. 28 – Oct. 2. Granada, Spain. doi:10.1109/DigitalHeritage.2015.7419456 Furukawa, Y., Curless, B., Seitz, S. M., & Szeliski, R. (2009). Reconstructing building interiors from images. In IEEE 12th International Conference on Computer Vision (pp. 80–87). 10.1109/ICCV.2009.5459145 Gruber, T. (1993). What is an Ontology. Retrieved from http://www-ksl.stanford.edu/kst/what-is-anontology.html Huber, D., Akinci, B., Adan, A., Anil, A., Okorn, B., & Xiong, X. (2011). Methods for automatically modeling and representing as-built building information models. In Proceedings of 2011 NSF Engineering Research and Innovation Conference, Atlanta, GA. Retrieved from https://www.ri.cmu.edu/ pub_files/2011/1/2011-huber-cmmi-nsf-v4.pdf Huber, M., Tsymbal, A., & Zillner, S. (2011). U.S. Patent 7,899,764. IEEE. (1990). Standard computer dictionary. a compilation of IEEE standard computer glossaries. New York: Institute of Electrical and Electronics Engineers. Ismail, A., & Scherer, R. J. (2018). A graph-based approach for management and linking of BIM models with further AEC domain models. In J. Karlshøj, & R. J. Scherer (Eds.), eWork and eBusiness in architecture, engineering, and construction, ECCPM (pp. 377–383). Copenhagen, Denmark: CRC Press. doi:10.1201/9780429506215-47 ISO. (2016). ISO 29481 - Building Information Models -- Information Delivery Manual. ISO. (2018). ISO 19650-1 - Organization of information about construction works — Information management using building information modelling — Part 1: Concepts and Principles. Laakso, M., & Kiviniemi, A. (2012). The IFC standard - a review of history, development, and standardization. Journal of Information Technology in Construction, 17, 134–161. Lasi, H., Fettke, P., Kemper, H.-G., Feld, T., & Hoffmann, M. (2014). Industry 4.0. Business & Information Systems Engineering, 6(4), 239–242. doi:10.100712599-014-0334-4 Logothetis, S., Delinasiou, A., & Stylianidis, E. (2015). Building Information Modelling for cultural heritage: a review. In ISPRS annals of the photogrammetry, remote sensing, and spatial information science. Taipei, Taiwan. doi:10.5194/isprsannals-II-5-W3-177-2015 Maedche, A., & Staab, S. (2001). Ontology learning for the semantic web. IEEE Intelligent Systems, 16(2), 72–79. doi:10.1109/5254.920602 McGuinnes, D. L., & Van Harmelen, F. (2004). OWL web ontology language overview. W3C Recommendation, 10(10). Megahed, N. A. (2015). Towards a theoretical framework for HBIM approach in historic preservation and management. Archnet-IJAR, 9(3), 130–147. doi:10.26687/archnet-ijar.v9i3.737

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Mill, T., Alt, A., & Liias, R. (2013). Combined 3D building surveying techniques – terrestrial laser scanning (TLS) and total station surveying for BIM data management purposes. Journal of Civil Engineering and Management, 1–10. Miller, E. (1998). An introduction to the resource description framework. Bulletin of the American Society for Information Science and Technology, 25(1), 15–19. doi:10.1002/bult.105 Mirarchi, C., & Pavan, A. (2019). Building information models are dirty. In 2019 European Conference on Computing in Construction. Chania, Greece. 10.35490/EC3.2019.180 Musso, S. F., & Franco, G. (2014). The “Albergo dei Poveri” in Genova: conserving and using in the Incertainty and in the Provisional. In S. Della Torre (Ed.), ICT per il miglioramento del processo conservativo (pp. 41–50). Firenze, Italy: Nardini. NBS. (2015). BIM toolkit. Retrieved from https://toolkit.thenbs.com/ Oliver, A. A., & Huber, D. (2010). Reconstruction of wall surfaces under occlusion and clutter in 3D indoor environments. In Proceedings of 3D imaging, modeling, processing, visualization, and transmission (3DIMPVT) (pp. 1–33). Hangzhou, China. doi:10.1109/3DIMPVT.2011.42 Oreni, D., Brumana, R., Cuca, B., & Gergopoulos, A. (2013). HBIM for conservation and management of built heritage: Towards a library of vaults and wooden beam floors. In CIPA 2013 XXV International Symposium, ISPRS Annals, Vol. 164 (pp. 1–6). Pauwels, P., Zhang, S., & Lee, Y. C. (2017). Semantic web technologies in AEC industry: A literature overview. Automation in Construction, 73, 145–165. doi:10.1016/j.autcon.2016.10.003 Penttilä, H., Rajala, M., & Freese, S. (2007). Building information modelling of modern historic buildings. In eCAADe (pp. 607–613). Schriml, L. M., Arze, C., Nadendla, S., Chang, Y. W. W., Mazaitis, M., Felix, V., & Kibbe, W. A. (2011). Disease ontology: A backbone for disease semantic integration. Nucleic Acids Research, 40(D1), D940– D946. doi:10.1093/nar/gkr972 PMID:22080554 Tang, P., Huber, D., Akinci, B., Lipman, R., & Lytle, A. (2010). Automatic reconstruction of as-built building information models from laser-scanned point clouds: A review of related techniques. Automation in Construction, 19(7), 829–843. doi:10.1016/j.autcon.2010.06.007 UNI. UNI 11337- 1 - Building and civil engineering works - Digital management of the informative processes - Part 1: Models, documents and informative objects for products and processes (2017). Italy. UNI. UNI 11337- 4 - Building and civil engineering works - Digital management of the informative processes - Part 4: Evolution and development of information within models, documents and objects (2017). Italy. Volk, R., Stengel, J., & Schultmann, F. (2014). Building Information Modeling (BIM) for existing buildings— Literature review and future needs. Automation in Construction, 38, 109–127. doi:10.1016/j. autcon.2013.10.023

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Xiong, X., & Huber, D. (2010). Using context to create semantic 3D models of indoor environments. In Proceedings of the British Machine Vision Conference (BMVC) (pp. 45.1-45.11). doi:10.5244/C.24.45

ADDITIONAL READING Alreshidi, E., Mourshed, M., & Rezgui, Y. (2016). Cloud-Based BIM Governance Platform Requirements and Specifications : Software Engineering Approach Using BPMN and UML. Journal of Computing in Civil Engineering, 30(4), 04015063. doi:10.1061/(ASCE)CP.1943-5487.0000539 Beetz, J., van Leeuwen, J., & de Vries, B. (2009). IfcOWL: A case of transforming EXPRESS schemas into ontologies. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 23(1), 89–101. doi:10.1017/S0890060409000122 Beetz, J., van Leeuwen, J. P., & de Vries, B. (2005). An Ontology Web Language Notation of the Industry Foundation Classes. In Proceedings of the 22nd CIB W78 Conference on Information Technology in Construction (pp. 193–198). Lecun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. doi:10.1038/ nature14539 PMID:26017442 Pauwels, P., Krijnen, T., Terkaj, W., & Beetz, J. (2017). Enhancing the ifcOWL ontology with an alternative representation for geometric data. Automation in Construction, 80, 77–94. doi:10.1016/j. autcon.2017.03.001 Sacks, R., Ma, L., Yosef, R., Borrmann, A., Daum, S., & Kattel, U. (2017). Semantic Enrichment for Building Information Modeling: Procedure for Compiling Inference Rules and Operators for Complex Geometry. Journal of Computing in Civil Engineering, 31(6), 04017062. doi:10.1061/(ASCE)CP.19435487.0000705 Soibelman, L., Wu, J., Caldas, C., Brilakis, I., & Lin, K. Y. (2008). Management and analysis of unstructured construction data types. Advanced Engineering Informatics, 22(1), 15–27. doi:10.1016/j. aei.2007.08.011 Tommasi, C., Achille, C., & Fassi, F. (2016). From point cloud to BIM: A modelling challenge in the cultural heritage field. ISPRS - International Archives of the Photogrammetry. Remote Sensing and Spatial Information Sciences, XLI, B5, 429–436. doi:10.5194/isprsarchives-XLI-B5-429-2016

KEY TERMS AND DEFINITIONS Common Data Environment: Digital environment used to collect, manage and share all relevant models and documents related to a specific project. Data Mining: Application of specific algorithms and techniques to extract patterns from data. Industry 4.0: Creation of digital value chains based on an increased communication capability between products, their environment and the involved subjects.

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Interoperability: The process to exchange and use information between two or more systems. Ontology: A formal description of knowledge, presented as a set of concepts, data entries or entities having a specific meaning within a domain of knowledge or discourse, together with the relationships that exist among them. Point Cloud: Dataset composed by points located in a three-dimensional coordinate system derived from both a range and an image based geometrical survey. Semantic Web: Extension of the current web to promote the cooperation between computers and people based on well-defined meaning of the information.

This research was previously published in Impact of Industry 4.0 on Architecture and Cultural Heritage; pages 306-329, copyright year 2020 by Engineering Science Reference (an imprint of IGI Global).

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The Future of Tourism Guidance in the Scope of Industry 4.0 and NextGeneration Technologies Yunus Topsakal https://orcid.org/0000-0003-3202-5539 Adana Alparslan Türkes Science and Technology University, Turkey Mehmet Bahar Cappadocia University, Turkey Nedim Yüzbaşıoğlu Akdeniz University, Turkey

EXECUTIVE SUMMARY Next-generation technologies such as robotics, the internet of things, artificial intelligence, sensors, cognitive technologies, nanotechnology, quantum computing, wearable technologies, augmented reality, intelligent signaling, and intelligent robots have led the fourth industrial revolution, often referred to as Industry 4.0. With the rapid advance of technology, most people today rely heavily on the internet to get information while traveling anywhere, because the use of technology has deeply penetrated daily life. The internet also makes travel easier and more convenient. For instance, it is possible to plan travel using smartphones and applications and at the same time meet instant travel needs as they arise. Therefore, the aim of this study is to examine tourism guidance within the scope of the super-smart tourists of the future, to determine the usage areas of next-generation technologies in the field of tourism guidance, and to give recommendations for tourism guidance in this regard.

DOI: 10.4018/978-1-7998-8548-1.ch071

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 The Future of Tourism Guidance in the Scope of Industry 4.0 and Next-Generation Technologies

INTRODUCTION Technology has a significant impact on human lives due to its continuous and rapid development, and therefore technology may be regarded as an unpredictable and important force that people must pay attention to (Hooijdonk, 2015). For example, while the number of mobile phone users in the world was 2.32 billion in 2017, it is expected to exceed five billion by the end of 2019 (Statista, 2018), showing the importance of mobile applications for economic industries. Smart technologies are accepted almost without notice by users and may be found in almost all areas of life today. Tourism destinations have also started to implement smart technologies in order to prevent ongoing climate change and to enrich the experience of tourists within the framework of European Union objectives (Endesa, 2008). Next-generation technologies such as robotization, the Internet of Things (IoT), artificial intelligence, sensors, cognitive technologies, nanotechnology, Internet services, quantum computing, wearable technologies, augmented reality, intelligent signaling, intelligent robots, big data, 3D technology, and intelligent networks have led to the fourth industrial revolution, known as Industry 4.0. These Industry 4.0 technologies have started to change business environments and lifestyles due to their rapid use in business life, communication, and education. Therefore, countries, governments, local administrations, enterprises, and educational institutions have started working to adapt to Industry 4.0 technologies. The term “smart” also entered the literature with Industry 4.0. Höjer & Wangel (2015) state that technological advances are not so significant on an individual basis; rather, different technologies used in a connected, synchronized, and harmonious way impact our lives in a unified fashion. Harrison et al. (2010) define the concept of “smart” as using real-time and real-world data, integrating data, sharing data, and using analytics, modeling, optimization, and visualization to make better operational decisions. The term “smart” has become a word that describes the technological, economic, and social developments brought about by sensors, big data, open data, and new technological avenues for communication and information exchange (e.g., the IoT, GPS, and NFC) (Gretzel et al., 2015). In recent years, intensive efforts have been made for the use of information and communications technology (ICT) in the tourism industry (Stamboulis & Skayannis, 2003). However, there are limited studies addressing how smart applications affect tourism. Considering the fact that tourism represents a significant source of income in developing countries, more attention should be paid to the adaptation of the tourism industry to next-generation technologies. Especially with the increase in the number of “Z generation” tourists, technology has found a place in the tourism sector. In this context, the concept of the super-smart tourist, also called Tourist 5.0, has been discussed in the literature. The expectations of professional tourism guides by super-smart tourists in the context of next-generation technologies have started to change. In this context, this study will first address how industrial revolutions have influenced tourism. Information is then given about next-generation technologies. Finally, the effects of next-generation technologies on professional tourism guides are discussed.

BACKGROUND A Brief Overview of the Effects of Industrial Revolutions on Tourism The first industrial revolution (FIR) began in England in the 1750s (Leighton, 1970). In 1781, James Watt patented the steam engine, the driving force behind the FIR. Steam engines were then used in the 1423

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first train, boat, and agricultural machinery within the next 30 years (Roberts, 2015). While the steam engine was a driving force of the FIR, the textile industry was another force (Outman & Outman, 2003). The basic facilitating technologies of the FIR were the change in power supplies (steam engines running on coal and wood fuel). The FIR was focused on textiles and iron manufacturing. It aimed to increase productivity, mostly in terms of mechanical tools, and it spread a new standard of living across England from the 1750s onward (Tien, 2012). During this period, with the development of tourist transport in San Sebastian in 1820, the bath/spa facilities were frequented by many more tourists (Larrinaga, 2005). Seaside visits also started with bath facilities and spas. In these years, seaside travels became valued for health, recovery, and fashionable recognition (Beckerson & Walton, 2005). The second industrial revolution (SIR) focused on assembly lines, as specifically designed by Henry Ford, and steel production; it aimed to increase productivity in mass production. The first effects were felt in the USA and Germany in 1860 (Tien, 2012). With the SIR, fields of business began to shift from manufacturing to services (Blinder, 2006). The main facilitating technologies of the SIR included the change in power supplies (electric power) and transportation (railways, automobiles), developments in iron and steel production, and the invention of the lightbulb. According to Janicke and Jacob (2009), the SIR enabled mass production and mass earnings. Cunard, a British cruise line, made its first transatlantic voyage in 1840. Passenger transport developed largely due to migration, but it has been used heavily for tourism purposes since World War I (Gierczak, 2011). Transatlantic travel accelerated in the 1860s and the concept of the “big tour” emerged. Hotel Ezcurra in 1870 and Hotel Bermejo in 1884 started to serve customers in Spain for tourism purposes, while in 1881 the Hotel de Inglaterra and in 1884 the Hotel Continental were introduced (Larrinaga, 2005). The Castle Line maritime company published the “Guide to South Africa” guidebook for the first time in the 1890s to guide tourists, athletes, and immigrants (Mackenzie, 2005). The third industrial revolution (TIR) began in the last half of the 1950s and aimed at increasing the national and international operations of US companies (Leighton, 1970). The term “TIR” was first used by the economist Rifkin (2011) and the TIR was explained as the integration of Internet technology with renewable energy, aiming to increase productivity in mass customization (especially the integration of services and manufactured goods), and to use computer and communication technologies that can work in the long term and are mostly connected to brain power in enterprises (Tien, 2012). During the TIR period, after 1945, tourism entered a new phase of development with collective travel. In 1953, the Vickers Viking, a twin-engine aircraft seating 36 people, was designed to take tourists on a two-day tour of Lyon, Barcelona, Madrid, Tangiers, Casablanca, and Agadir (Gierczak, 2011). The fourth industrial revolution was triggered by the development of ICT. Industry 4.0 can be understood as a series of technology-based smart automation and cyber-physical systems. The term “Industry 4.0” was first used at the Hannover fair in 2011 (Rojko, 2017). With Industry 4.0, digitalization has begun to change the way we do business in information management and production/service, because technologies such as light robots, tablet computers, transponders, sensors, and artificial intelligence have become more economical for enterprises (Schlund et al., 2014). ICT is now significantly affecting and changing the tourism industry. Rapidly developing smartphone applications particularly provide tourists with many benefits, such as destination guides, address finding, exchange rate calculation, and hotel or flight booking. Smartphones search for information, social network connections, navigation help, and so on. Smartphones now offer a wide range of services and can support thousands of mobile applications (Wang et al., 2012). Kramer et al. (2007) found that tourists’ choices could easily be changed with smartphones. 1424

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Next-Generation Technologies of Industry 4.0 Big Data In recent years, the tourism industry has been rapidly adapting and adopting technological developments. Therefore, enterprises and managers operating in the tourism industry should use ICT and the Internet at maximum levels. For example, visitor information, visitor needs, and visitor email addresses and phone numbers are stored in the tourism industry. In addition, businesses operating in the tourism industry can collect information from a variety of sources through social media accounts, representing a large amount of data. Tourism enterprises and actors in the tourism sector can use that “big data” for promotion, marketing, competitions, and the following of trends (Topsakal, 2019). The benefits of big data for smart tourism and destinations are constantly becoming more obvious. From the planning of a journey to its realization and across the whole process, a large amount of data is released about accommodation, entertainment, and restaurant services, and that external information is published via various social media tools, leaving a digital mark by tourists on web-based and mobilebased services (Esen & Türkay, 2017). Big data usage is the most innovative tool to date to identify consumer behavior and improve process efficiency. The main concerns for the tourist are satisfaction with the services, keeping costs to a minimum, gaining recognition or improving their reputation, and seeing that enterprises’ commitments are fulfilled (Sheoran, 2017).

Internet of Thing (IoT) The Internet of Things (IoT) means that everything is connected to all other things over the Internet without limitations of time, space, or presence. The IoT can communicate across devices and collaborate to achieve common goals. Radio frequency identification tags, sensors, processors, chips, mobile phones, and mobile devices are some examples of the common presence of the IoT among the objects that surround us. Since these things are all connected to the Internet, they close the gap between the real world and the digital world. Therefore, the IoT facilitates the development of a variety of platforms capable of transmitting a wide range of various types of data using participating detection systems (Gretzel et al., 2015a). The IoT provides support for information collection, analysis, automation, and control. For example, a chip embedded in entrance tickets or a smartphone application may allow tourism service providers to track tourists’ locations and consumption behaviors. This also allows for location-based rescues in the event that a tourist leaves a designated route (Masseno & Santos, 2018).

Cloud Computing Cloud Computing services are designed to provide an easy way to access online storage. Cloud computing provides access to robust web platforms and data storage through a public electronic communications network (Masseno & Santos, 2018). Cloud computing centers offer fairly centralized user information, and tourists are thus at risk of the violation of important personal privacy when using cloud resources. When cloud computing is implemented within smart tourism, a primary challenge is to ensure the security and authentication of personal information (Liu & Liu, 2016).

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Blockchain A blockchain is a constantly growing list of records, called blocks, that are linked and secured using cryptography. Blockchains provide a secure distributed database that can function without a central authority or administrator. Blockchains use a distributed peer-to-peer network to make a continuously growing list of regular records, called blocks, to create a digital ledger. Each transaction represented in a cryptographically marked block is then automatically verified by the network itself (Angraal et al., 2017). The future of the tourism industry in light of developing blockchain technology is focused on four basic points. These are simplified and safer traveler identification, improved baggage tracking, more userfriendly customer loyalty programs, and the facilitating of payments between travel agencies and airlines. Blockchains can bring advancements to the tourism industry in these four realms (Blockchain, 2017).

Mobile Applications Specially prepared software that provides mobile services through mobile devices, especially smartphones, is called an “application” (Topsakal, 2019a). The increasing diversity of travel practices has enabled individuals to access, buy, and share knowledge anytime and anywhere (Lu et al., 2015). Tourism mobile applications help users plan and map their travels and make accommodation reservations, ticket reservations, taxi reservations, and more. Mobile applications can also improve service quality by marking the right places in tourist destinations or forecasting reservations and waiting times at various tourist sites (Amanda et al., 2018). Mobile applications can be divided into mobile applications specifically targeting tourists (e.g., Airbnb, TripAdvisor, Skyscanner) and those often used in typical travel contexts (e.g., Google maps, iMoney, Instagram) (Lu et al., 2015). Applications developed for tourism provide visitors with services such as geographical positioning, socialization, communication, security, emergency assistance (information, medical), transactions (shopping, tickets, finance, banking, etc.), information, and entertainment (Topsakal, 2019a). For example, Airbnb (“Air Bed and Breakfast”) is the most popular home rental application. It was founded in San Francisco, California, in August 2008, and is now a marketplace that offers the option of listing, discovering, and renting from thousands of accommodation options online or via the smartphone app worldwide. It is possible to rent houses with different price options in more than 25,000 cities and 192 countries via Airbnb, whether it is an apartment for a night, a boat, a tent, a castle, a lighthouse for a week, or a villa for a month (Airbnb, 2018).

Near-Field Communication (NFC) NFC technology allows tourists to browse information points about paintings, sculptures, or historical artifacts and it can be used in touristic places such as museums where the smartphone can provide information about an object in the desired language. At a train station, for example, NFC posters may be available and thus a user can be directed to an online site with information on routes, travel times, and prices using a smartphone at the station (Smith, 2015). With the use of a mobile phone, using panels placed at bus stops, NFC can provide transportation schedules, waiting times for arrival, ticket purchasing information, and so on. When a tourist approaches a work exhibited in a museum, he can get information about it, or he can pay by phone in a restaurant after eating (Çelik & Topsakal, 2019).

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QR Codes (Quick Response Codes) QR (quick response) codes as a specific form of mobile code provide solutions for connecting physical and virtual content to provide users with additional information or access to mobile services (Canadi et al., 2010). Tourists can access information about their destinations via mobile devices by scanning the QR code. They can access various contents (text, videos, links, any data or webpages) with barcode-reading software installed on their mobile devices. Differences between NFC and QR code show in Table 1. Table 1. Differences between NFC and QR code NFC

QR Code

The NFC interface is integrated into almost all mobile phones.

To read the QR code, a special application is needed on the mobile phone.

NFC technology has been standardized worldwide and offers a wide range of applications in areas such as industry, logistics, marketing, automotive

The QR-code can optically downgrade the customer layout

No special application required

The QR code is susceptible to dirt which reduces the first-pass read rate significantly.

NFC is based on ISO14443 standard or ISO15693 and according to the International Transmission standard, the frequency is 13.56 MHz.

QR code data cannot be changed so that they are static and cannot be changed.

Maximum distance up to 5 cm and fast transmission establishment

Long website URLs affect a very large QR code, which leads to poor read rates.

Each NFC chip has a unique ID number worldwide. Thus, each product is unique, retractable and becomes an original.

The QR Code can be processed in two steps, first opening the scanning program and second scanning the code.

A virtual 100% first pass reading rate

The cost of QR codes is low.

NFC provides the possibility to add, read and modify data on the chip at any time.

Once created and cannot be changed again

Source: “NFC-Technology”. n.d.

Kiosks Electronic touch screens are a form of smart information systems. Especially in smart destinations, these touch screens placed at points such as important tourist attractions, airports, shopping malls, and tourist information centers provide much information about the destination to tourists. Electronic touch screens are systems that can improve the experience of tourists during their stay at their destination and provide different information. An advantage of electronic touch screen technology for visitors is that it provides information 24 hours a day (Rodrigues et al., 2017).

Smart Tourist Cards The creation of smart tourist cards standardizes experiences and makes it easier for tourists to learn about options at their destinations (transport, guides and excursions, visits to museums, performances, etc.) (Segittur, 2019). Tourist cards, which help tourists gain unique perspectives of their destinations,

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provide a number of benefits and advantages to users in terms of time and cost savings, and they stand out in making experiences more successful. Tourist cards are important destination marketing tools, combining various services and activities provided at the destination at an affordable price. The main facilities provided by tourist cards include free or discounted entrance to museums, churches, and historical sites; free use of public transport; map guides with tourist attractions, restaurants, shops, and amusement parks; guided tour activities; and information or discounts for car rentals, bike rentals, or city parking. Given the large number of tourist services and facilities available via a tourist card, it is an essential marketing tool for the success of destinations (Topsakal et al., 2018a). A smart card will be either a chip card or a QR code, or a mobile application.

Augmented/Virtual Reality Augmented/virtual reality in smartphones may be used to provide information about points of interest (Sato et al., 2017). By using virtual reality and augmented reality technologies, visitors can find their destinations in virtual space. Geographical information systems, on the other hand, combine the location data of a site with its geographic location and the properties of the object and provide a wide range of information about the destination. Smart tourist guide services based on geographic information systems and mobile tour guide applications have been developed to provide digital tourism before, during, and after travel (Jwa, 2016). Augmented/virtual reality plays an important role in tourism, especially in the promotion of cultural heritage in a colorful and realistic way. For example, in an ancient Roman theater, an environment in which users can watch a gladiator fight in the arena through their smartphones can be achieved by digital reconstructions provided by augmented reality. With augmented reality, historical places can be presented to visitors on a digital platform. Augmented reality also has the ability to guide visitors through applications such as Google Maps that offer location-based services (Smith, 2015). Before booking accommodations, tourists can visit the hotel rooms with augmented reality. Using maps created by augmented reality while navigating a destination, they can enjoy a more in-depth experience with arrows, direction signs, and digital elements. Augmented reality multimedia can personalize interactive information in order to enhance tourists’ experiences (Çelik & Topsakal, 2019).

Global Positioning System (GPS) The location-based applications have started to develop with the introduction of features such as GPS and near-field communication (NFC) on mobile devices. The best examples of location-based applications are public transport applications. SmartCity public transportation applications can use GPS to locate public transport routes passing by a location in real time. In terms of tourism, the Foursquare and Swarm applications can show location-based suggestions for restaurants, cafes, and shopping centers together with information about these places. Socialeyes, Zomato, Tinder, and Glympse are other examples of applications that offer different location-based services (Topsakal, 2019b). On the other hand, mobile tour guides provide tour information and routes to help visitors easily find and navigate the points of interest around them as they travel around their destinations (Sun et al., 2016). In this context, Shen et al. (2016) propose the IoT and big data for developing a system of smart tourism and sustainable cultural heritage.

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Beacon One of the latest technologies being used today is beacon technology, a Bluetooth-based interaction technology. Beacons are placed at certain points and emit Bluetooth radio waves. Thanks to these waves, personal discounts can be offered to visitors entering an area by the surrounding businesses in line with their preferences. In order to receive these notifications, it is necessary to download an application such as a smart tourist guide to the smartphone and enter one’s preferences (Çelik & Topsakal, 2019).

Humanoid Robots Humanoid robots started to be developed with Industry 4.0 and rapidly began being used in daily life and by enterprises. There are examples of robots in businesses. For example, a new type of robot called Sawyer has the features of automatically saving a job once it is shown the job, and unlike its predecessors, it can learn and speed up its work by extracting the results from each job that it performs. Therefore, Sawyer specializes as people perform jobs, and it shortens the time needed to perform the work while increasing the quality. Assuming that a person does a job at best in a week, Sawyer can fit it into a few hours. In addition, this robot can share experiences with its similar siblings, allowing them to start a job with excellent experience from the very beginning. From this point of view, robot guides that are being developed thanks to learning robots will update their knowledge and data day by day with interactive dialogues with tourists and the knowledge and experience they acquire, and with each passing day their work will continue to improve. It will also be possible to transfer this gained experience to robot peers providing similar services automatically and simultaneously (Sawyer, 2019).

MAIN FOCUS OF THE CHAPTER Concept of Smart Tourism The concept of a “smart city” encompasses many industries; in this respect, it also includes the tourism industry (Boes et al., 2015; Buhalis & Amaranggana, 2014). Boes et al. (2015) define a smart city as a connected system that connects all local organizations to provide real-time services to visitors and citizens at all times. The development of a smart city facilitates seamless access to value-added services, such as access to real-time information about public transport, enriching tourist experiences and increasing the competitiveness of destinations (Buhalis & Amaranggana, 2014). Smart tourism includes tourist activities supported by smart technology (Gretzel et al., 2015b). Buhalis (2015) states that a smart tourism destination is a competitive tourism destination aiming to increase the social, economic, and environmental welfare of all stakeholders by successfully implementing the knowledge created by innovation supported by investments in human and social capital. According to Washburn et al. (2010), technology in smart tourism should be viewed as infrastructure rather than individual information systems, and it includes a variety of smart ICT for integrating hardware, software, and other technologies to enable people to make smarter decisions about alternatives by providing real-time awareness in the real world. Smart hotel management systems (Topsakal et al., 2018a), smart ticket (card) systems (Topsakal et al., 2018b), smart remote video monitoring systems, smart tour guide systems (Yüzbaşıoğlu et al., 2018), and smart travel agencies are used to create smart tourism (Gretzel, 1429

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2011). In short, next-generation technologies operate on the basis of efficient and effective communication in real time. McCartney et al. (2008) state that smart tourism destinations are an important element of smart cities and Guo et al. (2014) explain that smart tourism destinations represent an important smart city strategy. Smart tourism can be created by the application of next-generation technologies in the fields of service, business, management, and governance (Yao, 2012). Smart cities may differ from smart tourism destinations because smart cities do not take tourists into account, focusing only on citizens. Smart tourism destinations can thus be seen as a derivative of smart cities. Boes et al. (2016) state that a smart city focuses on its citizens while a smart destination focuses on improving tourist experiences through ICT. Different technologies can be found at smart tourism destinations. Technologies such as QR codes or NFC tags provide connections between the physical and digital worlds. Thus, the value of the tourist experience increases (Chillion, 2012). Access to information about nearby points of interest is made easier by the new generation of ICT (Buhalis & Amaranggana, 2014). In the case of applications, distance has no negative aspects as it did before. A smart tourism application with augmented/virtual reality can enable visitors to enjoy real-time experiences at different locations and at different times (Chillion, 2012). Smart tourism destination framework has shown in Figure 1. Su et al. (2011) identify different main applications such as green cities, smart medical treatment, smart transportation, wireless cities, smart homes, smart city management, smart public services, and smart tourism in their study on the importance of creating smart application systems. Basically, a smart city should integrate human capital, infrastructure, and knowledge (Buhalis & Amaranggana, 2014). Since the tourism industry is one of the most suitable industries in which ICT is used extensively, the idea of ​​smart tourism destinations has found application areas quite quickly (Koo et al., 2016). Since smart tourism destinations offer unique personalized experiences from real-time data based on ICT, each visitor can experience heterogeneous experiences even at the same destination. Therefore, the idea of ​​a smart tourism destination can be important for destinations where attraction centers are very different and especially for locations difficult to link and market (Koo et al., 2016). The concept of smart tourism destinations has emerged due to the development of smart cities (Buhalis & Amaranggana, 2014). While the smart city approach focuses on citizens, it may also include tourists, and the smart tourism destination approach focuses on tourists and passengers (Lamsfus & Alzua-Sorzabal, 2013). Boes et al. (2015) describe smart tourism destinations as platforms that implement ICT such as artificial intelligence, cloud computing, and the IoT to provide personalized information and advanced services created by mobile end-user tools. Lopez de Avila (2015) defines smart tourism destinations as innovative tourism destinations built on state-of-the-art technology infrastructure that is accessible to all, facilitates the sustainable development of tourist areas, and allows the interaction and integration of the visitor with the environment. The concept of smart tourism is defined as providing open information to tourists by integrating tourism resources with ICT such as artificial intelligence, cloud computing, and the IoT (Zhang et al., 2012). Research has been conducted in many areas related to smart tourism, including the following types of studies: • •

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Smart tourism and smart city studies defining the application of the smart city concept and technologies in the context of tourism (Topsakal et al., 2018a); Studies on smart tourism destinations (Topsakal and Çelik, 2017);

 The Future of Tourism Guidance in the Scope of Industry 4.0 and Next-Generation Technologies

Figure 1. Smart Tourism Destination Framework Source: Boes vd., 2016

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• • • • • •

Studies on smartphone applications in tourism, focusing on the functionality and adoption of smartphone applications (Topsakal, 2018); Studies on smart hotels that examine the use of information technologies in the hospitality industry and digital marketing decisions of smart hotels (Topsakal et al., 2018b, Topsakal et al., 2018c); Studies related to smart cards that can be used in the tourism industry (Topsakal et al., 2018d); Studies on gamification, augmented reality, and smart tourism to use augmented reality technologies (Topsakal et al., 2018d); Studies on smart suggestions for tourists searching for personalized destination suggestion systems combining individual preferences and geolocation data (Yüzbaşıoğlu et al., 2018); Studies on smart guides defining content-based information applications for smart tourist guidance (Yüzbaşıoğlu et al., 2018).

Super-Smart Tourist (Tourist 5.0) As part of the Fifth Science and Technology Basic Plan (FY16-FY20), announced in April 2016, the Japanese government introduced a new society, which they called Community 5.0 or Super-Smart Society. Community 5.0 provides a common community infrastructure for prosperity based on an advanced service platform. Wang et al. (2018) explain that the concept of Community 5.0 emerged in 2015 with a strategic national political initiative in Japan. Society 5.0 emerged following Industry 4.0; while Industry 4.0 focuses on production, Society 5.0 places people at the center of technology with the aim of improving quality of life and the sustainability of the results of Industry 4.0 (Costa, 2018). According to Hayashi et al. (2017), Society 5.0 aims to create new values ​​by collaborating with several different systems of Japan. In addition, smart property development, international standardization, IoT technologies, big data analysis technologies, artificial intelligence technologies, and similar developments are expected to encourage Japan’s competitiveness in “super-smart society”. Smart tourism technologies have also started to change the behavior of tourists and have created a specific segment of visitors, which are considered as a new type of tourist. Basic tourist demands in the information age include the following (Wang et al., 2016): • • • • • • • • • •

Keeping track of personal travel preferences and timing, Waiting less and experiencing fewer delays, Searching for travel information via the Internet, Online ticket booking and room booking, Online shopping, Price comparisons via different travel sites, Communication via virtual travel communities, Access to complaint management systems, Multimedia service queries, Mobile facilities and applications such as Wi-Fi, short message services, and multimedia messaging services.

The basic needs of the super-smart tourist include the following: information about the city (the state of beaches, instant alerts); emergency telephone numbers; public Wi-Fi maps; maps of tourism offices, hospitals, and local government buildings; real-time maps, schedules, and information for 1432

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urban transport (metro, buses, bicycles, tourist buses, railways); real-time information about harbors, airports, and trains; traffic and road data; weather information; tourist information (hotels, restaurants, museums, tourist attractions, general interest, and entertainment); and local social networks (Mussina & Oryngazhiyeva, 2018). According to Femenia-Serra et al. (2019), a super-smart tourist is a tourist with smart experience who interacts with stakeholders by sharing smart data. This tourist is open to innovation and is social and proactive. In this context, it can be said that the super-smart tourists will be from the Z generation, a group of fast-learning and fast-developing people born in the mid-1990s and 2000s. Considering the general characteristics of generation Z (Bahar, 2018): • • • • • • • • • • • • • • •

They learn fast, They are confident and have high self-confidence, They are not prone to teamwork, They give more importance to education and social status, They like to act independently, They have an inward-looking structure and prefer to communicate via social media, They cannot make friends easily, Technology is not a luxury but a need for this generation, They know what they want, They believe that everything is possible in life, They have high creativity, They like to access information first-hand, They find rules boring, They avoid work and social environments that do not value creativity, They do not like standards.

Potential Effects of Next-Generation Technologies for Tourism Guidance The travel and tourism industries have been heavily influenced in recent years by new technological developments, such as the mobile technology revolution (Lamsfus et al., 2015). For example, visitors using smartphone applications can receive tourist information, promotions, and geolocation information; they can share their experiences instantly through applications or they can purchase tourist services through applications (INVAT.TUR, 2015). Mobile technology allows people to plan their travels and interact more with others (Gretzel, 2011). In this context, smart tourist guide applications are becoming widespread in many places. In cities like Malaga and Barcelona, ​​local tourism authorities have created smart guide applications. These applications are installed on a smartphone by the user before traveling to the destination. In doing so, tourists can explore the area before arriving and learn about the main attractions, as well as information on local resources. The Malaga tourism guide application combines written text with sound that can be listened to in advance or in a particular touristic area (Ayuntamiento de Malaga, 2015). The iBarcelona Smartour application (Barcelona Turisme, 2015) is a mobile tourist guide application with some different features. iBarcelona provides augmented reality services that enhance the user’s experience through visual effects. Other smart tourist guides that are in use like those of Malaga and iBarcelona include the following:

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•

•

•

•

•

The Deep Map app is a mobile system that can create personal guided tours in the city of Heidelberg, Germany. This app creates a tour by taking into account personal interests, social background (e.g., age, education, and gender), and type of transportation. It enhances the experience of tourists by reconstructing points of interest in 3D, such as virtual time travel (Malaka & Zipf, 2000). CRUMPET is designed to provide personalized information and service to tourists. It offers descriptions, maps, directions, and pictures for each area of interest. In the first use, tourists learn more about setting their user preferences when providing demographic information, traveling, and interacting with the system. Recommendations are based on personal interests and current location, obtained via GPS (Poslad et al., 2001). The MoreTourism app provides information about tourist resources based on user profile, location, schedules, and visiting times. The system consists of two parts: a mobile device using Android OS and a server that provides system-specific functions. It provides socialization and advertising as well as information. First, it allows users to interact through social networks and create activity groups in specific locations. Second, it offers different advertising campaigns according to the user profile (Rey-Lopez et al., 2011). MyTourGuide has been developed for collaborations with both visitors and tourism. These collaborations match visitors and businesses, and the app acts as an intermediary between tourists and tourism-related parties such as hotels, restaurants, car rental services, and attractions. The stakeholders of the app can share tour information and promote their latest activities, services, or products (Husain et al., 2012). GoTour is a smart tourism guide application designed for the city of Istanbul and works on Android and mobile devices. It provides information about points of interest, consulates, and city services. Based on location, it suggests tour plans for tourists while taking into account the visitor’s areas of interest, distance, and time (Al-Rayes et al., 2011).

It can thus be said that super-smart tourists will join the tourism movement within the scope of Community 5.0 and Industry 4.0 technologies. When Industry 4.0 technologies and generation Z are examined together, it can be foreseen that the following changes will occur in tourism guidance in the future. First, smart tourist guide applications, which have already been developed with the new generation of technologies, will now be able to offer location-based guidance to visitors with more advanced software. For example, the user can plan appropriate tours based on a GPS location and personal interests, entering the time that the user can allocate for the visit. In this way, the visitor will be able to save time as well as enrich his or her experience. Taking into account the length of the stay, as a visitor enters a hotel with a passport number, he or she could obtain guidance services on the room’s TV screen for the places that he or she plans to visit. In addition, it would be possible to join existing planned trips as an additional participant in the group if information is available via the TV screen about tour programs planned in the area. In this way, while reducing costs, visitors will be able to meet new people while seeing new places. Tourist flow could also be controlled by this method in order to not exceed the carrying capacity experienced at certain times of day. The automatic translation of speech into text is now very smooth with next-generation technologies. Therefore, by means of very fast processor devices or applications, it will be possible to provide guidance services to tourists in different languages by converting a main text into alternative languages very 1434

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quickly. In this way, guidance services can become a true cultural service rather than a language-based service so that individuals who have sufficient tourism guidance certification in any language can provide service to everyone in the customer’s desired language. In addition to departments such as reception services being predicted to vanish within the scope of smart tourism, traditional tourism guidance services will also disappear completely due to reasons such as the development of next-generation technology, changing visitor profiles (particularly the participation of generation Z in tourism mobility), and the demand for personalization of services. In particular, with Apple’s voice-help service via the iPhone’s Siri, it is not a stretch to imagine dialogues like “Siri, show me around Topkapı Palace”. The classical tourism guidance profession might therefore begin to seem outdated or antiquated, like other “old-fashioned” professions such as tinsmith and coppersmith work. The need for a physical guide will be replaced by the need for a visual guide With next-generation technologies. That need can be met interactively in 3D by hologram guides. In recent years, the first humanoid robot, Sophia, was granted citizenship by Saudi Arabia, and virtual guidance by humanoid robots with “guidance badges” is sure to follow.

SOLUTIONS AND RECOMMENDATIONS Online guidance services can be provided at destinations with videos prepared within the scope of Industry 4.0 for guided tours or with a live video connection. In this way, the obligation of the guide and the tourist being together in the same place is overcome. Interactive guidance services can be provided to visitors independent of time and place in any language with the applications that have been developed. When you visit Aspendos, for example, you will be able to join a tour group for a small fee after receiving a message through an application: “Do you want to participate?” This will provide both the price advantages of group membership and the freedom of individuality. Today, the consumption tendencies of visitors and the understanding of the service sector are moving in the direction of the personalization of the service provided/received. Therefore, if the visitor chooses a travel time and the amount of time to be devoted to an activity, and if the visitor also identifies areas of interest, the application will prepare a virtual tour, especially focused on the areas of his or her interest in an optimal way. Once these trends are entered into the system by the visitor, warning messages can be sent by the application along the journey and the routes that have been traveled can be recorded or explained, even if no tours or activities are planned. Thus, such applications will benefit both visitors and tourism stakeholders. Stakeholders participating in the implementation of these features will be directed to use NFC and beacon technology. In this context, the following recommendations may be made: • • •

Applications are now expected to offer the most suitable tour programs to visitors with new technologies. Therefore, countries should develop travel and scheduling plans in touristic regions and develop applications specific to destinations with software supported by expert guides. Industry 4.0 technologies and the time limits of the Z generation, demanding everything immediately, mean that hotels and guides should work together to provide guidance and tour services to visitors more quickly. It can now be said that the general language of the world is technology. It is inevitable that the next-generation technologies developed with Industry 4.0 will be applied in the sector; in fact,

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•

these technologies have already started to affect tourism, like all other industries. Thus, tourism guidance education curricula should be harmonized with the requirements of Industry 4.0. Virtual and hologram guides should be prepared. Those who cannot physically provide tourism guidance can do so virtually, particularly with the help of translation programs that will make it possible to offer such guidance in any requested language.

FUTURE RESEARCH DIRECTIONS In this study, Industry 4.0 and the next-generation technologies in use have been examined and some suggestions have been made about the potential effects of next-generation technologies on tourism guidance. In this context, this study is a conceptual study. Although new technologies have potential effects for tourism guidance in the tourism industry, they are not being widely utilized yet. A model proposal study could be realized for tourism guidance with next-generation technologies. These technologies could be examined separately for tourism guidance education, tourism guidance standards, etc. In addition, the perceptions and expectations of tourism guidance regarding next-generation technologies can be determined by field studies or surveys with tourism guides and tourists.

CONCLUSION Technology has emerged as both a trigger and a main force for tourist destinations (Kuflik et al., 2015). For this reason, the tourism industry is undergoing a technological transformation in order to offer its services more easily and faster because the tourism industry is growing with increasing knowledge (Benckendorff et al., 2014). Today, ICT has become an integral part of the tourist experience; this is because tourists have begun to use different technological tools to first plan their travels and then to better experience their destinations and to share them afterwards (Wang et al., 2014). Therefore, it can be said that future tourism guides will be influenced by the next-generation technologies that form the basis of the fourth industrial revolution. In this context, both the training of tourism guides and the standards of tourism guidance should be harmonized with next-generation technologies.

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ADDITIONAL READING Ang, S. C., & Zaphiris, P. (2009). Human Computer Interaction: Concepts, Methodologies, Tools, and Applications (Vols. 1–4). IGI Global. doi:10.4018/978-1-60566-052-3 Guerra, A. G. (Ed.). (2019). Organizational transformation and managing innovation in the Fourth Industrial Revolution. IGI Global. doi:10.4018/978-1-5225-7074-5 Mezghani, K., & Aloulou, W. (Eds.). (2019). Business transformations in the era of digitalization. IGI Global. doi:10.4018/978-1-5225-7262-6 Rodrigues, J. M., Ramos, C. M., Cardoso, P. J., & Henriques, C. (Eds.). (2017). Handbook of research on technological developments for cultural heritage and etourism applications. IGI Global. Ruiz, G. R., & Hernandez, M. H. (Eds.). (2018). Augmented Reality for Enhanced Learning Environments. IGI Global. doi:10.4018/978-1-5225-5243-7 Sabri, E. (Ed.). (2019). Technology optimization and change management for successful digital supply chains. IGI Global. doi:10.4018/978-1-5225-7700-3

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KEY TERMS AND DEFINITIONS Industry 4.0: The fourth industrial revolution, led by next-generation technologies such as robotization, the Internet of things, artificial intelligence, sensors, cognitive technologies, nanotechnology, Internet services, quantum computing, wearable technologies, augmented reality, smart signaling, humanoid robots, big data, 3D technology, and intelligent networks. Smart Tourism Guide Application: Applications developed with next-generation technologies that offer guidance services based on location with more advanced software. Smart Tourist Cards: Smart cards that combine various services and activities provided at a discounted price in an integrated manner, which can be either a chip or QR code, or a mobile application. Super-Smart Tourist (Tourist 5.0): A tourist who provides smart data by interacting with stakeholders in the production process by using next-generation smart technologies. Virtual/Hologram Tourism Guide: Tourism guides that provide tourist guidance in a 3D interactive way via the new generation of technologies regardless of time and place.

This research was previously published in Cases on Tour Guide Practices for Alternative Tourism; pages 281-302, copyright year 2020 by Business Science Reference (an imprint of IGI Global).

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Chapter 72

Accessible@tourism 4.0:

An Exploratory Approach to the Role of Industry 4.0 in Accessible Tourism Pedro Teixeira https://orcid.org/0000-0003-4395-4296 University of Aveiro, Portugal Leonor Teixeira https://orcid.org/0000-0002-7791-1932 University of Aveiro, Portugal Celeste Eusébio https://orcid.org/0000-0002-2220-5483 University of Aveiro, Portugal

ABSTRACT This chapter describes how Tourism 4.0 is a concept that combines tourism and the fourth industrial revolution, and although the literature in this field is very scarce, this concept has been explored in some research projects, such as the government-sponsored research project in Slovenian tourism. People with various kinds of access requirements represent a combination of challenges and opportunities for the tourism industry. Tourism 4.0 set up the main goals of making tourism accessible to everyone at any time. Therefore, this new phenomenon may have an essential role in the development of accessible tourism. The adoption of technological components in accessible tourism enables the development of a new technological solution that can facilitate access to tourism products for disabled people, contributing to the development of accessible tourism. The new term Accessible@Tourism 4.0 is the answer to the role of the fourth industrial revolution in accessible tourism, emphasizing the effect of Industry 4.0 components in the tourism sector.

DOI: 10.4018/978-1-7998-8548-1.ch072

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 Accessible@tourism 4.0

INTRODUCTION Currently, there is an ongoing technological revolution that may ultimately change society as a whole. The fourth industrial revolution is evolving at high speed and will lead to a deeply connected world. A significant change in the way that society works and communicates is happening at the moment, and is having impacts on diverse manufacturing and service industries. This new industrial revolution results from a new paradigm called Industry 4.0 (Schwab, 2016:10). The drivers of this revolution came from innovations in different technologies, which are taking the world into a new era of digitalization. One sector that is being most affected by the new digital era is undoubtedly tourism (Ivanović, Milojica, & Roblek, 2017). The United Nations World Tourism Organization (UNWTO) defined tourism as: “a diverse industry, which is a central economic driver for socio-economic development in many areas/ destinations throughout the world” (UNWTO, 2009). Tourism is one of the economic activities that generates most income worldwide, often having a vital role in the economy of the countries. With the emergence of new technologies, the way of practicing tourism has radically changed. This new industrial revolution will bring not only benefits but also challenges for tourism. A big challenge is how it can contribute to the social inclusion of people with disabilities (PwD) in tourism practices. Tourism is an activity that enriches people in many dimensions. However, it is difficult for disabled tourists to travel without constraint. People with reduced motor/cognitive skills often end up unable to travel due to various factors such as transport and accommodation. These situations make it impossible for these people to explore and experience tourism. This chapter intends to explain how technology can improve accessibility in tourism and fight against the social exclusion of PwD. The main objective is to understand the impact of the fourth industrial revolution on accessible tourism, as the literature reveals that research in this field is currently very limited. The study of the connection between tourism and industry 4.0 led to the arrival of a new paradigm on tourism, named Tourism 4.0. With careful exploration of this new concept and its relationship with accessible tourism, it was possible to overcome the gap in the literature on how the fourth industrial revolution is transforming accessible tourism. The theoretical background provides interesting findings related to accessible tourism and the core concepts related to Industry 4.0 and the fourth industrial revolution. After the theoretical background, the chapter offers insights about the technological impacts of tourism, which led to the appearance of Tourism 4.0. A new concept, Accessible@Tourism 4.0, was created to explain the role of technological innovation in promoting accessible tourism. The chapter ends by discussing the main findings, possible limitations of this study, and recommends targets for future researches.

THEORETICAL BACKGROUND About Accessible Tourism Accessibility is a quality concept that may be interpreted differently from person to person (Persson, Åhman, Yngling, & Gulliksen, 2015). Some people think of accessibility as directly serving some of their needs, while others think of it in a more general way. The Convention on the Rights of People with Disabilities states in art. 9 that, in order to enable persons with disabilities to live their life independently and participate in all aspects of life, appropriate measures must be taken. To ensure accessibility condi1445

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tions for access for PwD, special attention should be given to “the physical environment, transportation, information and communications technologies and systems, and to other facilities and services open or provided to the public, both in urban and in rural areas” (United Nations, 2006). In addition, art. 30 implements the right to participate in cultural life, recreation, leisure, and sport. This convention supported the Manila Declaration on World Tourism signed in September 1980, where tourism was recognized as a fundamental right (UNWTO, 1980). Accessible tourism encompasses many other concepts. Buhalis and Darcy (2011, pp. 10–11) defined Accessible Tourism as “a form of tourism that involves collaborative processes between stakeholders that enables people with access requirements, including mobility, vision, hearing and cognitive dimensions of access, to function independently and with equity and dignity through the delivery of universally designed tourism products, services, and environments”. Accessible tourism seeks to include people with impairments and disabilities in travel and leisure activities (Milicchio, 2016). It is estimated that by 2020, around 25% of travel and leisure spending will come from PwD (Bekiaris et al., 2018). Demand in the accessible tourism market is increasing, which makes it a very attractive one. However, despite all the potential, the accessibility market is largely ignored (Buhalis & Michopoulou, 2011; Figueiredo, Eusébio, & Kastenholz, 2012). In fact, the accessibility market is not homogenous (Buhalis & Michopoulou, 2011), with different types of disabilities having different demands and requirements. Therefore, it is critical to divide the market into segments with similar needs and wants, in order to create customized offerings and suitable products (Buhalis & Michopoulou, 2011; Figueiredo, Eusébio, & Kastenholz, 2012). Segmentation can be seen as the key to competitive advantage (McKercher, 2008). According to Darcy and Buhalis (2011: 38), two leading authors in this field of study, seven clusters for people with disabilities/impairments can be identified: mobility; blind or vision impaired; deaf or hearing impaired; speech; cognitive (including mental health/intellectually/learning); hidden impairments and elderly/ seniors/boomers. Accessible tourism has been gaining more exposure in recent times, but there are still many obstacles that need to be conquered. Tourists, especially those with disabilities, face some significant barriers that negatively influence their touristic experiences and impede PwD from traveling and having new experiences. Almost half of all disabled tourists would travel more frequently if there were more barrier-free offers (Pühretmair, 2004). Sometimes, these obstacles are so challenging to conquer that they prevent people with impairments from traveling (Popiel, 2016), making these barriers a big issue. These obstacles exist, as they create “roadblocks” to tourism, threatening the quality of life and independence. Tourism practices recognize the need for information and physical accessibility, but barriers to access are complex and not reducible to the physical environment (Buhalis & Darcy, 2011, p. 50). In tourism, planning by people with impairments needs more detailed information, focusing on their individual/special requirements. Higher accessibility requirements demand more detailed and more specialized information. However, as found by Waschke (2004), the supply of specific information becomes scarcer for those with higher accessibility requirements. Technological solutions and more accessible design for all arise as possible solutions to eliminate these obstructions to the tourism industry. Accessible design and the provision of information improve accessibility and usability for PwD, but they also make tourism more approachable for a broader range of the population (Pühretmair, 2004). If the right information is displayed to tourists, correct planning is possible, helping eliminate all other obstacles, including the provision of solutions for physical ac-

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cessibility. Information is undoubtedly the pillar of accessible tourism, and with the rise of the fourth industrial revolution, connectivity is becoming a significant topic.

About Industry 4.0 Increasing digitalization is changing the everyday lives of people, markets, business models and value chains. Nowadays, companies face various challenges like new corporate strategy, new requirements for the training of personnel, new demands for cybersecurity and radical changes in business processes across all company levels, justifying solutions based on new information and communications technologies (ICT). In fact, emergent ICTs provide the opportunity for companies to build new customer relationships, to invent new business models, and to increase their competitiveness (Parida, Sjödin, & Reim, 2019). The phenomenon of Industry 4.0, first mentioned in 2011 in Germany (Bundesministerium für Wirtschaft und Energie, 2016) launched the fourth industrial revolution, which is based on a set of concepts enhanced by new technologies. McKinsey, an American management consulting company, studied Industry 4.0 as digitalization of the manufacturing sector which originates from four disruptive technology groups: i) connectivity, data, and computing power; ii) analytics and intelligence; iii) humanmachine interaction; and iv) digital-to-physical conversion. Most of the literature review studies made on this topic rely on the definition provided by Bundesministerium für Wirtschaft und Energie (2016), that define Industry 4.0 as a “new level of organizational control over the entire value chain of the life cycle of products, and it is generated towards increasingly individualized customer requirements”. The foundation of the fourth industrial revolution is the availability of all relevant real-time information (Crandall, 2018), which was made possible by focusing on the networking of equipment and processes in the industry through the use of ICT to form an intelligent value chain (Schlüter, Hetterscheid, & Henke, 2017). Industry 4.0, alongside with digitalization, capitalized on the developments in several technologies. These new technological trends became known as technological drivers, which is a current issue in the literature. Several authors have identified and studied different technological advances that promoted the appearance of Industry 4.0 (Jasperneite, 2013; Kovar et al., 2016; Rüßmann et al., 2015; Saturno, Pertel, Deschamps, De, & Loures, 2018). Cyber-physical systems (CPS), the Internet of things (IoT), big data, cloud technologies, 3D printing, radio frequency identification (RFID), cognitive computing, mobile technologies, machine to machine technologies, virtual reality, and augmented reality represent some of these solutions. These technological drivers demonstrate that the competitiveness and innovation needed for Industry 4.0 depend on mastering a wide range of ICT skills and competencies (Sedlar et al., 2018). However, the fourth revolution is still in progress, so it is possible that these new technological solutions will be improved and will innovate even further. These technologies have mainly been studied in the production area. However, their utility can be expanded to services industries, like the case of tourism, helping in the transition of tourism into a new digital age. Table 1 offers a brief description of some Industry 4.0 technological drivers, and some authors that studied them. These technological drivers demonstrate the different areas changing with the fourth industrial revolution. It is almost possible to claim that technological evolution gave rise to this new fourth industrial revolution, and the technological drivers prove it. The key technology drivers enable the transformation of processes along the entire value chain, from the development phase up to the use and maintenance phase. Different types of ICT can contribute to Industry 4.0, but some of them have 1447

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a crucial role for developing products and services, conducting research and promoting innovation activities (Sedlar et al., 2018), making them essential in this new digital era. Industry 4.0 is changing the landscape of industries with a particular impact on the relations between companies and clients and the way they interact (Schwab, 2016: 21). With technologies dominating markets, clients will become more and more demanding. Services will need to offer quick and customized solutions to clients, which can be obtained with the integration of Industry 4.0 foundations. Adapting services to customer demand and the utilization of new digital technologies is crucial to remain competitive in rapidly changing environments. The application of industry 4.0 principles can promote the digitalization of services, which in turn could provide a source of competitive advantage in the near future. One of the service industries that is quickly changing thanks to new ICTs is tourism. The benefits that the fourth industrial revolution brings to tourism are great and can even revolutionize the sector. In recent years, due to the development of ICTs, tourism has become smarter. These technologies helped improve tourism in many areas. Therefore, it is imperative to understand how the different technological drivers contributed to improving tourism. Of course, with the new industrial revolution ongoing, new technologies have stepped up to develop tourism even further, with particular implications on making tourism more accessible. Table 1. Industry 4.0 technological drivers Technological Driver

Author(s)

Description

Cyber-physical-Systems (CPS)

Wolter et al., (2015)

CPS are intelligent systems with integrated physical and computational capabilities, that can interact with humans and are responsible for the interconnectivity of devices, machines and moving objects.

Internet of Things (IoT)

Tobergte and Curtis, (2013) Atzori, Iera, and Morabito, (2010)

IoT refers to the networking of objects with the Internet, providing a worldwide network of interconnected objects, based on standard communication protocols.

Big Data (analysis tools)

Wolter et al., (2015) Schwab, (2016: 135)

Innovative analysis tools (text analytics, machine learning, predictive analytics, and statistics) are tools that have the capacity to distinguish data that should become information from data that has no relevance.

Cloud Technologies

Wolter et al., (2015) IBM, (2015)

These technologies can be perceived as online data centers, where information can be stored and made available for many users.

3D Printing (additive manufacturing processes)

Klein, Avery, Adams, Pollard, and Simske, (2014)

3D printing is a methodology using three-dimensional data sets for producing new physical objects. The replication is made layer by layer, allowing existing objects to be replicated in a three-dimensional manner.

RFID (Radio Frequency Identification)

Pala and Inanç, (2007) Smyth and Crabtree, (2012)

Radio frequency identification is a wireless technology that lets computers or other devices read/identify electronic tags from a given distance, using an object (generally referred to as an RFID tag) for identification and tracking with radio waves.

Cognitive Computing

Esser et al., (2011)

This incorporates digital systems that learn at scale, reason with purpose and interact with humans, in order to achieve big data treatment.

Mobile Technologies

Jazdi, (2014)

Electronic equipment such as mobile phones or small computers that can be used in different places, and the technology connected with them, such as mobile apps.

Virtual Reality (VR) and Augmented Reality (AR)

Nunes, Pereira, and Alves, (2017) Barnes, (2017)

VR can be defined as a computer-generated simulation of a three-dimensional image or environment that can be interacted with using special electronic equipment. AR is a technology that produces a computer-generated image on a user’s view of the real world, thus providing an augmented view.

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THE ROLE OF THE FOURTH INDUSTRIAL REVOLUTION IN TOURISM Tourism 4.0 In recent times, different types of technologies have become a powerful ally to tourism. Developments became available in ICTs which were responsible for the origins of smart tourism (Gretzel, Sigala, Xiang, & Koo, 2015) and which helped tourism advance to new platforms, reaching more clients and present more quality services. With the fourth industrial revolution, new technologies are taking a step forward to develop tourism even further. The impact of the industrial revolution on the travel industry gave rise to a more advanced paradigm in tourism, named Tourism 4.0. The concept of Tourism 4.0 first appeared as the largest government-sponsored research project in Slovenian tourism. This research was possible due to the establishment of a partnership between the Arctur company and Slovenia’s government. Arctur is a commercial supplier of computing services, which provides innovative and user-friendly digital solutions to businesses, public institutions, and research institutions. Artur’s R&D department together with the Slovenian government sought a way to potentialize innovation in the tourism sector. The result of the research was the creation of the Tourism 4.0 consortium. According to the Tourism 4.0 consortium, Tourism 4.0 is defined as “a trend of big data processing, collected from a vast number of travelers, to create personalized traveling experience, based on a variety of modern high-tech computer technologies” (Arctur & Tourism 4.0 Consortium, 2018). Tourism 4.0 follows the slogan: “Green, active, healthy” (Peceny, Urbančič, Mokorel, Kuralt, & Ilijaš, 2019), which demonstrates that the concept encompasses many future trends. According to the World Economic Forum (World Economic Forum & Accenture, 2017), four subjects are crucial for digital tourism transformation: • • •

•

Living Travel Experience: According to tourists’ habits - optimize and personalize the customer experience by collecting and exchanging data and continuously generating comprehension. Enabling the Travel Ecosystem: By evolving different stakeholders. Digital Technologies: That promoted Industry 4.0 - optimizing the real-time use of resources and transforming operations, through innovations such as IoT, virtual reality, and digital platforms will enable flexible working and changes to basic operational processes and will optimize the real-time use of resources. Safety and Security: Using digital technologies to produce secure environments and enhance the global travel security system (Moavenzadeh & Maar, 2018).

Tourism 4.0 aims to integrate these four topics into the diverse industries connected with tourism. In fact, by applying new technologies in tourism, it is possible to promote personalized solutions to every tourist, while assuring that they are traveling with safely and securely. Tourism 4.0 also promotes sustainable tourism, encouraging a positive environmental, social, and economic impact and collaboration between all stakeholders. The success of Tourism 4.0 depends greatly on the different collaborations in the travel ecosystem. With the objective of achieving sustainable tourism, Tourism 4.0 sets up a primary goal of making tourism “accessible to everyone at any time,” making it tremendously crucial for the development of accessible tourism. Technologies have a crucial role in helping the development of more accessible tour-

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ism. The main problem preventing people with different types of disabilities from traveling is mainly related to the shortage of information (Stumbo & Pegg, 2005). There are several uses of technologies that can help diverse tourism objects display greater accessibility features. With the new industrial revolution, technology is exceeding all expectations, making society evolve in many sectors and providing better life-conditions, especially for those with some type of impairment. Ensuring that tourism is accessible is very important from an economic point of view, due to the potential of the accessible tourism market. Furthermore, Tourism 4.0 has all the tools for achieving more inclusive travel ecosystems and boosting inclusiveness of PwD. Industry 4.0 was responsible for launching the fourth industrial revolution, and now this revolution is impacting other sectors, like tourism. Industry 4.0 technological drivers are the tools responsible for making travel experiences more accessible, efficient, and affordable for travelers. Innovative information tourism platforms that offer innovative products and services will play a crucial part in the current and future digitalization of the travel industry. However, the question remains: how can the technological impacts of the fourth industrial revolution contribute to a more accessible tourism?

ACCESSIBLE@TOURISM 4.0: HOW THE FOURTH INDUSTRIAL REVOLUTION IS REVOLUTIONIZING ACCESSIBLE TOURISM The different innovative technologies promoting the fourth industrial revolution became known as technological drivers. As the relationship between new technologies and tourism developed to a very close one, the use of Industry 4.0 technological drivers in touristic activities was mainly responsible for the urge of Tourism 4.0. In smart tourism, technology was already responsible for improving tourism services and products. However, the technologies that Tourism 4.0 presents are one-step forward to the future of tourism. Industry 4.0 technological drivers and other technological innovations have opened the doors to the digitalization of the travel industry. IoT, virtual and augmented reality, big data, RFID, and even 3D printing are technological drivers that were born in the manufacturing world but have the capacity to transform the tourism industry. Other innovative technologies like robotics and virtual assistants are also drivers of the fourth industrial revolution (Schwab, 2016: 13,19) and can have a big impact on many sectors. Tourism 4.0 is a concept that adapts all these technologies to tourism, transporting tourism to a new digital era, stimulating the inclusion of disabled travelers. Accessible@Tourism 4.0 is an overview of different technological innovations developing accessibility conditions in tourism. This overview will be obtained by illustrating examples of technological innovations that are promoting the fourth industrial revolution and transforming the world of tourism, making it more accessible. The impact of Industry 4.0 technological drivers and other technological innovations in accessible tourism allows the literature gap to be addressed by showing how Tourism 4.0 can help in the development of accessibility conditions for PwD.

A. Mobile Technology Mobile technologies, especially mobile apps, have become significant players for smart tourism and crucial tools for assuring accessible tourism. Similar to their role in smart tourism, Tourism 4.0 also relies much on mobile apps to take tourism to a new level. The evolution that this type of technology has undergone in recent years made this device turn into a tour guide, a travel agency, a restaurant lo1450

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cator, and a map. Mobile apps and cell phones are side by side with tourists during the entire tourism experience. Services for passengers aligned with the use of social media have made mobile technology indispensable for all phases during a trip: planning (online bookings and places to visit), while traveling (cell phone as the best traveling friend), and post-trip (sharing experiences in social media) (Belén, 2018). Another example is related to apps that allow sharing and matchmaking. Airbnb and Uber became disruptive innovations because technology allows peer-to-peer sharing and matchmaking. (World Travel & Tourism Council, 2015).

B. Virtual Assistants A virtual assistant is “a computer program or device that is connected to the internet and can understand spoken questions and instructions” (Virtual Assistant, 2019). Voice recognition technology and voice assistant software assistants have experienced growth in recent years. Voice assistant products from Amazon, Apple, and Google enable users to ask questions and issue commands in natural language. Siri and Alexa are examples of voice assistants that can help people in various ways. Some functionalities include: sending and reading text messages, making phone calls, answering basic informational, setting timers, alarms, and calendar entries; controlling media playback and even controlling IoT enabled devices (thermostats, lights, alarms) (Hoy, 2018). These services on offer can help accessible tourism by making significant contributions to the daily activities of disabled tourists. By helping tourists with these activities, they make traveling more personalized and less troublesome. The main contribution of this type of technology is reducing the information barriers, which disabled tourists’ experience, and offering more customized services. The tourism sector is now experimenting with the arrival of virtual assistants specially designed for their atmosphere. IBM launched Watson Assistant, an AI-powered virtual assistant that creates interactive and personalized solutions for tourists. Watson Assistant creates customized experiences, taking into consideration factors like age, gender, and travel interests, forming personalized activity agendas that users can modify (IBM, 2017). The fact that this virtual assistant adapts to users can be fundamental to accessible tourism, due to the particular necessities of disabled tourists.

C. Virtual Reality and Augmented Reality Virtual reality and augmented reality offer a new set of experiences for tourism. Simulating digital environments can have a significant impact on traveling, leading to big changes in touristic experiences. Virtual reality can help in removing many accessible tourism barriers. It can deliver 3D and immersive experiences to disabled tourists, providing touristic guide services in sign language and even remove physical barriers in museums. This technology can be even used to access sites that are usually closed or inaccessible (Jung, tom Dieck, Lee, & Chung, 2016). VR can be a platform to capitalize on digital tourism in cultural attractions like museums and archaeological sites. AR makes use of the traveler’s real-time environment, adding it to a 3D environment, and recreates digital tourism experiences (Kounavis, Kasimati, & Zamani, 2012). Augmented reality is also strongly connected to travel mobile apps, which have been revolutionizing tourism. A practical example of the use of these technological drivers is the Geevor Tin Mine Museum (Jancenelle, Vivien, Storrud-Barnes, Susan, Javalgi, 2017). This museum is a UNESCO world heritage 1451

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site and uses VR and AR to produce engaging experiences for its main target markets (school groups, families, and elderly visitors). Some parts of this mining museum are difficult to access (underground mines). A VR/AR application is used for pre-visit stage and on-site underground mining experience.

D. The Internet of Things A network of smart devices enhances businesses by connecting clients and service providers, thus generating a large amount of data that can be stored and computed on available cloud services (Kaur & Kaur, 2016). Tourists trends and patterns can be easily stored. This connection can be useful to offer more personalized tourism offers. The more information known about guests, the better services can be offered, and satisfaction increases. A practical example is IoT data identifying that a customer has booked a room every year, so it sends a message asking the guest if they would like to make another booking this year (Newman, 2018). IoT is quickly becoming a significant technology for tourism. Some services that IoT has the potential to maximize are: helping to select destinations and searching for suitable travel arrangements; helping tourists during preparation for a trip; providing real-time support to the tourist during the trip, helping managing memories after the trip, for highly personalized onsite support of frequent travelers and personalized services for tourist groups (Balandina, Balandin, Koucheryavy, & Mouromtsev, 2015). Significant gains that shared data brings are, for example, personalized solutions, as they have a particular influence on accessible tourism. The sharing of information that IoT allows can put an end to the problems of a lack of information. Having all the information available is especially important for disabled people when planning a trip. In addition, having personalized services is crucial for disabled tourists. This particular group has different requirements according to the type of disability, so the offer of rapid and customized solutions is boosting disabled tourism. Examples go from reserving a room with the right requirements to the use of smart devices to enter and explore museums and monuments. IoT can also bring significant improvements to accessible tourism by offering real-time support. Assistance and support for disabled tourists in real time may just unlock all the potential of accessibility tourism markets. It is noteworthy that IoT promotes the quick and efficient flow of information. A better-connected world where information is a crucial aspect is vital for the development of accessible tourism.

E. 3D Printing 3D printing may not be a widespread technology associated with tourism. However, the potential to print perfect replicas can have a significant influence on improving touristic experiences in museums, art galleries, and archaeological sites. 3D printing has particular significance in the context of museums due to the low costs of replicating objects (Allard, Sitchon, Sawatzky, & Hoppa, 2005; Short, 2016). Another use in museums can be in creating and producing customized 3D souvenirs (Jancenelle, Vivien, Storrud-Barnes, Susan, Javalgi, 2017). In the case of art and design, creating exhibits in art galleries using 3D printing also proves the potential of this technology for tourism (Walters & Davies, 2010). 3D printing has particular importance for accessible tourism because it allows a more immersive experience. This technology helps to make artifacts more tangible and accessible to the public, improving the experience of visiting an art gallery or a museum. 3D printing objects also helps in eliminating barriers for people with cognitive disabilities who may experience learning difficulties during touristic trips to museums or archaeological sites. For this group 1452

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of disabled tourists, a 3D replica may just be the answer to their particular requirements. A practical example of the application of 3D printing to tourism is the Underwater Maya Project (Mckillop, 2013). The project uses 3D printing to build replicas of ancient Maya wooden buildings. This makes archaeological sites more accessible since exhibiting 3D replicas does not require loans of actual artifacts or extended security measures. The project already led to the opening of exhibits in some remote places, which is also a contribution to accessibility. The main objective is contributing to sustainable archaeological tourism while promoting access to the Internet for “digital museums” and engaging people with archaeological sites.

F. Big Data (Analyze Tools) With the internet becoming a significant player in tourism, tourists rely on various web-based platforms to plan trips. The electronic trace left by tourists, also known as digital footprint (Girardin, Blat, Calabrese, Dal Fiore, & Ratti, 2008), generates a lot of data. The big data problem is seen once again in Tourism 4.0. Moreover, analysis tools have a critical role in generating the right information and doing this can become a source of competitive advantage. Data can have different sources. From booking websites to searching for the best places to visit and sharing on social media, the digital footprint can help develop customized solutions in tourism. Li, Xu, Tang, Wang, and Li, (2018) identified three sources of big data generation: user-generated content data (UGC), including online textual data and online photo data; device data (devices), including GPS data, mobile roaming data, Bluetooth data; and transaction data (operations), including web search data, webpage visiting data, and online booking analytics for tourism. Some practical examples of the use of UGC data come from Meliá hotels and Airbnb. The Meliá hotel chain uses information about its guests to define marketing campaigns. By analyzing their database (amount spent, the reason for the trip, the country of origin), they are able to cross-check information to develop an appropriate customer profile and achieve a higher success rate. The objective is to obtain better market segmentation and optimize their promotional campaigns (Belén, 2018). Airbnb is able to use customer data to identify guests who chose not to book because they were displeased when hosts failed to respond to their expectations (Newman, 2018). Another example is the Destination Management Information System, Åre, presented by Fuchs, Höpken, and Lexhagen (2014). This information system for the Swedish mountain tourism destination Åre, measures destination performance and customer behaviors by analyzing data from customer-based knowledge sources, like web searches, booking, and feedback. Big data analysis is a vital element of Tourism 4.0, and it is becoming a significant success factor for accessible tourism. Similar to IoT, the main advantage of the analysis of big data is in allowing personalized tourist solutions to be offered. By studying different data sources, it is possible to identify tourists’ behaviors and establish patterns. Through the analysis of data derived from disabled travelers, one can understand the main difficulties and address them in order to optimize touristic experiences. Since disabled tourists needs differ according to the type of disability, much data is generated, making analysis tools indispensable. The data obtained can become valuable knowledge, and a simple understanding of disabled tourist behaviors is essential to promote accessible tourism. Analysis tools should be able to collect and analyze data from accessible tourism knowledge sources in order to deliver a personalized and enlightened service.

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G. RFID Radio frequency identification is an automatic identification method that retrieves and stores data using RFID tags. This technology is being popularized by Industry 4.0, as it is mainly utilized in retail and supply chain management. Nonetheless, while its main application is in manufacturing, RFID can also help enhance services by improving quality, speed, and customer satisfaction. In the case of tourism, Öztayşi, Baysan, and Akpinar (2009), described RFID applications in four major RFID areas: • • • •

Human tracking and control systems (E-passports, customer loyalty management, tracking children or people with special needs, airport security). Assets and valuables tracking systems (luggage tracking and food and beverage management). Contactless payment systems (toll collection system, RFID-tagged public transport cards, payments in a hotel, keyless room entry). RFID-based information (museums, archaeological sites, art galleries, monuments).

All of the mentioned examples are essential in assuring more accessible tourism. Tracking systems can help disabled tourists with navigation and wayfinding and prevent the loss of specialized equipment like wheelchairs while traveling (Konkel, Leung, Ullmer, & Hu, 2004). Contactless payment systems and digitally activated transport and hotel cards also improve accessibility and inclusiveness by making processes easier for disabled tourists. RFID applications have been studied in maritime cruises (Dias et al., 2016) and even in the organization of cultural events in a tourist destination (Zeni, Kiyavitskaya, Barbera, Oztaysi, & Mich, 2009). In literature, it is possible to identify some practical examples of RFID use in tourism, which can also contribute to the inclusiveness of disabled tourists. When visiting a capital city, mobility and visually impaired people often find it difficult to cross-busy streets. RFID can allow communication between tourists with disabilities and the crossing equipment to automatically activate a location message and input a request for the green light (Smyth & Crabtree, 2012). Another practical example is related to navigation at attractions. Disabled tourists can experience difficulties when moving around an attraction, so RFID can propose a route recommendation system that informs tourists which facilities they should visit and in what order. A proposal for this application comes from Tsai and Chung (2012), in the context of theme parks. At the entrance, tourists are provided a wristband embedded with an RFID tag. RFID readers are installed on the entrances and exits of each facility. Whenever a tourist enters the area of an RFID reader, the system records the entrance and time and transfers that information to a route database.

H. Robotics Automation in tourism attracted the use of robots. Robots are capable of performing automated tasks and delivering value to clients. Their utilization in tourism and hospitality is already a reality. Ivanov, Webster, and Berezina (2017) identified some current uses of robots across the tourism sector. Their findings highlighted six areas where robots are being used to improve services. Hotels have been evolving for adopting the use of robots in assisting people in their rooms (delivery and cleaning robots) or even working as staff (robot receptionist and porter). One practical example is 1454

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Henn Na Hotel in Japan, which uses technologies in order to maximize excitement, efficiency, and comfort for guests. Amongst other innovations (voice- and face-recognition), the hotel staff consists mainly of robots. The reception is operated by three multi-lingual robots responsible for greeting, checking-in and assisting guests, there is a robotic arm for storing luggage in the cloakroom, and there are porter robots to carry bags to the rooms (Alexis, 2017). Robots also have been used in restaurants (robot servers and even chefs), amusement parks (concierge robots), airports (robot-guide and bag drop robots), at events (delivery and entertainment robots) and galleries and museums (robot guides). The use of robots in tourism already led to the creation of the concept of R-tourism (robot tourism), proposed by Alexis (2017). R-tourism is deeply connected to Tourism 4.0 since some of the robotic impacts in tourism are already visible. Also, the relation with accessible tourism is quite close since the automation of some services can captivate the interest of disabled tourists. Robot guides represent a significant breakout for tourists to experience museums and art galleries, providing, for example, help to visit inaccessible exhibits. Also, the aid provided in a hotel with cleaning and assistive robots can fulfill some challenging accessibility requirements. In fact, one may recognize that a primary driver of R-tourism is accessibility. Another example of robotics improving accessible tourism is the proposal that a robot could take pictures and records videos from a faraway place and send them to the user in real time. This proposal comes from Cheung, Tsang, and Wong (2017), and aims to build a robot capable of helping disabled people to perform virtual tours of places which they cannot reach because of their mobility impairments. The following table (Table 2) presents a synopsis of the study conducted. The leading technologies promoting the fourth industrial revolution and impacting accessible tourism were identified. Various projects in the area of these technologies were explored with the help of some authors’ work in respective innovative areas. After analyzing the fundamental discoveries, the impact of each technology in accessible tourism was discussed. The table sums up the purpose of Accessible@Tourism 4.0 in describing how Tourism 4.0 has the power to promote more accessible tourism.

FUTURE RESEARCH DIRECTIONS Although there is a large number of studies in the field of smart tourism, there are not many specially dedicated to accessible tourism. There is an evident lack of research specifying the application of technologies to enhance accessible tourism. The same happens with Industry 4.0 technological drivers in tourism. Many authors describe the potential these technologies can have but never apply it to the case of accessible tourism. The concept of Accessible@Tourism 4.0 was mainly explored by using examples of the application of Industry 4.0 technological drivers in tourism and explaining how they could benefit and potentialize accessible tourism. Therefore, it is of utmost relevance carry out studies regarding the use of this kind of new technology in the accessible tourism. Future researches could study the impact of this new revolution in other areas and compare it with the impacts on tourism. Also, studies could be conducted focusing on applying Tourism 4.0 in other types of tourism (educational, religious, medical) and comparing the results. Research in these scopes aiming at clarifying potential opportunities in these particular areas is suggested. Tourism 4.0 is still a topic that lacks significant literature. The approach to this topic has the goal of stimulating further research and investigation, especially for the importance of enhancing accessible tourism. 1455

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Table 2. Technological Drivers Promoting Accessible Tourism Technological Driver

Author(s)

Impact in Accessible Tourism

Emrouzeh, Dewar, Fleet, and Bourgeois, (2017)

• Navigation (wayfinding). • Planning (trips, what to visit, accessible conditions) • Social (sharing, collaborating, and communicating). • Security and emergencies (health monitoring, weather alerts, real-time assistance). • Transactional (buying tickets, making reservations, and shopping). • Entertainment (tour guides, e-readers, etc.). • Information access.

Virtual Assistants

Hoy, (2018)

• Simplifying daily activities of disabled tourists. (setting timers, alarms, and calendar entries; setting reminders, making lists, and doing basic math calculations; controlling media playback; controlling IoT-enabled devices). • Making traveling more personalized and less complicated with fewer information barriers.

Virtual Reality and Augmented Reality

Jung, tom Dieck, Lee, and Chung, (2016) Kounavis, Kasimati, and Zamani, (2012) Jancenelle, Vivien, Storrud-Barnes, Susan, Javalgi, (2017)

• Recreating several points of interest in 3D, like monuments, museums, buildings, and other touristic attractions. • Creating realistic 3D environments for touristic activities and allowing immersive experiences for disabled tourists. • Potentializing better holiday planning functionalities. • Providing touristic services in sign language. • Improving access sites that are generally closed or inaccessible. • Preview of disabled tourism offers. • Helping in travel decisions by providing relevant information about accessibility conditions.

Internet of Things (IoT)

Balandina, Balandin, Koucheryavy, and Mouromtsev, (2015) Kaur and Kaur, (2016)

• Tourists trends and patterns can be easily stored. • More personalized tourism offers (rapid and customized solutions). • The sharing of information can put an end to the problems of lack of information. • Assistant and support to disabled tourists in real-time. • Automatically reserving rooms with the right accessibility requirements. • Allowing the use of smart devices to enter and explore museums and monuments.

3D Printing

Jancenelle, Vivien, Storrud-Barnes, Susan, Javalgi, (2017) Mckillop, (2013)

• Replicating objects offering more immersive touristic experiences. • Artifacts more tangible and accessible to the public. • Eliminating barriers for people with cognitive disabilities who may experience learning difficulties while visiting museums or archaeological sites.

Analysis Tools (Big Data)

Li, Xu, Tang, Wang, and Li, (2018) Fuchs, Höpken, and Lexhagen, (2014)

• Identifying tourists’ behaviors and establishing patterns. • Identifying the main difficulties and addressing them in order to optimize touristic experiences. • Collecting and analyzing essential data from different accessible tourism systems can promote interconnection between several digital databases. • Providing more personalized tourism offers.

Radio Frequency Identification (RFID)

Öztayşi, Baysan, and Akpinar, (2009) Tsai and Chung, (2012) Dias et al., (2016)

• Tracking systems help in navigation/wayfinding and prevent the loss of special equipment. • Contactless payment systems. • Digital activated transport. • Autonomous processes in hotels. • RFID in attraction sites can guarantee better access conditions. • Providing digital solutions in tickets. • Informing users by giving information about nearby objects.

Robotics

Cheung, Tsang, and Wong, (2017) Ivanov, Webster, and Berezina, (2017) Alexis (2017)

• Helping disabled people to perform virtual tours of places, which they cannot reach because of their mobility impairments. • Robot guides represent a significant breakout for tourists to enjoy museums and art galleries, providing, for example, help to visit inaccessible exhibits. • Cleaning and assistive robots can help PwD in hotels, restaurants, touristic events, and amusement parks.

Mobile Technology

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CONCLUSION Tourism 4.0 reflects the integration of technological tools into tourism. This integration has the potential to innovate tourism and make it accessible for everyone, offering disabled tourists the possibility of experiencing the wonders of traveling. Accessible@Tourism 4.0 can be considered the answer to the research gap on the role of Industry 4.0 and new technologies in tourism and their importance in developing more accessible tourism. Accessible@Tourism 4.0 shows the effect of Industry 4.0 and technologies in the accessible tourism sector, sending shockwaves across tourism and revolutionizing the way of traveling. Tourism 4.0 clearly shows that “Integration of technology makes life easier, especially for disabled people” (Altinay et al., 2016). Technological advantages are finally allowing many disabled tourists to fulfill their dreams of exploring the world. The adoption of fourth industrial revolution components in accessible tourism (Accessible@Tourism 4.0) enables the development of a new technological solution that can facilitate access to tourism products by disabled people, contributing to the development of accessible tourism. Many of the technologies analyzed demonstrate the significance of connectivity, one of the pillars of the fourth industrial revolution, in the promotion of accessible tourism.

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ADDITIONAL READING Avis, A. H., Card, J. A., & Cole, S. T. (2005). Accessibility and Attitudinal Barriers Encountered By Travelers With Physical Disabilities. Tourism Review International, 8(3), 239–248. doi:10.3727/154427205774791591 Chareyron, G., Da-Rugna, J., & Raimbault, T. (2014). Big data: A new challenge for tourism,” 2014 IEEE International Conference on Big Data (Big Data), Washington, DC, 2014, pp. 5-7. 10.1109/BigData.2014.7004475 Han, D. I., Jung, T., & Gibson, A. (2014). Implementing Augmented Reality in Tourism. In Information and Communication Technologies in Tourism. Academic Press. doi:10.1007/978-3-319-03973-2_37 Jaremen, D. (2016). Advantages from ICTS usage in hotel industry. Czech Journal of Social Sciences. Business Economics (Cleveland, Ohio), 5(3), 6–17. doi:10.24984/cjssbe.2016.5.3.1 Koo, C., Gretzel, U., Hunter, W. C., & Chung, N. (2015). The Role of IT in Tourism. Asia Pacific Journal of Information System, 25(1), 99–104. Li, Y., Hu, C., Huang, C., & Duan, L. (2017). The concept of smart tourism in the context of tourism information services. Tourism Management, 58, 293–300. doi:10.1016/j.tourman.2016.03.014

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Michopoulou, E., & Buhalis, D. (2013). Information provision for challenging markets: The case of the accessibility requiring market in the context of tourism. Information & Management, 50(5), 229–239. doi:10.1016/j.im.2013.04.001 Xiang, Z., Magnini, V. P., & Fesenmaier, D. R. (2015). Information technology and consumer behavior in travel and tourism: Insights from travel planning using the internet. Journal of Retailing and Consumer Services, 22, 244–249. doi:10.1016/j.jretconser.2014.08.005

KEY TERMS AND DEFINITIONS Accessible Tourism: A form of tourism that that enables people with various access requirements, including issues of mobility, vision, hearing and cognitive dimensions to have access to tourism products, services, and environments, without constraints. Accessible@Tourism 4.0: The contribution of the fourth industrial revolution for improving accessible tourism, resulting in an overview of different technological innovations developing accessibility conditions in tourism. Industry 4.0: A new level of organizational control over the entire value chain of the life cycle of products, and it is generated towards increasingly individualized customer requirements. Technological Drivers: Technological trends that capitalize on the fourth industrial revolution and the digitalization era. Tourism 4.0: The impact of the fourth industrial revolution in tourism, resulting in the creation of more personalized traveling experience, based on a variety of modern high-tech computer technologies. People with Disabilities: People suffering from any restriction or lack of ability (resulting from an impairment) to perform an activity in the manner or within the range considered normal for a human being.

This research was previously published in the Handbook of Research on Social Media Applications for the Tourism and Hospitality Sector; pages 192-211, copyright year 2020 by Business Science Reference (an imprint of IGI Global).

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Chapter 73

Technological Developments: Industry 4.0 and Its Effect on the Tourism Sector Hande Mutlu Ozturk https://orcid.org/0000-0002-4404-0106 Pamukkale University, Turkey

ABSTRACT Technological developments in recent years have been affecting the lives of people and societies more rapidly than in the past. Developments in the field of communication, robotics, transportation, etc. are called the 4th Industrial Revolution or Industry 4.0 in the industrial sector. Technological developments have created great changes in the services and industrial sectors. Industry 4.0 has also led to changes in the transformation of the tourism sector and is likely to occur in future processes. This chapter examines the impact of Industry 4.0 on the tourism sector.

INTRODUCTION Technological developments have been increasing in recent years. At the end of the 18th century, the advancements in technology have been advancing rapidly since the 1st Industrial Revolution, which started with the use of steam engines. Developments in the field of communication and progress in informatics have affected the change in all fields. The first three of the previous industrial revolutions are called mechanization, use of high electrical energy and automation and electronics, respectively (Lasi et al., 2014). Today, economies are turning to the fourth industrial revolution, defined by the use of cyber systems, smart factories and innovations in the service sector (Shamim et al., 2016, Lee et al., 2014). Industry 4.0 can also be defined as a subcomponent of digital transformation in existing businesses and processes (Porter and Heppelmann, 2014). Depending on the changes in technology and digitalization; both the product and the production methods vary. This change is called industry 4.0. The 4th industrial revolution, called as Industry 4.0,

DOI: 10.4018/978-1-7998-8548-1.ch073

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 Technological Developments

is the process in which the use of computers and automation in the industry. With Industry 4.0, use of automatic machine also called robots has been at the industry. Peceny et al. (2019) discussed Tourism 4.0, a new tourism paradigm that emerged to reveal the potential for innovation in the entire tourism sector. In the study, it is assumed that technologies that provide important opportunities from Industry 4.0 such as Internet of Things, Big Data, Chained Disabilities, Artificial Intelligence, Virtual Reality and Augmented Reality can be applied to tourism sector. It is believed that by creating a common ecosystem involving local residents, local authority, tourists, service providers and government, an enriched tourism experience can be created in both the physical and digital worlds. In their study, Shamim et al (2017) examined Industry 4.0 issues in the service sector and the situations in the hospitality industry. The challenges of Industry 4.0 require continuous innovation and learning, depending on people and the capabilities of the business. Appropriate management approaches play a vital role in the development of dynamic capabilities and in an effective learning and innovation environment. In the study, proposes a framework of management practices that can support innovation and the learning environment in an organization was investigated.

BACKGROUND Industry 4.0 Depending on the technological developments, the current approaches in the tourism sector needs to change. In this issue, definitions such as Industry 4.0 or Tourism 4.0 have been made. The aim of Industry 4.0 or Tourism 4.0 in tourism is to reduce the negative effects of tourism, to see the effects of the use of technology in the tourism sector and to develop cooperation models in partners. Some researchers also define Tourism 4.0 or Industry 4.0 in the tourism industry as smart or intelligent tourism, and this is being discussed by many researchers. (Buhalis & Amaranggana, 2013, Gretzel et al., 2015a, Hunter et al., 2015, Gretzel et al., 2015b, Geissbauer et al., 2014, Schwab, 2016, Verevka, 2018, Goncharova and Bezdenezhnykh, 2018, Lebedev et al., 2018). Concepts and tools provided by intelligent tourism is a phenomenon that defines, extends and integrates Information and Communication Technology (ICT) into the tourism experience. Factors that led to the fourth industrial revolution called Industry 4.0; the spread of robots, the Internet of Things, artificial intelligence, sensors, cognitive technologies, nanotechnology, services of the Internet, quantum informatics, wearable technologies, augmented reality, intelligent signalling, intelligent robots, big data, new generation technologies such as 3D and smart networks. Industry 4.0 technologies have started to change business environments and lifestyles by rapidly using them in business life, communication and education. Since the tourism industry is a dynamic industry that is rapidly adapting to innovations and technologies, Industry 4.0 technologies have quickly found application in the tourism industry. With the use of Industry 4.0 technologies for tourism purposes, ‘smart tourism’ concepts emerged. Topsakal et al. (2018) discussed why some people, and therefore tourists, have previously used industrial revolutions than other people or tourists, and what the causes are for it. Mil and Dirican (2018) have focused on the effects of technological developments on the tourism sector and presented a large literature review on the subject. Icten and Bal (2017) examined the virtual and augmented reality and their application examples in fields such as education, art, traffic, engineer1465

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ing and tourism. Ilhan and Celtek (2016) carried out a detailed study on the effects of augmented reality on mobile marketing and tourism. In this study, details of how technological developments can be used in finding directions, examining two or three dimensional product images with mobile devices, getting information about travel, and visual trips in museums are presented. Artificial intelligence technology reduces errors in applications and processes and makes hotel managers to make more accurate decisions about tourist demand and supply. Thus, a better marketing strategy can be implemented and financial management and human resources planning can be performed (Claveria et al., 2015). With the Internet of Things and sensors installed in / around the hotel even in the cities, make it is easier to access data on the availability of facilities, tourist location, weather, road conditions, traffic situation and airport traffic. While this information does not directly affect the experience of tourists, it affects the overall impression and satisfaction of tourists (Jin et al., 2014). Ay (2009), in his study, discussed the reflections of the developments in information technologies and internet on travel agencies and online travel agency. In this study, the literature is searched on the subject and the subject is examined by making use of secondary data and sectoral applications. Ay (2009) indicated that the rapid developments in the field of information and communication technologies in recent years increase the tendencies towards distribution systems, electronic data transfer and globalization of communication systems. In the tourism industry, whoever adapted to the technological developments grow in a short time. Data banks, smart cards, automation and communication networks in production increase economies and competitiveness. In the tourism industry, internet applications and their relations with distribution systems have recently come to the forefront. Other technological developments (voice recognition systems, data mining, artificial intelligence applications, virtual reality or geographic information systems, etc.) are expected to be much more effective in the exchange of tourist products and distribution channels. With the widespread use of computers and the internet, travel agencies have started to provide their services via web pages. The person who lives in any part of the world can access the web page of the travel agency in any tourist area of the world via internet and make his / her holiday or reservations. All these developments have brought to the fore the issue of ensuring shopping and data security. Kıroğlu (2012) examined the concept of internet use and electronic tourism in her study. In this study, possible developments of e-tourism have been discussed by taking into consideration current applications and future expectations. In the research, the process of people’s orientation towards e-commerce is examined; The changes in the volume of electronic commerce over time and the structure of the user profile are examined. E-commerce applications in the tourism sector were examined and SWOT analysis of e-tourism was conducted. In the study, taking into consideration the benefits of electronic commerce in tourism sector, it is predicted that agency will end and the future potential of e-tourism is considered and the deficiencies of the current applications and solution suggestions are indicated. Kapiki (2012) analysed the current and future trends affecting the tourism and hospitality industry, including globalisation, guests’ safety and security, the importance of offering outstanding services, the new technologies that enhance competitiveness, the population ageing that impacts directly on tourist demand and the correlation between price and value. She gives detail on the title of “current trends in hospitality and tourism” about globalization, safety and security, diversity, service, technology, demographic changes and price-value. She indicated that important parameters for the hospitality industry in the future would be as; • 1466

Green and eco-lodgings.

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• • • • • •

Development of mega hotels (multi-purpose facilities with casino, shops, theatre, theme park, etc.). More boutique hotels. Intelligent hotels with advanced technology using the guest’s virtual fingerprint in order to perform all the operations (check-in, charges, check-out, etc.). Increasing employee salaries in order to retain the existing staff. More emphasis on the internet and technology. Guests’ virtual and physical social networks will be the best distribution channels.

Dubey (2016) examined the effects of new technologies on tourism. In this study, the effects of webbased applications such as digital technologies, social media, internet and cloud technologies on the preferences and experiences of tourists were investigated. Interactivity, augmented and virtual reality, space tourism was discussed in the study. The main objective in the use of Industry 4.0 in the tourism sector is to create an interactive platform based on the latest technology infrastructure. One of the main objectives of the Industry 4.0 for tourism sector is to provide services and products in a sustainable way, accessible to everyone at any time. The applications should be of a type that will facilitate the integration of the tourist to the destination, improve the quality of the experience and provide customized products and services. Practices should facilitate the integration of tourists to the destination, improve the quality of experience and provide customized products and services. In other words, it can be possible to develop the sector by providing the application of high technology products in tourism services in Industry 4.0.

MAIN FOCUS OF THE CHAPTER Today, use of automation systems, computers or communication systems have affected not only the industry or production, but also all areas of life and all sectors. Industry 4.0 will provide the highest quality standards for all engineering, management, production, operations and logistics processes, as well as great flexibility and robustness to all sectors. With the possibilities of technological advances, various variables such as costs, availability, use of resources and market demand can be easily optimized in real time. In countries this paradigm shift will allow the two strategic sectors to converge or disintegrate with each. This provides opportunities for all sectors. Industry 4.0 and its impact on the manufacturing sector have been studied in detail in many studies. However, there is very limited work in the service sector and it also faces the challenges of mass customization, digital development, intelligent work environment and efficient supply chain. Like all sectors, tourism sector is also affected by technological developments. Especially communication systems, internet of things, cloud and cognitive computing systems, web based software systems, mobile systems, internet and mobile banking, developments in national and international money transfer affect tourism sector and increase tourism mobility all over the world (see Figure 1). With the introduction of Industry 4.0, it was possible to access information easily, especially by using the infrastructure on the internet, making reservations easier and making choices by filtering the preferences according to requests and budget easily by using the internet infrastructure. This situation has helped to increase tourism mobility. The development of mobile communication and mobile internet has also had a positive impact on the development of tourism. 1467

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Some of the developments in the industry affect directly tourism and others indirectly. The developments in the transportation sector and the change in comfort and speed in airway, railway, highway and sea transportation have revived the tourism sector. Development in communication systems and social media make it easier to access information and arouse curiosity and desire to see. Figure 1. Industry 4.0 for tourism

Advances in technology offer new products and new inventions. Since many people now arrange the travel by themselves using the computer infrastructure, companies are also developing new booking methods according to this new situation. Nowadays there are countless different ways of booking for consumers, but before digitalization there was only one option: to use travel agency. Mobile phones have also increased the options. For example, online travel guides also offer the consumer some options and advantages; simple updates, links to specific information, search functions, bookmarks or feedback, and reviews of other travellers. All this both increases and facilitates the desire of the traveller to travel. While the older generation prefers to spend their holidays in a travel agency, the younger generation prefers to use online booking portals. Therefore, more and more hotels focus on online promotion. Unlike large hotels or large hotel chains, small hotels tend to rely on online sales systems and try to sell through large portal systems. Today, new media culture has developed. This culture is differentiating in every aspect of life and has changed the way consumers spend their holidays and accommodations. At the moment, the wide-

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spread use of mobile devices has made it possible for potential tourists or consumers to communicate with each other, share information resources, and make reservations in all situations and environments. A large part of the hospitality sector has integrated into digitalization and new media culture. Mobile applications have become widely used not only in the hospitality industry but also in the travel industry. Last minute deals are easily tracked and experienced. Attractive accommodation and culinary delights contribute to the rapid growth of the tourism industry. At the same time, tourism areas that have not been used accessible until recently, have benefited from these developments and started to take a share in the tourism sector. The main purpose of this study is to investigate the effects of technological developments on tourism sector. Like all sectors, tourism sector is also affected by technological developments. Today’s technological developments, also called Industry 4.0, are changing the services in the tourism sector and the way they are offered. The application of Industry 4.0 leads to changes in many areas such as hospitality, travel and booking. Nowadays, reservations for accommodation are made via the internet or mobile applications. Robots are used in hotels to produce services or virtual reality is used in marketing in tourism. Smart tourism has been mentioned recently. In this section, Industry 4.0 and its applications in tourism sector are discussed in detail. Technological developments bring some problems. One of the most important of these problems is data security. Protecting systems and data against cyber-attacks is very important. The replacement software systems and robots instead of people can causes to serious problems such as unemployment. As a result of robots replacing the worker, the workforce may lose its value. This change in the tourism sector may adversely affect the employees in the sector. Unmanned hotels where robots work, machines cook and software are serving can only be turned into places where accommodation is made and there is no social life.

INDUSTRY 4.0 AND TRAVEL 4th industrial revolution also affects travel systems. Compared to the past, today, mobility is constantly increasing. Today, more people travel than in the past. In 2030, considering that there will be 1 billion more people traveling in addition to today, and therefore transportation infrastructure will have to be transformed to meet this increase. As a result of improvements in the internet and communication, transport and accommodation can be offered as a package. Using the infrastructure of mobile systems, a transportation system will be developed that will take you from your location to the hotel where you will be staying and then bring you back to where you live. Nowadays, in the transportation and hospitality industry, most of the operations cannot be performed automatically but are performed manually. In the future, it seems that most of them will be done automatically. In this case, in the future, machines that we often call robots will interact with the customer. In addition to providing communication, mobile phones will be a device that confirms location sharing, likes, or dislikes, contributing to their preferences for subsequent travel. Communication or mobile phone is a modern need nowadays. People shares their travel experience using devices, channels, and back-end technology systems. Travel in an Industry 4.0 world where machines interact and react wisely to the physical environment will be different. Industry 4.0 will also make change on travel systems, and travel and transport will integrate (see Figure 2). 1469

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Travel agency is in a radical change in itself and the definitions, practices and management approaches in this field also change in line with global trends (Yolal, 2003). One of the changes caused by globalization in the tourism industry is that businesses start to operate on a global scale. They also have to compete on international platforms. Companies operating globally see the world as their field of activity, develop global strategies and maintain their presence in the global market. Global powers change the structures of enterprises and eliminate the boundaries restricting enterprises (Yılmaz and Yılmaz, 2005). The effects of information technologies on travel agencies are manifested in the production, marketing, distribution and management of tourism products for both the public and private sectors. Industry 4.0 increase the managerial efficiency and productivity in travel enterprises, while enabling the management of the business to adapt to the new business environment and benefit from new opportunities (Buhalis, 1998). Figure 2. Smart city and transportation

Digitalization offers an exciting opportunity for the aviation, travel and tourism ecosystem. In the next decade it has the potential to have a value of about $ 1 trillion in the industry and other sectors. The travel ecosystem changes with blurring boundaries and changing roles in the industrial landscape (Weinelt and Moavenzadeh, 2017). Weinelt and Moavenzadeh (2017) were indicated that in the next decade (2016 - 2025), digitalization in aviation, travel and tourism is expected to be: • • •

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Can generate $ 305 billion value for the sector with increased profitability Migration from traditional players to new opponents worth $ 100 billion Achieve $ 700 billion worth of benefits for customers and the wider community through cost and time savings for consumers with fewer environmental footprints, improved security and security

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•

As a result of the clear shift of existing jobs in the sector, which are expected to be partially offset by the creation of next generation skilled jobs within and outside the travel ecosystem

Digitalization will have a positive environmental impact, contributing to the footprint of a more sustainable sector through innovation in production, smart assets and efficient resource utilization. For customers, personal impact is expected to be important as travel becomes a seamless, more quality experience. The greatest social impact could be the impact of digital transformation on the travel labour force, which by 2025 could represent one in every 11 jobs worldwide (Weinelt and Moavenzadeh, 2017). Intelligent automation will change the nature of some travel jobs and completely eliminate others. However, digitally activated growth will create new employment opportunities that can eliminate the automation of existing roles, especially as strong growth is anticipated for the sector. The aviation, travel and tourism industries are at the forefront of digital innovation. However, when industry and technology trends are analyzed, it can be predicted that there will be greater changes in the future. The transportation sector has been one of the early applications of digital technologies and platforms with other name Industry 4.0. On the other hand, in emerging markets, the demand for travel driven by a growing middle class emerges. The middle class plays a leading role in the use of digital technology. In the future, it can be expected that digital technologies will develop more and demand will increase. The travel ecosystem has helped shape customer expectations for on-demand and appropriate services through digital innovation, both within and across industry boundaries (Weinelt and Moavenzadeh, 2017). The next step is to change the way they work so that they can capture the opportunities that digital transformation offers, and to expand its use in areas where the digital sector has not yet been implemented.

INDUSTRY 4.0 AND HOSPITALITY The concept of hospitality has changed drastically over the years. Online booking platforms have changed a lot, differentiated and greatly increased their market share. As one of the most important results of Industry 4.0, digitization has found application in hospitality sector and has created dynamic changes in accommodation. Hospitality as a subdivision of tourism is a fundamental part of the indoor and outdoor entertainment market. Consistent tourism demand allows the hospitality industry to anticipate demand and identify opportunities to increase consumer spending by creating a wave of secondary financial impact’ (Robinson, et al., 2013). The hospitality industry has always contributed to the development of the imagination of travellers. The competitive environment in the hospitality industry pushes the tourism sector to find new and effective solutions. One of the main trends in this area is the ability to offer new services and develop innovative applications for the development of the hospitality industry. The applicability and competitiveness of all kinds of innovations in hotel and restaurant establishments is essential (Dzhandzhugazova et al., 2015). In the last decade, there has been a significant increase in both the use of social media and the overall development of new technologies worldwide. The tourism and hospitality industry shows an impressive development due to technological developments. Industry 4.0 has started to find application in tourism sector. The increase in the use of social media, easier communication and evaluation of customers after the hotel accommodation, makes it easier to find new customers for accommodation. Technological 1471

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developments offer new opportunities for the tourism and hospitality industry. The hospitality sector sees technological developments as a way to increase profits. However, one of the most important challenges that service providers face is how to integrate into social media marketing and to verify whether it is profitable to use their business (Benea, 2014). Industrial problems, special mentality of consumers, cultural and national differences of customers benefiting from hotel services are among the factors that affect the application and development of hospitality (Pine et al., 1999). The innovative trend in the hospitality industry is reflected in the successful implementation of innovations in the hotel (Zaitseva, 2013). With the widespread application and combinations of new information, new services, products and technologies can be produced. If a hotel is not involved in any innovation process, its performance will eventually decline and may lose competitiveness (Ilyenkova and Kuznetsov, 2009). Technology is changing the way travellers interact with brands before, during and after the journey. Away from the spirit of hospitality as a human-centred industry, the adoption of new technologies allows hospitality businesses to offer greater personalization and better service (Dzhandzhugazova et al. 2016). In their study, Shamim et al (2017) examined the applications of Industry 4.0 in the service sector and the situations in the hospitality industry. All the challenges of Industry 4.0 require continuous innovation and learning that depend on people and the capabilities of the business. Appropriate management approaches can play a vital role in developing dynamic capabilities and in an effective learning and innovation environment. Shamim et al (2017) proposes a framework of management practices that can support innovation and the learning environment in an organization, and therefore recommends the adoption of Industry 4.0, which will enable the adoption of technology, such as digital enhancements and the implementation of cyber physical systems (CPS). The Hospitality Service Providers (HSP), use the Internet of Things (IoT) technology to provide competitive advantage in the market. In the accommodation sector; IoT, sensors, actuators, identification labels, mobile devices, etc. devices can communicate directly or indirectly between the traveller and the accommodation facilities. The paradigm of IoT can provide to the HSP different ways for interacting with guests and collecting real-time data. When HSP systems are used, guest behaviors and preferences can be measured with very high accuracy. IoT also helps to the HSP for improving the efficiency of multiple departments (Figure 3). The entertainment and hospitality industry is one of the most important elements of the global economy. In recent years, the widespread use of new technologies in the tourism sector, like all sectors, has affected the way in which services are provided and received. Kansakar et al. (2019) investigated how new technologies and guest experiences are currently being used in the hospitality industry and how they have changed the hospitality service platform. In this study, it is predicted that the Internet of Things (IoT) technology will affect hospitality services in the future. Verevka (2019) examined the role of digital innovations in the hospitality industry and discussed the impact of the digital revolution on business management. In this study, the conceptual aspects of Industry 4.0 are discussed and the current situation and the development of the digital transformation of the industry are analyzed. Pilot projects have been researched to implement Industry 4.0 in the hotel and restaurant business. Based on the researches, the basic conditions for achieving successful digital transformation and reducing the risks of digital innovation in the hospitality industry have been determined.

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Figure 3. Industry 4.0 for hospitality

INDUSTRY 4.0 AND GASTRONOMY Food is one of the most important key elements of identity and culture of a nation, as well as its symbols, history, discourses and myths (Smith, 1995). Studies have shown that there are strong historical links between food and tourism that unite them as a political force. In addition, food and agriculture have traditionally been strong economic sectors in the formulation of public policies and strategies in all societies. Agricultural systems, food products and outputs in the countries have been the most important inputs of the tourism sector and also sometimes have been among the symbols and rituals for the countries. The concept of gastronomy emerged in line with the need for nutrition. The concept of gastronomy, which mostly used as the science of eating and drinking, has been influenced, changed and evolved according to conditions throughout history. Gastronomy and Gastronomic Culture is one of the most effective parameters for choosing a destination for the guest. One of the reasons for visiting a destination is the culinary culture of that area (Zagralı and Akbaba, 2015); Çevik and Saçılık, 2011). There is a broad consensus that gastronomy plays an important role in enhancing the pleasure and enjoyment of the guest. The food and drink culture of the local people varies in each destination and it can turn into a kind of entertainment by attracting the attention for the guests to discover what the local people eat and drink (Baytok et al., 2001).

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The concept of food is one of the basic elements of the tourism sector and constitutes an important aspect of the accommodation process. Food Tourism is a concept that includes the socio-cultural characteristics of food. There are a limited number of international researches on gastronomic tourism (Hall & Sharples, 2003). It is thought that the globalization phenomenon, which is gaining momentum nowadays, has started to make the world a market that offers the same or similar products (Toksöz and Aras, 2016). Gastronomy Tourism enables the realization of alternative tourism types. This requires the protection of socio-cultural heritage. Gastronomy is also important for preserving historical and cultural heritage and transferring it to future generations (Hall et al. 2003). Figure 4. 3D printing and Gastronomy application

In their studies, Gunes et al (2018) discussed the historical development and current status of gastronomy. They examined the effects of advances in digital technology on gastronomy. It is stated that globalization and technological developments have an effect on food, beverage and gastronomy. It has been reported that the use and development of robots, 3D printers or cooking technologies to have an impact on gastronomy. With the development and spread of 3D printers, it becomes also possible to apply them to the field of gastronomy. Thus, it is possible to produce food in very difficult and complex figures. Considering that gastronomy appeals to the taste and the eye, with technological advances and the use of robots, visual foods that cannot be made today can be cooked using 3D printers (see Figure 4). Depending on the development of robots, it will be also possible to produce food by robots in the future. Depending

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on the development of automation and cooking techniques, foods as same taste and similar appearance can be cooked. Automation can also prevent time loss. Temperature and time parameters can be easily set in the cooking units. This may prevent under- or over-cooking of food.

INDUSTRY 4.0 AND SMART TOURISM ‘Smart’ is generally used to describe sensors, big data, open data, new connection methods, and information exchange. The concept of smart develops depending on technological improvements and affects economic and social developments. Höjer and Wangel (2015) argue that individual technological developments are not so important. In fact, they argue that the rational creator has synchronization and harmonious use of interconnections of different technologies. In some cases, the concept of ‘smart’ is simplified and used by politicians to direct societies and sell technological solutions. It is also used as to provide free wireless internet or to develop mobile applications. It should be noted, however, that internet or mobile systems are only infrastructure, not ‘smart’. Using these infrastructures and technologies, data collection, management, processing and conversion to new data for a particular purpose are very important concepts. Although all of these are new approaches to the concept of smart tourism, they are not fully capable of explaining smart tourism. In the context of tourism, smart is used to describe a complex connection of all intelligent systems used in everyday life. In the tourism sector, there is an incredible institutional support for switching to smart tourism applications. In some cases, there are also internal and external pressures in the sector to realize smart tourism. Efforts have been made to advance smart tourism applications around the world in regions and countries where the tourism sector is vibrant. Many governments provide financial and technological support for the establishment of technological infrastructure that supports smart tourism and endeavors to establish infrastructure. Becoming ‘smart’ is widely acknowledged in the need for a more customer-oriented approach in order to develop the experiences, needs and preferences of tourists and to evaluate their needs to achieve better satisfaction (Correia et al., 2013, Prayag et al., 2013). Tourists are more interested in experiences from a wide range of products and services linked to the nature, history, gastronomy and culture of the region they visit. For this reason, tourism operators are required to provide ‘all-inclusive’ package solutions that can include a large number of products and services in order to increase the satisfaction of tourists (Buhalis & Law, 2008). Gretzel et al. (2015a) identified smart tourism in their studies, addressing the current smart tourism trends and studied the technological developments and their effects on enterprises. They discussed the expectations and disadvantages of smart tourism. In their study, Neuhofer et al. (2013) focused on tourism experiences that develop technology as an integrative conceptual framework to define today’s tourism as a combination of experiences, co-creation and technology. The concept of smart tourism developed with technological developments. The concept of Industry 4.0 also prepared the ground for the emergence of smart tourism. The development of internet infrastructure, mobile applications and smart city projects also contributed to the spread of smart tourism and smart tourism destinations (see Figure 5). The focus of smart tourism is the development of innovation structures and the competitiveness of the tourism sector. Thus, it is possible to use resources less while providing efficiency. With the use of smart data, it will be possible to enrich tourism by using existing

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data and developing new ones. Smart end-user applications have been developed to transform the experiences of tourists into positive ones. Smart Tourism has focused on the use of advanced technologies to transform data into efficient new business models by using and evaluating data collected through physical infrastructures and social connections. The objective of Smart Tourism is to analyze tourism products, services, venues and experiences for a wide stakeholder community and to create services that meet the requirements. By using new technological developments, it is possible to develop innovative and entrepreneurial enterprises and sectoral connections and create productive spaces. Major changes have been experienced in the technological fields of tourism in recent years. As a result of e-tourism, global integration and global distribution and central reservation systems have been developed (Buhalis 2003; Werthner and Ricci 2004). The spread of social media has accelerated the sharing of information in the tourism sector (Sigala et al. 2012). The use of the Internet and mobile internet has facilitated the knowledge of tourism sector, consumer expectations, mobility and demands of tourism consumers (Buhalis and Law 2008; Wang et al. 2012). However, smart tourism has made progress especially with the use of digital systems and the physical and governance infrastructure of the tourism sector. New smart systems have started to be implemented in tourism systems and there has definitely been a significant progress in the evolution of ICT in the tourism sector (Gretzel, 2011). The introduction and expansion of social media and the internet has helped to develop the dynamic infrastructure connections and technological networks necessary to achieve the Smart Tourism Goals. The development of social media is considered to be a useful and rich source of information in the development of tourism (Miah et al., 2016). Big Data is one of the most fundamental data defining the ‘knowledge economy’ and stands out as an emerging field of research for researchers and practitioners (De Mauro et al., 2016, Erickson and Rothberg, 2014, Laney, 2001). Del Vecchio et al. (2018), in their study, aims to show how giant Social Big Data that can be obtained by tourists can improve the value creation process for Smart Tourism Goals. Bernabeu et al. (2016), taking into account the capacity of destinations, discussed the Big Data technology used in tourism planning and management for the new Smart Tourism Destination (STD) approach. Big data technology, using advanced technological infrastructure, produce large amounts of data. The HTA approach and Big Data Technology (BDT) can be handled together so that some of the most typical cases applicable to the tourism sector can be analyzed. With the use of this technology, strengths and weaknesses in companies and tourist destinations can be identified from a wider perspective. By cooperating with the enterprises in the tourism sector and analyzing the perspectives of the entrepreneurs and experts, a healthier and sustainable growth of the tourism sector can be achieved. Given the high dependence on information and communication technologies (ICT), the information intensity of tourism is not surprising (Law et al. 2014; Koo et al. 2015; Werthner and Klein 1999; Benckendorff et al. 2014). Due to the development of the internet, there has been a major development in the area, also known as e-tourism. Innovations have begun to be applied easily to the sector and the sector and users (tourists or travelers) have benefited from the opportunities offered by technology. Sharing information easily has a positive impact on both the tourism sector and the user. ICT applications are available for tourists in different cities around the world. Some cities offer to the tourist some physical infrastructure. For example, in Barcelona, tourists are informed about the arrival time and route of the buses. There is also USB ports for charging mobile phones at bus stops. The city 1476

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Figure 5. Components of smart tourism

of Brisbane has recently installed over 100 pointers at points of interest to communicate information to tourists via mobile application, if within a certain radius of their location. Amsterdam uses beacons to allow tourist signs to translate them into different languages, and Amsterdam ArenA is testing sensors for better crowd management. Seoul is investing heavily in providing tourists with free wireless internet and smartphones. Jeju Island in South Korea has declared it a smart tourist destination using innovative technology for content presentation (Gretzel et al., 2015b).

INDUSTRY 4.0 AND SHOPPING Shopping is a very important economic activity. It is also used as a leisure tool to meet a wide range of social and psychological needs (Howard, 2007, Jones, 1999, Wakefield and Baker, 1998). Shopping, which is also considered as a great leisure activity, has become an important element in tourism (Law and Au, 2000). All over the world, shopping is no longer just a means of meeting needs. It varies depending on the differences in social, cultural and economic trends. It is used as a method to meet the created demands. (Bellenger and Korgaonkar, 1980; Michalkó and Timothy, 2001; Timothy, 2005). With the changing social structures and the impact of globalization, tourists do not do shopping only in order to meet their needs. Tourists buy clothes, souvenirs, art and crafts to remember the countries they visit. Thus, shopping becomes a motivating factor for travel and becomes an important part of the tourism

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experience (Timothy and Butler, 1995; Timothy, 2005). In addition, shopping is considered to be one of the important parameters of the tourism sector and is part of their distillation (Goeldner et al. 2000). Tosun et al. (2007) examined tourists’ satisfaction with local shopping culture, staff service quality, product value and reliability, physical characteristics of stores, payment methods and other shopping and store features. The different levels of satisfaction of the tourists were investigated with different store and shopping features in the study. Based on the results of the research, it is suggested that providing a higher level of shopping experience for tourists and increasing the contribution of shopping to the regional economy should be supported by various financial and educational tools to continue the production and sale of authentic handicrafts and souvenirs. Shopping acts as a factor that influences and motivates tourists’ travel decisions and consumer behaviour, such as visiting, having fun, experiencing and getting to know the local culture (Fodness, 1994). Law and Au (2000) describe shopping as follows; the consumption of purchased goods is a source of pleasure and satisfaction and a driving force for tourism. Shopping is one of the oldest and most common travel related things. In most cases, lack of shopping for tourists is seen as a lack of holiday experience. (Keown, 1989; Turner and Reisinger, 2001). Mostly, tourists buy goods or souvenirs not only for themselves, but also for relatives and friends (Anderson and Littrell, 1995; Kim and Littrell, 2001). Shopping, which is one of the most important tourism activities, contributes economically to the visited countries because money is just spent for fun (Jackson, 1996; Asgary et al., 1997). However, the effects of shopping are not limited to the local, regional and national economy. When tourists return to their country, products purchased by tourists can help them develop a positive image for the visited country. They may share feeling with their friends and relatives. People in general and tourists in particular tend to share their experiences through the photos, videos and items they buy. Therefore, tourism can be used as an alternative economic growth strategy. It is also used as a political tool for the development of industry and its promotion to international markets (Richter, 1989; Hall, 1994; Tosun, 2001; Tosun and Fyall, 2005). Depending on technological changes, the shopping style also changes. Shopping centres are becoming more and more widespread. In shopping centres, many products and services are offered together. Tourists visit the natural and historical places in the countries they visit, as well as shopping malls. In the countries visited, technological products are marketed as well as food, clothing and handicrafts.

SOLUTIONS AND RECOMMENDATIONS It can be foreseen that technological application to the tourism industry will continue especially in tourism-intensive cities and countries. Depending on technological developments, tourists’ expectations are changing. Tourists want to get easy information, find their way easily and easily go to wherever they want, while traveling comfortably in the countries they visit. At the same time, the ability to use mobile phones and access to the Internet easily has become a priority. Smart tourism starts with smart city infrastructure. However, tourists will need to be provided with information to improve their satisfaction by processing data in the cities. For this reason, it is very important to develop mobile applications that contain information about cities and provide them to visitors. Smart tourism should be approached as a whole. Hotels, restaurants, local governments, central governments, data providers, transportation sec-

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tor are all part of this system. It is not possible for companies operating in the tourism sector to provide smart tourism. By using technological applications in the tourism sector, guests can be provided with excitement, efficiency and comfort. Recreational industrial robots have been used in tourism for a long time mainly because of their efficiency and reliability. Robots with voice and face, at the reception, check-in and carrying goods are available at the hotels. Robots appear almost at all levels of the tourism industry. The main drivers of its widespread use are: productivity, accessibility and service enhancement. With the use of artificial intelligence in the tourism sector, human-robots who learn more easily began to serve. With the development of technology and the increase in application areas to tourism, it can be seen that more robots are used in the entertainment sector. Virtual Reality (VR) has entered daily life as a technology that reflects real-life situations and can be used to create interactive scenarios. With VR, complex situations can be simplified, simulated and recreated imaginatively. Virtual reality is also widely used in the tourism sector and hospitality. VR has emerged as a very important concept in the hospitality industry and has begun to be used. Guests can learn and experience the hotel’s physical environment before visiting the hotel. The increased accessibility of virtual reality creates new marketing and brand opportunities in the hospitality industry. It is possible to reach customers with modern methods by using virtual reality. VR has begun to replace traditional brochures. Using virtual reality methods, resorts or hotels can provide more immersive experiences such as VR presentations, providing 360º videos or more personalized tours. Virtual reality has found its use as a marketing tool. Virtual reality for companies in the field of tourism can be seen as a tool to promote. From a user’s point of view, virtual reality can be a tool to experience new exciting things. With VR, virtual environments can be created for tourism planning. Using VR, tourists can experience an artificial and limitless environment. Considering the virtual reality in the tourism sector, one should be aware that a person - customer or tourist - has a virtual experience from a touristic point of view (Guttentag, 2010). This is due to the fact that virtual reality is an interactive and immersive world (Mazuryk and Gervautz, 1996). Virtual technology can provide an almost unlimited experience for users (Guttentag, 2010). It should be noted that, depending on the type of virtual environment, it has different effects on all five senses of the human body (Gruber 2015). Technological developments and chat habits made via social media have enabled the development of chat applications in hotel technology. Chatbot is the chat interface and with this application, customers can interact with a person or artificial intelligence and contact the accommodation facility. The chat industry has great potential in the hotel industry as users spend a long time in messaging applications today. Using chat interfaces or applications, guests can make reservations at hotels, access hotel services, be aware of daily activities in the building, learn about breakfast or mealtimes, and forward any room problems to the housekeepers. Changing conditions and advances in technology both create a change in hospitality and offer new opportunities.

FUTURE RESEARCH DIRECTIONS Industry 4.0 has a positive role in solving a variety of problems related to data management and other technological issues. The future of the hospitality industry will be shaped by current developments with the Internet of Things (IoT) technology. 1479

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The concept of Industry 4.0 also began to change the way we shop. Mobile or online shopping is becoming more common. With the impact of globalization, it became possible to purchase every product from anywhere in the world. Therefore, local products and crafts began to attract more tourists. One of the biggest problems for tourists is the return of purchased products. Due to the widespread use of internet shopping, there has been an incredible growth in the logistics sector. Serving the products purchased by tourists to their doors when they return home will increase the desire for shopping. With the next development, by using data exchange on technological infrastructure, using a single image of the travellers and synchronizing data between all systems, taking into account previous experiences and decisions, preferences can be made for him according to his previous wishes. At this point, with the creation of new technologies such as robotics, the so-called Smart Travel systems can be developed. Depending on technological advances, robots may be expected to provide services at airports, trains station or bus stations during the travel. As a result of Industry 4.0, in the future, travel technology can be expected to become smarter. In the future, as systems that process, measure and evaluate data evolve, it will be possible to transform the travel system into smarter systems. Depending on the technological improvements, the concepts of smart buildings or smart houses are also developing. When the guests stay in a place, the comfort requests they desire and adjust in the environment they live in can be used in the next accommodation via mobile infrastructure or internet of objects by using the mobile big data ’infrastructure. This situation will increase the satisfaction of the guests regarding to the facility as well as increase the efficiency of the facilities. In general, the hospitality industry should create a mutually beneficial platform between guests and HSP, with new technological advances and facilitating partnership. The platform should enhance HSP’s operational management efficiency while providing an exceptional travel experience. In addition, after the evolving revolution of IoT technology, future potential hospitality services will also develop.

CONCLUSION The tourism sector is growing rapidly. Tourism is an important sector in terms of the promotion of countries, normalization of relations between countries and the promotion of cultures. Tourism sector is also changing due to technological developments. Technological developments change the expectations of individuals and societies. The expansion of the Internet and mobile applications contributed to the growth of the tourism sector. People want to visit the countries and regions they see. The spread of social media also increases the desire to travel. People have a desire to visit places that friends or famous people share on social media. Traveling habits or travel patterns also change. The reason for this rapid change is due to technological developments. In this study, the effects of technological developments on tourism sector are examined. Technological change is called Industry 4.0. Although the concept of Industry 4.0 was initially used for the production sector, it is now being adopted for all sectors. With the digitalization, developments in communication infrastructure, and the use of mobile systems and internet infrastructure, tourism sector is one of the sectors positively affected by Industry 4.0.

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ADDITIONAL READING Azmi, A., Nur’Hidayah Che Ahmad, K. K., Abdullah, D., & Zubir, H. A. (2018). Industry 4.0: Teaching Preferences, Perceptions, and Challenges among Tourism and Hospitality Academicians. Journal of Academic Research in Business and Social Sciences, 8(15), 350–365. Dohale, V., & Kumar, S. (2018). A Review of Literature of Industry 4.0. In National Convention of IIIE and International Conference. Gerd, J. H. (2019). Industry 4.0: A supply chain innovation perspective. International Journal of Production Research. doi:10.1080/00207543.2019.1641642 Güneş, E., Bayram, Ş. B., Özkan, M., & Nizamlıoğlu, H. F. (2018). Gastronomy Four Zero (4.0). International Journal of Environmental Pollution and Environmental Modelling, 1(3), 77–84. Holjevac, I. A. (2003). A vision of tourism and the hotel industry in the 21st century. International Journal of Hospitality Management, 22(2), 129–134. doi:10.1016/S0278-4319(03)00021-5 Ivanović, S., Mijolica, V., & Roblek, V. (2016). A holistic approach to innovations in tourism. Proceedings of ICESoS, 2016, 367–380. Mil, B., & Dirican, C. (2018). Industry 4.0 technologies and its effects on tourism economics. Journal of Multidisciplinary Academic Tourism, 3(1), 1–9. doi:10.31822/jomat.347736 Minaudo, M. (2018, October). New Technologies Applied to Tourism 4.0. In Global Conference on Business, Hospitality, and Tourism Research (GLOSEARCH 2018).

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Oztemel, E., & Gursev, S. (2018). Literature review of Industry 4.0 and related technologies. Journal of Intelligent Manufacturing. doi:10.100710845-018-1433-8 Papathanassis, A. (Ed.). (2011). The long tail of tourism: Holiday niches and their impact on mainstream tourism. Springer Science & Business Media. doi:10.1007/978-3-8349-6231-7 Pindžo, R., & Brjaktarović, L. (2018). Digital Transformation of Tourism. In TISC-Tourism International Scientific Conference Vrnjačka Banja. 3 (1), pp. 340-355. Rio, D., & Nunes, L. M. (2012). Monitoring and evaluation tool for tourism destinations. Tourism Management Perspectives, 4, 64–66. doi:10.1016/j.tmp.2012.04.002 Trappey, A. J., Trappey, C. V., Govindarajan, U. H., Chuang, A. C., & Sun, J. J. (2017). A review of essential standards and patent landscapes for the Internet of Things: A key enabler for Industry 4.0. Advanced Engineering Informatics, 33, 208–229. doi:10.1016/j.aei.2016.11.007 Wallace, S., & Riley, S. (2015). [an industry perspective. Journal of Tourism Futures.]. Tourism (Zagreb), 2025. Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2017). Intelligent manufacturing in the context of industry 4.0: A review. Engineering, 3(5), 616–630. doi:10.1016/J.ENG.2017.05.015 Ziyadin, S., Litvishko, O., Dubrova, M., Smagulova, G., & Suyunchaliyeva, M. (2019). Diversification Tourism in the Conditions of the Digitalization [IJCIET]. International Journal of Civil Engineering and Technology, 10(02), 1055–1070.

KEY TERMS AND DEFINITIONS 3D Printer: A machine allowing the creation of a physical object from a three-dimensional digital model, typically by laying down many thin layers of a material in succession. Industry 4.0: The 4th industrial revolution, called as Industry 4.0, is the process in which the use of computers and automation in the industry. IoT: The growing network of devices that can connect, communicate and transfer data between one another. Robot: A robot is a machine—especially one programmable by a computer— capable of carrying out a complex series of actions automatically. Smart tourism: Smart Tourism has focused on the use of advanced technologies to transform data into efficient new business models by using and evaluating data collected through physical infrastructures and social connections. Tourism 4.0: The aim of Tourism 4.0 in tourism is to reduce the negative effects of tourism, to see the effects of the use of technology in the tourism sector and to develop cooperation models in partners. Virtual Reality: Virtual reality (VR) is a simulated experience that can be similar to or completely different from the real world. This research was previously published in the Handbook of Research on Smart Technology Applications in the Tourism Industry; pages 205-228, copyright year 2020 by Business Science Reference (an imprint of IGI Global). 1487

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Leadership 5.0 in Industry 4.0: Leadership in Perspective of Organizational Agility Bülent Akkaya https://orcid.org/0000-0003-1252-9334 Manisa Celal Bayar University, Turkey

ABSTRACT In today’s competitive environment, agile firms tend to be more successful. If today’s technology companies, which are leaders in their sector, may fail in that competitive environment, it would be possible that they might lose their market leadership in the future. Some companies which were in the top in market in their own sector in the past are likely to be stand back from their competitors for not adapting to market change conditions. Fast process of technology and digital world are taking place in all organizational authoritative in all area and in all kind of sectors because the business world is transformed by the postmodern revolution-Fourth Industrial Revolution. In this dynamic environment, leaders should learn new management behaviors, with which they can communicate both internal and external environment of their enterprises by the strategies of being agile and innovative organizations. This can be by being aware of changes in environment and having the ability to manage these changes for the company’s favor.

INTRODUCTION Fast process of technology and digital world are taking place in all organizational authoritative in all area and in all kind of sectors, because the business world is transformed by the postmodern revolutionFourth Industrial Revolution. In this dynamic environment, leaders should learn new management behaviors, with which they can communicate both internal and external environment of their enterprises by the strategies of being agile and innovative organizations. This can be by being aware of changes in environment and having ability to manage these changes for the company’s favor.

DOI: 10.4018/978-1-7998-8548-1.ch074

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 Leadership 5.0 in Industry 4.0

Change is an indispensable fact of our lives. Change happens in organizations, in people behaviors and ideas and in every area of the world we live in. In particular, the continuous changes in customers’ demands and needs make it inevitable for managers and leaders of companies to constantly adapt to these changes in order to keep their firms in a competitive environment. Leaders and managers have sought new solutions in order to manage and to survive their enterprises in dynamic and changeable business environment. For that, they firstly focused on adaptation, then on flexibility and finally they focused on agility, especially in production in order to adapt to change. Especially after 2000s, this change has been getting increased and for companies it has been inevitable to be agile organizationally. We live in a digital time, and in this time, most of the people are in communication with the technology itself instead of each other. The changes in this time force leaders and managers to understand both changes and developments in the environment and to adapt those changes to their enterprises. Kouzes and Posner (2003a) state that leaders and managers are expected to have four qualities such as vision, trust, courage and knowledge in order to achieve this adaptation. Today’s business environments are changing at an extraordinary rate (Günsel & Açikgöz, 2013). In today’s competitive environment, agile firms tend to be more successful. If today’s technology companies, which are leaders in their sector, may fail in that competitive environment, even it would be possible that they might lose their market leadership in the future. Because some companies which were in the top in market in their own sector in the past are likely to be stand back from their competitors for not adapting to market change conditions. Some of these firms have not had high market share which they had in the past, although they keep their surviving. It may be said that one of the main reasons of these companies losing their leadership in their sector is to fail meeting customers’ needs and demands. Companies need to develop their structures and processes according to customers’ demands, by taking into consideration variables in both internal and external environment in order to get competitive advantage and maintain their market position. Therefore, agility is quick turning into a key driver for organizations additionally as an important issue to a firm’s ability to survive and thrive in uncertainty market (Ganguly et al., 2009). Organizational agility, which has been used in the field of production, was first systematized in the literature of organization and management in 1990s and after that has been used in different fields. For example, organizational agility has been studied in the field of human resources (Shafer, 1997), in the field of production (Lopes, 2009), in terms of sustainable competition (Mason, 2010) and in the performance of employees (Latham, 2014). However, the important question here is there a relationship between multiple leadership styles and organizational agility? Even no study is seen in literature that examine the relation between multiple leadership style and organizational agility in techno-enterprise firms which create technology and mostly need to adapt changes in environment in industry 4.0 term. This gap in literature leads us to focus on this basic research question in our study whether “Multiple leadership styles in techno-enterprise firms have an effect on organizational agility or not?” Organizations need a leader to reach their goals, to maximize their profit and value, to overcome chaos, turbulent and incomprehensible situations and the leaders have been able to rescue organizations from these adverse situations with the least harm and also they have been able to maintain their organizations presence. Throughout history, different leadership approaches and styles have been defined by different researchers. The multiple leadership approach, one of these approaches, will be discussed in this research.

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Leadership and Historical Background of Leadership The concept of leadership is as old as humanity history. Many kinds of leadership have been occurred during history in military system, organization and enterprises. For business, the concept of leadership became increasingly important, especially, after the industrial revolution period which stable market environment and competitive pressure existed. Leadership may sometimes be confused with some terms such as power, authority, management, administration, control, and supervision (Yukl, 2010). There are numerous definitions of leadership in literature. In this context leadership has been defined in terms of traits, behaviors, role relationships, or direction position. But it can be minimized with some common definitions as below: • • • •

It is a behavior of an individual (Hemphill & Coons, 1957), It is a process of direction (Jacobs & Jaques, 1990), It is the ability to influence and motivate others (House et al, 1991), It is the ability to start evolutionary change processes (Schein, 1992).

As it is understood through the definitions above, leadership is a concept on that is important for the effectiveness of organizations. It may be hard to survive without leadership for an organization and every member should be a leader in today’s dynamic environment of organizations (Ekren, 2014). In social sciences leadership is among the popular widely studied topics. The literature has organized the theories of leadership into five categories: (1) trait (2) behavioral (3) contingency and (4) leader-member exchange (5) contemporary leadership. It is important to understand the historical development of these theories to see how modern theories align with the contemporary nature of business and organizations.

Trait Theories of Leadership Trait theories of leadership, rooted in the “Great Man” of the 18th and 19th centuries, center on the personal characteristics that differ leaders from non-leaders (Kirkpatrick and Locke, 1991; Bryman, 1992). It has the belief that leaders are born not made and individuals have qualities which make them more effective leaders.

Behavioral Theories of Leadership Behavioral theories of leadership, being focused on late of the 20th century, consider leader behaviors as factors in deciding leadership effectiveness. It has belief that effective leaders, such as laissez-faire, autocratic or democratic leadership, differ from ineffective leaders based on defined behaviors of action. The three most popular cited behavioral leadership studies are the University of Michigan Studies (Likert, 1967), The Managerial Grid (Blake and Mouton, 1970) and the Ohio State University studies (Bass & Stogdill, 1990).

Contingency Theories of Leadership Contingency theories of leadership, being popular in late of the 21st century, described leadership in terms of leader behavior. Employers, task and culture of an organization and particular environment 1491

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may lead the leadership effectiveness. Fiedler (1967) stated that high performing groups are occurred when a leader’s natural style fits the situation or characteristics of organization. The three most popular models of contingency theories of leadership have been met in literature. Least Preferred Co-Worker (LPC) identifies leaders as either oriented toward human relations or tasks. The Path-Goal theory suggests that the role of the leader is to explain goals and provide the path and resources for followers to achieve those goals (House, 1971). Situational Leadership Model imply the factors of task behavior, leadership behavior to determine the effectiveness of the leader and consider that leaders should focus more on relationships and less on tasks (Hersey et al., 1988; Deckard, 2009).

Leader-Member Exchange Theories of Leadership Leader-Member Exchange Theories of Leadership (LMX), paralleling with contingency theories of leadership in late of the 21st century, describes that leadership does not occur in a vacuum, it is characterized by the interdependence of leaders, followers and organized groups (Gooty et al., 2012). The theories focus specifically on the mutual relationship between individual leaders and followers (Graen & Uhl-Bien, 1995). Leader-member exchange theories also suggest that leaders develop close relationships with members.

The Contemporary Leadership Approach Multiple leadership styles, with its emphasis on the relationship between leaders and followers, are rooted in the concepts described in leader-member exchange theory. This term, which is considered to be beyond the situational approach by the researchers, is named the “Contemporary Leadership Approach” and the term leader is used referring to mean “farsighted” (Sashkin, 1988), “charismatic” (Conger & Kanungo,1988; House,1977) and “transformational” (Bass,1985; Bass and Avolio,1994). Burns (1978) was the first person using the term transformational leadership, one of type of multiple leadership styles. Since then, multiple leadership approach has drawn attention all over the world that has been studied in different disciplines. He described transformational leadership as a process in which leaders and followers engage in a mutual process of raising one another to higher levels of morale and motivation (Burns, 1978). Transformational leaders lead their followers towards long-term goals instead of short-term ones and motivate them toward self-realization. This kind of leader prefers intrinsic rewards, rather than materialistic rewards and relies on a personal value system (Lewis-Kunhert, 1987; Gibson & Donnelly, 1994). Multiple leadership styles play an important role in the success of presentday organization; multiple leadership approach includes three leadership styles called transformational, transactional and laissez faire leadership.

Transformational Leadership Burns to begin with presented the concept of changing authority in his expressive inquire about on political pioneers, but this term is presently utilized in organizational brain research. According to Burns (1978), changing authority may be a handle in which “leaders and followers help each other to advance to a higher level of morale and motivation”(p.425). Kuhnert and Lewis (1987) stated that the transformational leader links an organization’s vision to the aims and personal standards of its employees. Such 1492

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leaders prefer internal tools to reward their employees, rather than material values and they are based on personal value systems (Kuhnert & Lewis, 1987). Transformational leaders based on these values have features. These features are expressed as follows by (Tichy&Devanna, 1986): • • • • •

They are visionaries seeing themselves as change agents, They are leaders taking risks, They believe in the people in their organization, They learn from their experiences They overwhelm uncertainty which brought by change.

In industry 4.0, transformational leaders thanks to these features play a crucial role in managing this change in today’s businesses where change happens fast.

Transactional Leadership Mutual interest relationship is very important between transactional leader and his/her followers. If the leader meets the needs and expectations of their followers, they will perform the demands of the leader. Therefore, it may be said that in the transactional leadership understanding, the effectiveness of the leader depends on the extent to which the changing needs of followers. The interaction between followers and the leader is based more on moral values and the interaction between followers and the leader is based on the material and external rewards such as salary increase, permission (Kuhnert & Lewis, 1987:649). As Tichy & Devanna (1986) stated that “Transactional leaders are concerned about a more stable environment with slight competition”.

Laissez-Faire Leadership In multiple leadership approach, transformational and transactional leaders are mostly compared with laissez-faire leadership styles (Bass and Stogdill, 1990). Laissez-faire leaders hesitate to decide or take positions, hesitate to act, hesitate to use their authority, and often disappear when they are needed. Each of these leadership styles stated above has its own components. These components are shown in Table 1.

ORGANIZATIONAL AGILITY AND ABILITIES Organizational agility is a main concept for organizations in today’s competitive and fast-changing market environment (Bessant et al., 2002; Goodhoue et al., 2009). It enables an organization to adapt efficiently, rapidly and accurately way in fast-changing market environment (Ganguly et al., 2009). It is about reaction and adaptation to changes which driven by customers, competitors and technology. It shows that change is a key aspect of organizational agility. There are many researches and studies related to adaptation of organizational structure and processes to changing environmental conditions in the field of management organization, and also researches have been conducted on how organizations have coped with ambiguity and change by adaptation to environmental conditions (Burns & Stalker, 1961; Hage& Dewar, 1973). Competition is no longer local, but global, technology is getting more evolved, environment is changing rapidly and industrial environment 1493

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Table 1. Multiple leadership styles and components Transformational Leadership

Transactional Leadership

Idealize Influence-Attitude Leaders are admired, respected, and trust.

Contingent Reword Leaders get agreement on what needs to be performed, and promise rewards in exchange for carrying out the agreement between leaders and followers.

Idealize Influence-Behavior Leaders demonstrate high standards of ethical and moral conduct.

Management by Exception-Active Leaders arrange to actively monitor deviances from standards, mistakes, and errors in the follower’s agreement and to correct mistakes and errors when necessary.

Inspirational Motivation Leaders motivate and inspire their followers.

Management by Exception-Passive Leaders keep waiting for deviances, errors and mistakes to occur and then correct them.

Laissez-Faire Leadership Laissez-Faire Leaders are inactive, ineffective and nothing is transacted.

Intellectual Stimulation Leaders stimulate their followers ‘perform to be more innovative and creative. Individualized Consideration Leaders pay special attention to each individual’s needs for achievement as coach.

is developing more. That’s why enterprises must be agile in order to manage their environment and to survive in dynamic and competitive environment. In order to get over these problems, leaders have tried to find new solutions. For that, firstly they focused on adaptation, then on flexibility and finally they focused on agility, especially in production in order to adapt to these changes. As a natural consequence of this, the concept of organizational agility can be stated as rapidly change and adapt in response to changes not only in the production department but in all departments of firms. Organizational agility, which was used in the field of production in 1990s and after that has been used in different fields, is a new concept so it has not a common definition, but it is defined as an organization capability to respond rapidly to market changes by Breu et al. (2001). Organizational agility is the manufacturer’s ability to react quickly to sudden and unpredictable changes (Putnik, 2001). Organizational agility can be also explained as the ability of the enterprise to respond quickly to unforeseen and unexpected changes in business internal and external environment. In other words it can be defined as the ability of an organization to respond as fast as to changing competitive conditions

Organizational Agility Abilities Organizational agility has its own characteristics and abilities. To tell whether an enterprise or organization is organizationally agile, it must have some capabilities. The organizational agility model usually has an identical structure consisting of three elements. These are (Sharifi & Zhang, 1999:11; 2001:775):

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• • •

“Agility Drivers” which are defined as the way in which the enterprises operate (the pressures which force enterprises to adapt the changes in the business environment, the need for agility, the strategic intent to be agile and agility strategy). “Agility Capabilities” which are explained as the organizational agility of the business (these are the skills required for businesses to respond positively to the changes in business environment, consisting of four dimensions; responsiveness, competence, flexibility, and speed). “Agility Providers” which mean that administrators/managers use the agility capabilities of the business (practices that are necessary for agility skills to be acquired).

Although there are some differences in opinion in different researchers in the literature, Sharifi & Zhang (1999), Zhang & Sharifi(2000), Sharifi et al. (2001), Crocitto & Youssef (2003), Lin et al. (2006), Shahaei, (2008), Zhang (2011), Nejatian & Hossein Zarei (2013), Mohammadi et al. (2015) state that organizational agility has four basic abilities; Responsiveness, Competence, Flexibility and Speed.

Responsiveness This ability is stated to be the first ability of a business organizational agility (Sharifi & Zhang, 1999; 2001), Sharifi et al. (2001), Crocitto & Youssef (2003), Lin et al. (2006), Shahaei (2008), Zhang (2011), Nejatian& Hossein Zarei (2013), Mohammadi et al. (2015). It is the ability to be aware of the changes in the market, and the ability to react these change quickly. Due to technological and environmental changes, customer demands and needs may change over time. Businesses must respond to these changes on the right time and right place. If they respond to those changes, they could gain a competitive advantage. This is due to the fact that the business being agile organizationally and using its responsiveness ability.

Flexibility Because of the rise of this concept are the globalization of markets, there has there been significant attention on flexibility (Günsel & Açikgöz, 2013). The ability of a business to be flexible means to adapt to environmental changes (Sanchez, 1993), to reach the optimum size and to respond continuously to unexpected changes (Kundi and Sharma, 2015). Flexibility can be stated that a business adaptation its own structure and resources to change, to increase its market share or to create new product and technology. Moreover it can be stated the ability of a business to re-change its internal resources (employees, machines, equipment, architectural structure, etc.) according to the demands and needs of the customers. If it were succeeded, the enterprises could maximize its profit.

Speed There is a strong relation between speed and responsiveness. As a matter of fact, some researchers (Sharp et al., 1999; Gunasekaran & Yusuf, 2002; Lin et al., 2006; Jain et al., 2008) stated that after having decided how to react to the changes, businesses should be able to apply these decisions quickly. It is the ability to complete an activity as quickly as possible (Zhang &Sharifi, 2000), in other words, is the ability to do an activity as soon as possible (Christopher, 2000) or the ability to respond quickly to changes in the environment of the business (Hoyt et al., 2007; Shahaei, 2008). Speed, which can be explained as the 1495

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ability of a business to deliver the product or service effectively and in short time, is quite important for a business in aspect of innovation abilities, improving a new knowledge against change.

Competence The competence dimension of organizational agility can be stated as the ability of using the other three abilities (Responsiveness, Flexibility, and Speed) of organizational agility. Because the ability of a business to carry out, an event is related to its ability to use its competence. Competence is the ability and capacity to renew existing or potential skills to adapt a business to changes in environment (Teece et al., 1997) or the ability to reach business goals effectively and efficiently (Sharifi& Zhang, 1999).

Studies about Multiple Leadership Styles and Organizational Agility There are some researches about organizational agility and leadership styles in literature. But most of them are related with leadership styles and agile production, job satisfaction, organizational success, performance or service quality (See Table 2). Table 2. Studies about multiple leadership styles and agility Researcher

Year

Topic

Result

Zhang &Sharifi

2000

A methodology for achieving agility in manufacturing organizations

A model has been identified and an organizational agility model has been designed to provide agility in the manufacturing sector.

Tetik

2008

The Role of transformational leader in managing change

The transformational leader has an important role in managing change.

Xu et al.

2008

The impact of transformational leadership style on organizational performance: The intermediary effects of leader-member exchange

The results show that the transformational leader increase organizational performance.

Hüseynov

2010

The role of organizational agility in Strategic management of human resources

Strategic management of human resources is important to create organizational agility.

Judkrue

2012

The influence of transformational leadership style on organizational success: A study on MNCs in Bangkok, Thailand

The results show that transformational leaders can increase employee performance in multinational companies.

Young

2013

Identifying the impact of leadership practices on organizational agility

The results show that leadership style increases organizational agility in the service sector .

Chou

2014

Does Transformational Leadership matter during Organizational Change?

The results show that transformational leaders affect employees’ behaviors supporting change directly.

Veiseh et al.

2014

A study on ranking the effects of transformational leadership style on organizational agility and mediating role of organizational creativity

The results show that transformational leaders have effect on organizational agility.

Karimi et al.

2016

The Effect of Transformational Leadership Style on Components of Organizational Agility in Isfahan University of Technology

The results show that transformational leadership style effect on organizational agility dimensions.

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As mentioned above, these researches are mostly about agile production, job satisfaction, organizational success, performance or service quality and especially about transformational leadership style, which is only a style of multiple leadership styles. However, there are limited studies discussing multiple leadership styles, consisting of transformational, transactional and laissez faire leadership related with organizational agility in literature, in other words the discussing the interaction between these two components.

TECHNOLOGY AND SCIENCE PARKS In Industry 4.0 terms, science parks (techno parks) have a critical role for organizational agility which is inevitable for them. These parks also create technology by itself, which is one of the most important components in industry 4.0. Technology can be defined as to create a product, to transform an existence product or service to another new one. So it can be said using and improving of technology has an aim. As Burgelman (1991) said that the enterprises create new marketing and technologies to introduce new services and products, to meet customers’ demands (p.240). Thus, by using technology to meet both customer’s needs and expectations and new market demands, the enterprises can transform their own resource and organizational structure (Brown & Eisenhardt, 1995:343). It may be said that the enterprises can create new markets by offering new products and services that meet customers’ demands and expectations. They can do it with some abilities such as to be innovative, adaption to change and respond to this change. Furthermore they can do it easier and faster with the technology. This is a dynamism which is more common in techno parks where technology is very intense. As known, the first technopark was founded in the prior of Stanford University in California. In Turkey, one of the countries which pay attention to technology, the first project for developing of technoparks started at the beginning of 1990s. By then, the number of technoparks increased in a short time and by May 2016 the number of technoparks increased to 69 and the number of companies conducting R&D in techno parks was about 4,510 (www.tgbd.org.tr, 18.03.2018). This rapid increasing in the number of companies shows that the technoparks are successful. Those companies contribute to both the region and the country’s economy. In addition, these companies operate in different sectors such as software sector, computer and communications, electronics, machinery and equipment, medical, energy, chemical, food, defense and automotive (See Table 3). Table 3. Sectorial distribution of firms operating in the technoparks in Turkey Sector of Activity

Average Numbers

Percentage (%)

Software Sector

1668

37

Computer and Communication

767

17

Electronic

361

8

Machinery and equipment,

271

6

Medical, Energy, Chemistry, Food, Defense, Automotive

1443

32

Total

4.510

100

Source: http://www.tgbd.org.tr (18.03.2018).

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The statistical information given above shows that the number of technoparks and the number of people working in these companies are rather high. In order to determinate the relationship between multiple leadership and organizational agility, 31 managers were interviewed in science parks in Dokuz Eylül University Technoparks by Akkaya (2018). Akkaya (2018) used MAXQDA qualitative data analysis program to analyze this relationship in his doctoral dissertation. According to the results of this dissertation most of those managers have transformational leadership behaviors. The strong relation is between transformational leadership and organizational agility. While transactional leadership has also slight relationship with organizational agility, laissez-faire leadership has no relation with it (Figure 1). He also used SPSS to analyze the correlation between dimensions of multiple leadership styles and organizational agility components (See Table 4). Those quantitative results also confirmed qualitative results seen in Figure 1. Multiple leadership styles and organizational agility relation

CONCLUSION In today’s world, technology companies, especially, in developed countries are working hard to become a company with organizational agility which is one of the production strategies in today’s manufacturing enterprises (Nagel & Bhargava, 1994; Nath et al., 2008; Sukati et al., 2012). Organizational agility is becoming an essential element in eliminating the environmental uncertainties of the supply chain, especially when supply chain management is critical (Sahin et al., 2017:338). The concept of organizational agility emerged after manual production, mass production and lean production, and has been seen by researchers as the top development of the thought of production management and is a critical approach to manufacturing operations (Hormozi, 2001). Today’s leaders launch strategic offensives to out-innovate and out-maneuver rivals and secure sustainable competitive advantage (Yozgat and Şahin, 2013). Managers and owners of today’s enterprises are aware of the need to be agile to meet customer needs and demands. Therefore, our research about “Organizational Agility”, which is an important topic in the literature, and a new leadership style in industry 4.0 “Agile Leadership” that can achieve this may provide significant contributions literature. In particular, it is important to determine the effective and agile leadership type that will provide organizational agility in technopark firms where change and technology are very fast. 1498

Transformational Leadership

Transactional Leadership

Laissez Faire Leader

,000

,000

,249* ,000

,300*

,000

,251* ,000

,409*

0,002

,000

,000

,180*

,000

,000

,298*

,259*

0,007

,334*

-,156*

-0,08

0,112

0,148

0,091 0,114

0,092

,000

,617*

,508*

,000

,000

,000

,325*

,460*

,523*

,366*

,537* ,000

,485*

,000

,499* ,000

,536*

1

Idealized InfluenceAttitude

,000

,000

,501*

1

Inspirational Motivation

,000

,310*

,000

,334*

,000

,260*

,000

,343*

0,076

-0,1

0,696

0,023

,000

,399*

,000

,427*

,000

,434*

,000

,448*

1

Idealized InfluenceBehavior

,000

,303*

,000

,285*

,000

,209*

,000

,322*

0,577

-0,03

0,012

,145*

,000

,327*

,000

,499*

,000

,545*

1

Individualized Consideration

,000

,294*

,000

,252*

,000

,229*

,000

,254*

0,006

-,159*

0,003

,173*

,000

,497*

,000

,507*

1

Intellectual Stimulation

,000

,269*

,000

,347*

,000

,206*

,000

,315*

0,016

-,138*

0,004

,166*

,000

,431*

1

Contingent Reward

*Correlation is significant at the 0.05 level (2-tailed). **Correlation is significant at the 0.01 level(2-tailed).

Organizational Agility

Speed

Responsiveness

Flexibility

Competence

Laissez Faire

ME-P

ME-A

Contingent Reward

Intellectual Stimulation

Individualized Consideration

Idealized InfluenceBehavior

Idealized InfluenceAttitude

Inspirational Motivation

Components

Table 4. Correlations between multiple leadership styles and organizational

,000

,239*

,000

,251*

0

,165*

,000

,253*

0,15

0,08

0

,404*

1

ME-A

,000

,243*

0,009

,151*

0,024

,130*

0,019

,135*

0

,328*

1

ME-P

0,327

-0,057

0,043

-,117*

0,395

-0,049

0,04

-,118*

1

Laissez Faire

,000

,558*

,000

,552*

,000

,536*

1

Competence

,000

,510*

,000

,486*

1

Flexibility

,000

,548*

1

Responsiveness

1

Speed

 Leadership 5.0 in Industry 4.0

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FUTURE RESEARCH Change has been described as one of the few variables in this life. With the Industry 4.0 digital era in enabling more rapid data collection and communication than ever before, the speed of leaders’ decision making and change has been accelerated. Attempts to manage and adapt these changes within a reactive organizational system may fail miserably, with autocratic leadership styles. Organizational agility which can be defined as a proactive organizational system, including all departments of an enterprise being agility, can be achieved by contemporary leadership styles such as transformational leadership and transactional leadership even agility leadership which may be the modern leadership style in future. It is held that contemporary leaders who develop past-future connections with their followers may be in a privileged position to navigate such changes. A type of evolved contemporary leadership, termed agile leadership, described precisely this quality. The present research sought to determine how organizational agility exists within a science park organization, how it is described, and the dimensions of o it, and what the relationship between multiple leadership styles and organizational agility. These objectives have been met and addressed in this research. Evidence of organizational agility was found in this study. As this is one of the first researches in which such empirical findings have been published, an invitation is extended to the scholarly community to explore the subject more in future. Arguably, a deeper research on 4.0 industry will be an inherently positive one, especially by using artificial intelligence. This study has presented the relationship between organizational agility and contemporary leadership, but a new construct of organizational agility leadership, and its’ presumed organizational impacts will be studied on. It also offers a further evolution in the way in which we observe business dynamics, that being from an agility perspective. In this context, another suggestion is therefore extended to the academy to more this exploration in service of agile leader development and organizational agility and well-being. Like other common companies, techno science companies, can be called as industry 4.0 firms, also need leaders who will direct the employees, adapt and motivate them to the fast changing environment. These kinds of leaders are to adapt to the competition by starting organizational change and innovation with their abilities. These leaders can be called as “agile leaders.” Agile leadership can be stated leadership 5.0. So who are agile leaders? And what do agile leaders do? Bittner (2018) partly answer this question answer of which is given in additional reading below.

REFERENCES Akkaya, B. (2018). Teknogirişim firmalarındaki yöneticilerin liderlik tiplerinin firmaların örgütsel çevikliğine etkisi: Teknopark firmaların üzerine bir araştırma (Unpublished doctoral dissertation). İzmir Katip Çelebi Üniversitesi, Sosyal Bilimler Enstitüsü, İzmir, Turkey. Avolio, B. J., & Bass, B. M. (2001). Developing potential across a full range of Leadership Tm: Cases on transactional and transformational leadership. Psychology Press. Bass, B. M. (1985). Leadership and performance beyond expectations. Collier Macmillan. Bass, B. M., & Avolio, B. J. (1994). Transformational leadership and organizational culture. International Journal of Public Administration, 17(3-4), 541–554. doi:10.1080/01900699408524907

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KEY TERMS AND DEFINITIONS Agile Leadership: It is a postmodern leadership style that provides organizational agility in technopark firms where change and technology are very fast and leadership style in Industry 5.0. Agility: It is the capability of a firm, particularly in production department, to adapt the changes in market environment. Competence: It is the ability of using the other three abilities (Responsiveness, Flexibility, and Speed) of organizational agility. Flexibility: Flexibility can be stated that a business adaptation its own structure and resources to change, to increase its market share, or to create new product and technology. Organizational Agility: It is about reaction and adaptation to changes which driven by customers, competitors and technology. Being agile organizationally, not only in enterprise’s production department but in other departments as well. Responsiveness: It is the ability of a firm to respond to technological and environmental changes and customer demands on the right time and right place. Speed: Speed, which can be explained as the ability of a business to deliver the product or service effectively and in short time, is quite important for a business in aspect of innovation abilities, improving a new knowledge against change Technopark: It is a kind of science park, where technology is very intense, that creates technology by itself and one of the most important components in Industry 4.0.

This research was previously published in Managing Operations Throughout Global Supply Chains; pages 136-158, copyright year 2019 by Business Science Reference (an imprint of IGI Global).

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APPENDIX Agile Leaders I’ve had this ongoing discussion with a few of my colleagues who says that the term “agile leader” is an oxymoron - that the ideal organization is a bunch of Scrum Teams and not much else. Even in an ideal world, I disagree, and here’s why in a nutshell: I’ve never seen, and have not even heard of, an organization that was successful in their pursuit of agility who did not have a strong leader guiding the vision for what the organization can become, motivating people to achieve that vision, nurturing the pursuit of that vision, and protecting, when necessary, the people who want that vision from the people who don’t. The reason for this is simple, and is as old as civilization. As Nicolo Machiavelli observed, “It must be considered that there is nothing more difficult to carry out nor more doubtful of success nor more dangerous to handle than to initiate a new order of things; for the reformer has enemies in all those who profit by the old order, and only lukewarm defenders in all those who would profit by the new order; this lukewarmness arising partly from the incredulity of mankind who does not truly believe in anything new until they actually have experience of it.” As intellectually compelling and self-evident we would like the advantages of agile to be, the truth is that there are people who benefit from the old system, and they didn’t get to where they are because they are not astute and influential. They are not simply going to resign their current advantages because someone proposes a superior system; you have to expect that they are going to fight to maintain the status quo.

What Agile Leaders Do? Agile Leaders focus on three things: (1) they create and nurture a culture in which experimentation and learning are embraced; (2) they collaborate with employees (at all levels in the organization) to find common values to create a greater goal for the company and the teams; and (3) they create an organizational structure that reinforces and rewards the other two dimensions.

Agile Leadership’s Focus Goals Providing guiding vision for shared goal setting is, in my opinion, the most important focus area, and the one that survives even after the organization and culture are largely self-managing and self-sustaining. The goals they inspire others to contribute to and make their own are strategic, and are generally customer or market focused. By strategic, I mean bold and audacious; both aspirational and inspirational. To providing contrasting comparison, think about the motivational difference between the goal of landing on the moon and returning, by the end of the decade with a more prosaic goal of improving profitability by 25%; no one is going to tell their grandchildren that they helped improve investor returns. These motivational goals that leaders help us to identify have some common characteristics: the are motivating and inspiring, but they also are uncertain; they force us to stretch, to do things we have never done before. That’s why agility is so important; if we knew how to reach those goals, we should

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just develop a plan and march to it. Leaders help their employees to persist in the pursuit of their shared goals and values when times get rough and old behaviors want to take over.

Organization The basic agile team structure is very simple; if we use Scrum as an example, there are only three roles: the Product Owner, the Development Team, and the Scrum Master. But not everything an organization does is done by an agile team; agility is needed whenever we are dealing with complexity, but not everything is complex. If you were running a company that makes paint, research and development would need agility, but the paint factory itself might be better suited to using lean processes; unless you’re doing a lot of small-batch custom manufacturing, a predictive continuous flow process is probably better than planning production as a series of Sprints. The point is that, unless you are a small software start-up, there will always be things outside the scope of what agile teams do, even if they are only as mundane as payroll, accounting, tax compliance, legal, and investor relations. The role of management is to design, monitor, and correct this system to make sure that the organization achieves its goals. Even product development companies need to do more than simply developing the product. Where agile leadership comes into play in the management context is that they need to make sure that the different parts of the organization, with different operating models, don’t destroy each other. Put more positively, agile leaders need to help the organization optimize for flexibility and continuous improvement, making sure that improving customer outcomes always comes first, and that the other parts of the organization support this mission. But the other things need to get done, too. Agile leaders also help teams progress in their maturity. Agility is not binary, and there are predictable stages that teams go through as they improve their ability to learn and improve. Leaders create a supportive environment in which teams can progress, they provide coaches and exposure to peers who can help the teams learn, and they commit themselves to improving their own abilities in parallel.

Culture The most important thing agile leaders do is to foster a culture that supports empiricism and learning, and that is constantly seeking better customer outcomes and better ways of achieving those outcomes. The challenge for leaders is that they can’t dictate the culture; they can only create the right conditions for it to emerge. Some of my colleagues like to use a “gardener” metaphor: if you’ve ever had a garden, you know that you can’t make anything grow. You can create the right conditions with the right amount of water (but not too much), and enough sun (but not too much). You can remove other plants that might compete with the ones you want to grow, and you can protect the plants from predation. You can’t control all factors, however, and an organization’s culture emerges only partly as an expression of its leaders’ aspirations; most of it comes from the people in the organization, how they treat each other and work together. Culture is the non-copyable je ne sais quoi that makes your organization unique. But while leaders can’t control and dictate this culture, they can encourage it and cause it to flourish by the examples they set and the behaviors that they model.

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Agile Leaders Are the Key to Scaling Agility Agile leaders play an important, even essential, role in scaling agility in an organization. While agile teams can fly under the radar so long as their scope remains small, the larger the scope and scale of agility, the more agile teams need supportive leaders to help them to frame the right goals, to make the organization work in support of agility and not against it, and to evolve the culture to embrace and reward learning, rather than merely tolerating it. What organizations who are struggling to scale their agility are most often missing is strong, supportive agile leadership that helps them to build strong, cohesive agile teams. Agile leadership and high-performing teams work in a kind of feedback loop: weakness in one weakens all, while strong leadership reinforces and strengthens strong teams, and vice versa.

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Chapter 75

Insights Into Managing Project Teams for Industry 4.0 Carl Marnewick University of Johannesburg, South Africa Annlizé Marnewick University of Johannesburg, South Africa

ABSTRACT In a fast-paced and changing world demanded by Industry 4.0, the continuous delivery of products and level of integration of technologies are required. This is achieved through the introduction of agile but agile itself demands changes in the way projects are managed. The role of the project manager itself is changing from a command and control to a collaborative and coaching style of leadership. Project teams on the other hand should be self-organizing and self-directed to be agile. Managing agile teams requires a different approach as the idea is to deliver workable solutions and products at a faster space. New project manager skills and competencies are required as well as ways to manage agile teams. A conceptual model is introduced, highlighting the required enablers for an agile environment. The enablers have an impact on how the agile project manager interacts with the agile team. The end result is that products are faster deployed enabling organizations to react to the changes demanded by Industry 4.0.

INTRODUCTION Projects and project management have a long history. Some of the major projects undertaken by humankind delivered the Great Pyramid of Giza (2550 – 2530 BCE), the Colosseum (70 – 80), the Cathedral at Hagia Sophia (532 – 537), the Taj Mahal (1631 – 1648) and the Empire State Building (1929 – 1931) (Kozak-Holland & Procter, 2014). These civil engineering projects evolved into various other types of projects including space exploration projects and information system projects, for instance the implementation of an ERP system (Sudhaman & Thangavel, 2015).

DOI: 10.4018/978-1-7998-8548-1.ch075

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 Insights Into Managing Project Teams for Industry 4.0

The history of formal project management does not stretch that far back, and project management was introduced in the late 1950s between the second and third Industrial Revolutions (Seymour & Hussein, 2014). A formal way was needed to manage projects and the notion of project management was born. The introduction of project management as a discipline resulted in new opportunities for researchers as well as organisations: project management standards and best practices were introduced, roles and competencies of the project manager and team members were documented and ways were created for determining the success of projects (Söderlund & Lenfle, 2013). The skills and competencies of project managers and those of project team members have also been researched and various research articles as well as competence models have been published. Some of the well-known competence models include the Project Management Institute’s Project Manager Competency Development Framework (PMCDF) (Project Management Institute, 2017c) and the International Project Management Association’s Individual Competence Baseline for Project, Programme & Portfolio Management (ICB) (International Project Management Association, 2015). The PMCDF focuses on personal and performance competences. The ICB, on the other hand, focuses on three competence areas, i.e. perspective, people and practice (Marnewick, Erasmus, & Joseph, 2016). Industry 4.0 have a disruptive impact on various industries such as manufacturing and logistics (Santos, Mehrsai, Barros, Araújo, & Ares, 2017). These disruptions are caused by various advances in technology such as the Internet of Things (IoT), wearable technology, artificial intelligence and machine learning. Traditionally, these new technologies or systems would have been implemented through projects and project management. Just as Industry 4.0 has a disruptive impact on other industries, it is evident that Industry 4.0 has a disruptive influence on project management and project teams in particular (Weber, Butschan, & Heidenreich, 2017). The disruption focuses on the way and manner that projects are implemented and team members interact with each other as well as new skills and competencies needed to manoeuvre through the 4th Industrial Revolution. Current project management practices and standards do not cater for or meet the needs of Industry 4.0. Agile project teams have already resulted in some challenges in the working environment. Some of these challenges are the co-location of teams and the cross-skilling of individual team members (Svejvig & Andersen, 2015). Industry 4.0 challenges the project management discipline in various ways. Some of these challenges are the dynamics of the project management discipline itself in Industry 4.0, the organisational structure in project-oriented organisations, the effects on the project managers’ responsibilities and roles, Industry 4.0 tools and approaches used in project management as well as agile project planning (Project Management Institute, 2018b). The problem that project management faces currently as a discipline is that there has been no development in best practices since 1994 (Svejvig & Andersen, 2015). Svejvig and Andersen (2015) state that project management as a discipline needs to include the following categories to be relevant during the 4th Industrial Revolution: 1. Contextualisation: Projects are not run in isolation, and with the advent of cloud computing and the Internet of Things (IoT), it will become important for each project to be assessed based on the context within which it is implemented. Concepts such as sustainability and the organisational strategy should be considered when the context is determined. 2. Social and political aspects: Projects are implemented within a certain context and this context is influenced by social and political aspects, which can have either a positive or a negative influ-

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3. 4.

5.

6.

ence on projects. An example might be the political influences of stakeholders and the emotional intelligence of the project manager and the project team members. Rethinking practice: Current best practices and standards have not changed dramatically since their introduction a couple of decades ago and new or alternative best practices and standards need to be introduced, such as agile and scaling agile. Complexity and uncertainty: Marnewick, Erasmus, and Joseph (2017) have identified 75 features that contribute to a project’s complexity and project managers need to wade their way through these complexities. This is only possible if they understand how to manage complexity. Complexity and uncertainty are even more prevalent in the 4th Industrial Revolution but a counteraction is the application of agile principles to reduce complexity and uncertainty. The actuality of projects: There is a need to understand the difference between practice and theory to determine how projects are actually implemented. Marnewick et al. (2016) suggest that there is a discrepancy between practice and theory. In the case of information technology (IT) projects, IT project managers do not necessarily apply all the principles of theory to practice, resulting in project failure. Broader conceptualisation: Alternative perspectives on projects and project management need to be offered. An example is the introduction of agile as a way to implement software versus the traditional waterfall method.

The next section deals with the role of the project manager from a historical perspective, focusing on the competencies that a project manager had to master. New competencies and skills that project managers should exhibit in an agile environment are then analysed. These new competencies and skills are very different from current traditional competencies.

ROLE OF THE PROJECT MANAGER The project manager is assigned by the organisation to lead and manage the project team in order to realise the strategies (Project Management Institute, 2017b). A good project manager needs various skills to lead and manage the project team. The most important skills are (i) a sound knowledge of the body of knowledge, (ii) the application of knowledge, national and international standards as well as regulations, (iii) knowledge of the project environment, (iv) general management skills and (v) soft skills (Schwalbe, 2016). However, Bredillet, Tywoniak, and Dwivedula (2015) caution that we still need to delineate what constitutes a good project manager and the level of performance expected of the project manager. This is even more applicable in an agile environment where the role of the project manager is changing dramatically. According to Bredillet et al. (2015), a project manager should act wisely and perform the right acts within the context of the project. Hodgson and Paton (2016) state that the focus of the traditional project manager is to plan, monitor and control projects. This is all done within the framework of proprietary bodies of knowledge. The concern is the difficulty in applying standard bodies of knowledge to a changing environment dictated by the 4th Industrial Revolution (Hodgson & Paton, 2016). The current project management bodies of knowledge from which project managers draw their knowledge and technical expertise are not fit for purpose and project managers find it difficult to perform in a constantly changing environment. Project managers should be able to deal with soft issues relating to team members and relationships (Loufrani1510

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Fedida & Missonier, 2015). This is especially important in an agile environment where the focus is on coaching and servant leadership. The project manager should also portray additional competencies apart from those highlighted in the competency standards. Loufrani-Fedida and Missonier (2015) are of the opinion that agile project managers should also portray servant leadership, the ability to communicate at multiple levels, have verbal and written skills as well as the ability to deal with ambiguity and change. Current project management competency standards focus on traditional competencies and do not consider the competencies needed for the 4th Industrial Revolution.

Traditional Competencies Competence within the project management environment is the application of knowledge, skills and techniques in order to complete an activity successfully (Marnewick et al., 2016; Mnkandla & Marnewick, 2011). There are various project management competency standards that provide guidance on the types and level of competencies a project manager must possess and have mastered. The Project Management Institute (2017c) identifies three major competencies: 1. Technical project management skills: the skills that a project manager uses to apply his/her project management knowledge effectively. 2. Leadership: the ability to guide, motivate and direct the team. Within a scaled agile environment, traditional leadership is replaced by servant leadership. This is discussed in more detail later in this chapter. 3. Strategic and business management: the ability to see the overall view of the organisation and effectively negotiate and implement decisions and actions that support strategic alignment. The Global Alliance for Project Performance Standards (2007) has a slightly different view of the competencies of a project manager, as the focus is more on the technical aspects of managing a project. According to GAPPS, six competencies need to be mastered: 1. 2. 3. 4. 5. 6.

Managing the stakeholders and subsequent relationships Developing the project schedule Managing the project’s progress on an ongoing basis Managing the acceptance of the final product or service Managing the transition of the final product or service into operational management Evaluating the project’s performance and improving areas as highlighted through a lessons-learned exercise.

The International Project Management Association (IPMA) clusters project management competencies into three competence areas (International Project Management Association, 2015; Marnewick et al., 2016): 1. The people competence area defines the personal and interpersonal competencies required to deliver projects successfully.

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2. The practice competence area defines the technical aspects that a project manager should master to manage projects successfully. 3. The perspective competence area defines the contextual competences that must be navigated within and across the broader project environment. The focus of the PMBOK® Guide and the ICB is mainly on attribute-based competencies, whereas the GAPPS standards focus on performance-based competencies (Bredillet et al., 2015). The project management competencies as preached by the competency standards are not appropriate to the 4th Industrial Revolution and an agile environment. These competencies are very prescriptive and do not allow for the project manager to coach and lead. Project performance can only be improved when the individual competencies are combined into a common endeavour (Loufrani-Fedida & Missonier, 2015). This is achieved through joint problem solving as introduced by agile and an understanding of the bigger picture through systems thinking. Just as the project manager’s competencies need to change, the role of the project manager also needs to change to address the demands of the 4th Industrial Revolution. The role of the project manager is currently changing and will continuously change in an agile environment as dictated by the 4th Industrial Revolution.

Changing Role of the Project Manager According to the Project Management Institute (2017a), the role of the project manager within an agile environment is becoming a misnomer. This is due to the fact that the project manager’s role is not defined in any of the various agile frameworks. Dikert, Paasivaara, and Lassenius (2016) mention that mixing the role of the project manager and scrum master has created a conflict of interest whereas the role of the scrum master has become one of coaching and not one of policing. In an agile environment, the focus of the project manager shifts from command and control to serving and managing the team (Project Management Institute, 2017a). The project manager’s skills in an agile environment include servant-leader, coach, collaborator and stakeholder manager. Various new roles have emerged from within an agile environment that fulfil the traditional role of the project manager: •

•

•

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The product owner guides the direction of the product and is ultimately responsible for the delivery of the product (Project Management Institute, 2017a). The product owner prioritises the work based on business value, defines the stories and prioritises the backlog based on the business value while maintaining the conceptual and technical integrity of the features (Scaled Agile Inc., 2018). The scrum master is a servant-leader and performs the role of a coach. The main responsibility is to create a conducive environment, through the removal of obstacles, for team dynamics, continuous flow and improvement. The time of the scrum master is spent assisting other team members in communicating and collaborating (Bass, 2014; Scaled Agile Inc., 2018). The release train engineer (RTE) facilitates the events and processes associated with an Agile Release Train. The RTE also assists agile teams in delivering value. RTEs are the centre point of all communication with stakeholders, they escalate issues and concerns, manage risk and drive improvement (Scaled Agile Inc., 2018).

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The project manager traditionally performed these three roles (product owner, scrum master and RTE). In an agile environment, the agile project manager needs to adopt new methods as depicted in table 1. In addition, agile project managers also need to have skills to manoeuvre in an Industry 4.0 environment. Table 1. Comparison of agile and traditional methods Traditional

Agile

Industry 4.0

Command and control

Leadership and collaboration

Systems thinking

Autonomous

Cooperative

Business analytics expertise

Disciplined

Flexible

Maneuverable

Manager as planner

Manager as facilitator

Manager as change agent

Explicit knowledge

Tacit knowledge

Artificial intelligence

Individual reward system

Team reward system

Organisational reward system

(Bishop, Rowland, & Noteboom, 2017, 2018)

Table 1 illustrates that project managers need to focus on two main aspects. The first aspect is how to manage teams in an agile environment. This is different from the traditional way of managing project teams. The second aspect is that while teams needs to be managed differently, project managers should master new skills as demanded by Industry 4.0. The combination of these two aspects challenges the project manager to upskill and sometimes re-skill. Project managers also need to move away from the more traditional way of managing projects. Embracing the new way of managing projects requires them to acquire new skills and knowledge. New skills focus on the technical aspects of agile project management, such as features, sprint planning backlogs and sprint reviews. New knowledge that project managers should master includes how to manage agile team members. The focus should be on leadership and collaboration instead of command and control. The agile project manager places greater emphasis on servant leadership that empowers, encourages and supports the team. The notion of command and control has no place in an agile environment (Bishop et al., 2018). The biggest challenge for the project manager is to strike a balance between the individual team members’ autonomy within self-organising teams and the organisational strategies. The focus of the team is on flexibility, value creation and incremental delivery (Craddock, Roberts, Godwin, Tudor, & Richards, 2014). Managing projects during the 4th Industrial Revolution is getting more complex every year. Project managers need to adapt to this complexity as well as respond rapidly to the constantly changing economic, social and technical situations imposed by the 4th Industrial Revolution (Ramazani & Jergeas, 2015). Agile project managers should be adaptable, be critical thinkers and have multidisciplinary as well as collaborative skills (Ramazani & Jergeas, 2015). Different types of projects should be approached differently (Andersen, 2016) and this is the case with agile projects as well. Irrespective of whether a project is adopting an agile approach, value should be created through the final product and/or service (Andersen, 2016). There is a definite shift from the more traditional waterfall project management towards agile project management (Levitt, 2011). The composition of project teams are also changing in this new environment dictated by agile and the 4th Industrial Revolution.

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PROJECT TEAM COMPOSITION In an environment that is constantly changing as demanded by Industry 4.0, agile as a mindset is a perfect solution for the management of projects because the underlying principles of agile focus on change and therefore agile is perceived as a change agent. This constantly changing environment requires constant and frequent interactions with the customer or user as well as quick delivery and deployment of the product or service (Bishop et al., 2017). The focus is shifting from prediction and control to adaptation and innovation (Vinekar, Slinkman, & Nerur, 2006). This is a new mindset and challenges current project management standards and best practices. Current standards and best practices need to be revamped to reflect the new development methodologies (Hoda, Noble, & Marshall, 2008; Project Management Institute, 2017a). Within the agile environment, the focus is on the team and how the respective team members interact with each other. Part of this interaction among team members is to create self-organising teams (Vinekar et al., 2006). Hoda et al. (2008) are of the opinion that the agile project manager should be a visionary. The management style in an agile environment creates an environment that is conducive for creative thinking and problem solving. Some elements of an agile project stay the same, for example the management of customer relations and risk management, but the execution of these elements is different within the agile framework and requires a different way of thinking from the agile project manager and the team members (Hoda et al., 2008). Irrespective of whether the project is executed in an agile way or a more traditional way, the success of the project is anchored around the project team. The agile team should be in a position to “perform together toward a common goal, which results in the creation of a collective outcome, an outcome that could not be accomplished by one member due to its complexity” (Ruuska & Teigland, 2009, p. 324).

Managing Agile Teams Agile teams need to be co-located as team members are required to embrace change and rapidly changing requirements. Agile team members are required to have a high level of commitment to the team as they perform various roles and responsibilities within the team (Fernandez & Fernandez, 2008). The following challenges have been identified with regard to agile teams: 1. The vision and strategies of the organisation are not easily translated back to agile teams as they are constantly addressing and resolving issues (Augustine, Payne, Sencindiver, & Woodcock, 2005). In a scaled agile environment, the work of agile teams can be related back to epics (Scaled Agile Inc., 2018). 2. Team members are dealing with additional stress as they need to incorporate management skills into the self-organising team instead of just focusing on their professional contribution to the problem (Augustine et al., 2005). 3. Self-organising teams are highly motivated and self-driven. Team members that are not exhibiting these qualities add additional stress to the team and the agile project manager (Bishop et al., 2017; Stone, Russell, & Patterson, 2004). 4. There are no hierarchies with agile teams and this creates conflict as some individuals still believe that there should be some seniority with the teams (Taylor, 2016; Vinekar et al., 2006).

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The value of agile teams is that they are empowered to decide on their own what the best way is to perform an activity and this decision is based on the competence level of each individual team member. Agile teams also decide on the order in which activities are to be implemented. Table 2 provides a comparison between traditional and agile teams. Table 2. Agile team versus traditional team attributes Agile team

Traditional team

Self-organised

Leader organised

Self-directed

Leader directed

Cross-functional

Functional

All team members are accountable

Project manager is accountable

Servant leadership

Direct and regulated leadership

Small team

Large teams

Multi-skilled team members

Single-skilled team members

Generalising specialists

Specialists

Make their own decisions

Leader makes decisions for the team

Focus on group success

Focus on individual success

(Canty, 2015; Project Management Institute, 2017a)

Various new attributes have an impact on the way that agile team members engage with each other to perform at an optimal level. The first two attributes in the table above speak to the essence of an agile team. Agile team members choose among themselves who will perform what activities and when they will perform them. This speaks to the flow of work within the project. The teams also decide which activities they will perform. All of this is done with the sole purpose of delivering a minimum viable product sooner rather than later. Another important attribute is that team members are generalising specialists. This implies that although they are specialists in their own right, they are able to perform other team members’ duties when the opportunity arises. It is evident from Table 2 that agile teams are operating at a different level than traditional teams. This places additional stress on team members to get used to operating in this environment and take ownership of their own destiny. Organisations need to ensure that an environment is created where agile teams have the space and freedom to embrace the attributes of an agile team. This shift in focus in the way that agile teams are managed also has an impact on the traditional role of the project manager. Project managers also need to learn and acquire new skill sets to manage agile teams. This is even truer for the 4th Industrial Revolution. Agile project managers should also be able to deal with the following managerial challenges (Schneider, 2018): 1. Analyse and Strategise: The agile project manager should analyse the environment, determine how Industry 4.0 will benefit the project and have a strategic plan for structuring the incorporation of technology. A second aspect is how the transition from a traditional project manager to an agile project manager will be facilitated.

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2. Cooperate and Network: The IoT opens possibilities for cooperation and networking that were not possible before. The agile project manager should have an awareness of possibilities and potential risks and these will have an impact on the management of the agile team and project itself. 3. Business Models: Current business models are not necessarily designed for Industry 4.0 and this is the case for project management as well. The project management model should be adapted as highlighted by Svejvig and Andersen (2015). Agile project management might be a possible alternative business model. 4. Human Resources: Agile project managers must understand and manage the impact of Industry 4.0 on task depth and content. The workplace of the future is also going to change with regard to competencies, skills, education and training. 5. Change and Leadership: Agile project managers should create an environment where team members are able to experiment, fail and rapidly recover from their failures. The next section focuses on enablers that bring about change in the organisation itself. Change is necessary as the 4th Industrial Revolution demands rapid change and agility from organisations. Organisations that are not able to change will not be competitive and will lose market share to their competitors.

ENABLERS The use of agile is becoming popular in a world demanding support for constant change and innovation (Bishop et al., 2018). The adoption of agile is driven by several influential factors such as project size, complexity, employee skillset, organisational culture and Industry 4.0 (Bishop et al., 2018). Transitioning from a more traditional way of implementing projects to an agile environment where projects are delivered in an iterative way does not happen overnight. The journey takes some time but before organisations can embark on this journey, certain enablers must be in place to ensure the success of this transition. The following enablers have been identified from literature: culture, servant leadership, technology and scaled agile (Cho, 2009; da Silva, Amaral, Matsubara, & Graciano, 2015; Iivari & Iivari, 2011; Laanti, 2014; Project Management Institute, 2018b; Turetken, Stojanov, & Trienekens, 2017; Winston & Fields, 2015).

Culture Culture is an important factor to understand organisational behaviour as it matters in projects, especially in an agile environment (Ramos, Mota, & Corrêa, 2016). According to Bishop et al. (2018), the organisational culture and management style have an influence on the way agile is adopted. If the organisation is not willing and able to embrace the changes that agile brings, then the adoption of agile is doomed (Bishop et al., 2018). Adopting agile as a new way of working requires various changes to how work is conducted. An important change is the focus on customer needs and a degree of adaptability to the continuous changes to the requirements. Another cultural change is the introduction of self-regulating teams that are collaborative and transparent. Teams meet frequently for short periods of time in order to achieve transparency, ensuring that many eyes are on each problem encountered. To achieve effective teamwork in an agile world, the notion of a leader needs to be transformed. Leadership in an agile world is much more likely to take the form of facilitation and coaching. Rather than leadership positions, individuals take on roles that could change from project to project. 1516

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Servant Leadership Within the agile environment, project managers adopt a servant leadership style. The project manager focuses on facilitating team members’ performance and development, acts in the best interest of the team members and does not engage in manipulative, self-interested actions (Winston & Fields, 2015). The project manager thus becomes the steward of both the project and the team members’ interests. Although the project manager is now a servant-leader, this does not imply that they can abdicate their responsibilities and accountabilities. The emphasis is on encouraging the development of autonomy and responsibility of the team members (Winston & Fields, 2015). A project manager can only be a servant-leader if there are high levels of trust among the team members and the project manager (Sendjaya & Pekerti, 2010). A servant-leader project manager should exhibit the following characteristics: (i) voluntary subordination, (ii) authentic self, (iii) covenantal relationship, (iv) responsible morality, (v) transcendental spirituality and (vi) transforming influence (Sendjaya & Pekerti, 2010). Table 3 highlights the attributes of a servant-leader. It is evident that the project manager’s focus shifts towards the well-being of the team and that of the individual team members. The emphasis is on achieving the project’s objectives through the team by creating a supportive environment that allows the team members to be creative and exhibit their strengths. Table 3. Servant-leader attributes Functional attributes

Accompanying attributes

Vision

Communication

Honesty and integrity

Credibility

Trust

Competence

Service

Stewardship

Modelling

Visibility

Pioneering Appreciation of others Empowerment

Influence Persuasion Listening Encouragement Teaching Delegation

(Russell & Stone, 2002; Stone et al., 2004)

Technology Technology has dramatically reshaped all forms of work over the last decade. The 4th Industrial Revolution came about as a direct result of the significant technological developments in ICT, cyber-physical systems and the IoT (Li, 2017). The focus of this revolution is on the integration of various technologies that enable ecosystems to function in an intelligent and autonomous way, decentralising factories and integrating product-services (Santos, Mehrsai, Barros, Araújo & Ares, 2017). Table 4 shows the top technology trends over the last decade and it is interesting to note that these trends are fluctuating.

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Table 4. Top 10 strategic technology trends 2008 – 2018 #

2008

2012

2013

2015

2018

1

Social Software

Media Tablets and Beyond

Mobile Device Battles

Computing Everywhere

AI Foundation

2

Metadata Management

Mobile-Centric Applications and Interfaces

Mobile Apps

Internet of Things

Intelligent Apps and Analytics

3

Web Mashup, Cloud Computing and Composite Applications

Contextual and Social User Experience

Personal Cloud

3D Printing

Intelligent Things

4

Web Platform and Weboriented Architecture (WOA)

Internet of Things

Enterprise App Stores

Advanced, Pervasive and Invisible Analytics

Digital Twin

5

Fabric Computing

App Stores and Marketplaces

Internet of Things

Context-Rich Systems

Cloud to the Edge

6

Real World Web

Next-Generation Analytics

Hybrid IT & Cloud Computing

Smart Machines

Conversational Platforms

7

Business Process Modeling

Big Data

Strategic Big Data

Cloud/Client Computing

Immersive Experience

8

Green IT

In-Memory Computing

Actionable Analytics

Software-Defined Applications and Infrastructure

Blockchain

9

Unified Communications

Extreme Low-Energy Servers

In-Memory Computing

Web-Scale IT

Event Driven

10

Virtualization

Cloud Computing

Integrated Ecosystems

Risk-Based Security and Self-Protection

Continuous Adaptive Risk and Trust

(Gartner, 2017)

Organisations that are mature in their digital strategy and adoption have made these disruptive technologies a priority (Project Management Institute, 2018a). The role of the project manager should also be that of a technology advocate, to motivate teams to implement disruptive technologies and become an authority on these technologies. Three technologies have a direct and positive impact on agile project management (Project Management Institute, 2018b): 1. Cloud computing offers new levels of collaboration and information access and frees up schedules so professionals can lend expertise to projects and customer issues. 2. The IoT offers increased and constant connectivity for the entire agile team. The automatic transfer of data will have a positive impact on the effectiveness of communication. The IoT also increases data efficiency that allows for accurate data-driven decision-making. 3. Artificial Intelligence reduces human error and biases when it comes to creating budgets, predicting cost overruns and developing schedules. AI-assisted tools could mean that project monitoring and schedule changes require less time and fewer resources.

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Organisations that adopt disruptive technologies experience increase in productivity, development of better products and services, automation of mundane tasks and stronger connections between team members. Disruptive technologies need to be incorporated into agile projects consistently. The challenge with incorporating these new trends is twofold. Firstly, agile teams do not necessarily have the knowledge to implement these trends and secondly, these trends contribute to the complexity of agile projects.

Scaled Agile Agile per se will not be able to deliver the intended benefits that organisations wish for. To gain the benefits through the adoption of agile, agile needs to be scaled. Scaling agile in large organisations needs to focus on enterprise architecture, interteam coordination, portfolio management and scaling agile itself (Laanti & Kangas, 2015). The benefits of scaling agile can be summarised as an increase in more motivated employees, faster time-to-market, increase in productivity and a reduction in defects (Dikert et al., 2016). Through the scaling of agile, the number of deliveries is improved. This improvement in productivity ensures that projects are delivered quicker and the user or customer uses the product or service as an outcome of the project quicker. This quicker delivery is only possible through better resource utilisation and flow. Traditional project management focuses on optimal resource utilisation. Technical staff are organised into pools, for example architects, developers, testers, project managers and business analysts, and these pools are the source of available resources. Various problems or issues are associated with this approach. A new philosophy needs to be instilled to implement scaled agile. Utilisation of 100% of the resources limits the flow of activity. Organisations need to focus on maximising the flow of applications and services and therefore on flow and not resource utilisation. To achieve this, organisations need to establish a fixed capacity model and then prioritise tasks accordingly, so that they address a rate that matches the fixed capacity. An advantage of the fixed capacity model is that the release of products or services occurs at a more predictable rate. The fixed capacity model also allows organisational strategies to drive the overall pattern of activity. The next section focuses on a conceptual model that can be utilised by organisations and project managers. This conceptual model allows organisations and project managers to position the project management discipline in such a way as to deliver continuous solutions.

CONCEPTUAL MODEL For organisations to become agile, agile technical practices as well as an agile mindset are required (West, 2017). The technical practices ensure consistency among teams and the agile mindset focuses on the customer, collaboration, openness to change, a willingness to fail and transparency (West, 2017). Organisations operating within the 4th Industrial Revolution should realise that products and services will become increasingly digital. The result is that organisations will have to operate in a more dynamic environment “driven by regional and global economics, regulatory mandates and increasingly demanding, technology-savvy customers” (Norton & West, 2017, p. 3). Adopting agile focuses on two levels:

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• •

Breadth by scaling agile Depth by applying agile principles end to end from strategy to delivery

The conceptual model depicted in Figure 1 highlights the enablers and interaction between the agile project manager, the agile team and the individual agile team member. Figure 1. Industry 4.0 and Agile Project Management Conceptual Model

The 4th Industrial Revolution demands that organisations deliver products and services quicker, smarter and better. Four major enablers need to be present before projects can be executed in an agile way. The four enablers are inter-related with each other and cannot create the necessary change individually. Cultural change is needed to facilitate the organisation and project teams for the transition into Industry 4.0. Culture also has an impact on the way that project managers manage their respective teams. The command and control style of leadership is not conducive anymore and a servant leadership approach is required. This style has its benefits as illustrated in Table 2 and Table 3. Industry 4.0 requires constant organisational change to incorporate new technologies as well as the changes introduced by technology, Agile as a change agent, is the perfect solution to deliver these changes as quick as possible, Agile, therefore delivers projects quicker and more reliable but the benefits of agile can only be harvested in a scaled agile environment. Scaling agile is only possible if the organisational culture allows it and when the project managers have adopted a servant leadership style. The first enabler is the culture of the organisation. Agile is as much about culture and core values as it is about principles and practices (Norton & West, 2017). Management from business and IT should send a consistent message around the adoption of agile and they should actively promote it. The move to agile and adopting an agile culture takes time and comes with considerable stress to both the organisation and the employees (Norton & West, 2017). Management should create an environment that encourages change. The second enabler is the adoption of a servant-leader leadership style by the agile project manager. This allows the agile project manager to lead and motivate rather than command and control. This change in leadership style is only possible when the culture of the organisation allows the traditional project manager to transform into an agile project manager that applies a servant leadership style. 1520

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Technology is the foundation of the 4th Industrial Revolution, especially the IoT and artificial intelligence. Agile project managers and teams should know how to embrace this technology and how it can make the management of projects easier. The adoption of technology is a two-way street. Technology can be used to enable the agile team to manage and deliver the project in a smarter way. An example would be to use artificial intelligence for the generation of burn-down charts. Technology can also be used to deliver the product and service and here an example might be to provide the solution via the latest technology such as smart watches. The Project Management Institute (2018a) has determined the impact that disruptive technologies have on the role of the agile project manager. Some of these impacts are: • • • • •

More expertise and experience are required in agile approaches. This is obvious as the 4th Industrial Revolution requires quicker deployments that are facilitated by agile. More business analytics expertise is needed. Subject matter expertise is also required. This is especially the case where the project manager needs to understand the impact of technology on the project, Project managers’ core skill sets must be modified. As seen earlier in this chapter, agile project managers cannot continue to rely on old competencies and skills. They have to learn new competencies and skills that are dictated by technology. Current project management standards and best practices do not incorporate disruptive technology and therefore agile project managers will not know how to deal with it. This requires agile project managers to acquire knowledge beyond the current project management certifications.

Organisations cannot and will not be able to reap the benefits of agile if agile is not scaled within the organisation. Various frameworks can be used to scale agile within an organisation with SAFe® (Scaling Agile Framework) as the most popular scaled framework. Scaling agile implies the adoption of Lean-Agile at a portfolio, large-solutions, programme as well as team level. VersionOne Inc. (2018) mentions five enablers for scaling agile: (i) internal agile coaches, (ii) consistent agile practices across all project teams, (iii) the implementation of common tools such as JIRA for reporting, (iv) external agile consultants and (v) executive sponsorship. Scaling agile does not come without its challenges such as an organisational culture that does not support agile or the lack of skills and experience in agile. The enablers create an environment conducive for the delivery of agile projects. This environment allows agile project managers and teams to ultimately deliver agile projects. Agile project managers, using servant leadership, lead the team to deliver the product or service. The team members portray the attributes as presented in Table 2. Agile project managers as well as agile team members must transform their traditional project management habits and practices into agile habits and practices. Both entities must make this transformation, as a non-agile project manager cannot lead an agile team and vice versa. The result is a product or service that is delivered faster. Delivering products or services faster than competitors and maintaining the leading edge is a prerequisite for organisations to survive and thrive in the 4th Industrial Revolution. A fully integrated and scaled agile approach allows for small continuous changes and manages the additional complexity of self-organised teams. One of the agile principles is to collaborate extensively with customers to produce a product or service that actually meets the requirements stipulated by the users. This is only possible when everyone in the organisation has an agile mindset. This is also applicable to the users themselves. They should also embrace an agile mindset and be involved from the start to the end of the project.

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SOLUTIONS AND RECOMMENDATIONS Traditional project management has a role to play when new product development and customisations are not involved. The moment organisations engage in new product development or customisations, an agile approach is required. Traditional IT projects such as infrastructure upgrades can be done without an agile approach but there are lot of lessons that can be learned from an agile approach. It is therefore recommended that organisations embrace agile as the preferred choice of implementing projects, irrespective of the type and nature of the project. Changing from a traditional project manager to an agile project manager does not happen overnight. To become an agile project manager, a conscientious effort must be made to adopt a servant leadership style. This will allow the agile project manager to focus on the management attributes as per Table 2. Project managers who cannot make this change on their own will face problems and challenges in managing agile project teams, as the management style will be in direct conflict with that of the team and the organisation at large. The agile project manager should also realise that different competencies are needed in a world dictated by the 4th Industrial Revolution. The competencies will be more of a coaching and leading nature. Organisations should have interventions in place to train and reskill agile project managers in these new competencies. Reskilling cannot be left to the agile project manager to resolve. Team members should also realise that they themselves need to change in the new world introduced by the 4th Industrial Revolution. Traditional skills are no longer applicable and team members should become experts in their specific disciplines and master the art of their discipline. This specialisation leads to the formation of guilds which reflect the original guilds formed by various artisans (Frey & Osborne, 2017; James & Charles, 2003). Team members belong to guilds for training, development and innovation (PWC, 2017), thus introducing a culture of continuous learning. The team members have high levels of knowledge and experience and are intrinsically motivated (Levitt, 2011). Team members perform their activities with the minimum supervision or oversight, making the role of the project manager obsolete. They are motivated by their level of status and respect in their respective guilds rather than by their salary, formal title or position in the hierarchy (Levitt, 2011). Working in this kind of environment, with very few rules or procedures about how to do things, requires workers to have a high tolerance for ambiguity. There is no right way to do things except the way that they choose to do them (Levitt, 2011).

FUTURE RESEARCH DIRECTIONS The Project Management Institute (2018a) identifies ten technologies that will have an impact on project management. Five of these technologies will have a direct impact on the way agile projects are managed. These five technologies are cloud solutions, IoT, artificial intelligence, 5G mobile internet and voice-driven software. Research is definitely needed on how these technologies will have an impact on the following: • • •

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Agile project management: The question that needs to be answered is whether these technologies will have a positive impact on the four agile principles and what this impact will be. How agile project managers lead and coach their team. Can these technologies be used to make the agile project manager a better coach and leader? How agile team members engage with their discipline and with each other.

 Insights Into Managing Project Teams for Industry 4.0

Apart from the technical research implications on agile project management, research is also required on the implications on the management of agile projects. Agile projects are currently managed with a 3rd Industrial Revolution mindset. This will have to change to adapt to a management style that is conducive for the 4th Industrial Revolution. Research will have to investigate this phenomenon.

CONCLUSION The overall purpose of this chapter was to determine how Industry 4.0 impacts on the role of the project manager and how agile project management facilitates this transition. It is evident from the discussion that traditional project management cannot and will not be able to deliver products and services in an Industry 4.0 environment. Changes are required and these changes incorporate the way project managers perceive themselves in an agile environment as well as how they are going to manage agile teams. The role of the project manager is changing to that of an agile project leader. This agile project leader should display new skills as highlighted in Table 3. The most important change is to move away from command and control and to focus on collaboration and coaching. The agile project leader needs to become a servant-leader that focuses on the well-being of the team members as well as of the project itself. The role is more on guiding and providing lanes within which to operate. If a team member wishes to function in the slow lane at a particular point in time, then it is their prerogative. The agile project leader should have empathy for this choice and provide the necessary support. Agile team members will also change their behaviour. Team members are more empowered in an agile environment. This empowerment places additional responsibilities on the team members. They need to take accountability for their own choices. These choices might result in failure, which is fine in an agile environment. The challenge is to recover quickly from these failures and to learn from them. Agile team members also need to acquire new skills in order to perform optimally in an Industry 4.0 environment. How these skills are acquired and mastered also poses a challenge as traditional teaching and learning is not necessary conducive for mastering these new skills. For project management and the project manager to be relevant in the 4th Industrial Revolution, the following changes have to be made: 1. The project manager will have to see agile projects within the context of Industry 4.0. Agile projects cannot be implemented by ignoring the impact and influence of Industry 4.0. 2. The current practices and standards need to be updated to include agile and scaled agile. This is also applicable to the competency frameworks. These frameworks need to focus on the competencies that agile project managers should master. 3. Industry 4.0 brings complexity and uncertainty to the table. Agile project managers should be able to apply the notion of complex adaptive systems to an agile project to simplify the complexity and uncertainty. The role of the project manager is definitely changing in the new environment created by Industry 4.0. Project managers are going to face more demands and a new set of competencies and skills are required to deliver projects successfully for the 4th Industrial Revolution.

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Loufrani-Fedida, S., & Missonier, S. (2015). The project manager cannot be a hero anymore! Understanding critical competencies in project-based organizations from a multilevel approach. International Journal of Project Management, 33(6), 1220–1235. doi:10.1016/j.ijproman.2015.02.010 Marnewick, C., Erasmus, W., & Joseph, N. (2016). Information technology project managers’ competencies: An analysis of performance and personal competencies. Cape Town, South Africa: AOSIS. doi:10.4102/aosis.2016.itpmc07 Marnewick, C., Erasmus, W., & Joseph, N. (2017). The symbiosis between information system project complexity and information system project success. Cape Town, South Africa: AOSIS (Pty) Ltd. doi:10.4102/aosis.2017.itpsc45 Mnkandla, E., & Marnewick, C. (2011). Project management training: The root cause of project failures? Journal of Contemporary Management, 8, 76–94. Norton, D., & West, M. (2017). Best Practices for Adopting an Enterprise Agile Framework. Retrieved from https://www.gartner.com/doc/3579019/best-practices-adopting-enterprise-agile Project Management Institute. (2017a). Agile Practice Guide. Newtown, PA: Project Management Institute. Project Management Institute. (2017b). A Guide to the Project Management Body of Knowledge (PMBOK® Guide) (6th ed.). Newtown Square, PA: Project Management Institute. Project Management Institute. (2017c). Project Manager Competency Development Framework (3rd ed.). Newtown, PA: Project Management Institute. Project Management Institute. (2018a). Maximizing the Benefits of Disruptive Technologies on Projects. Retrieved from https://www.pmi.org/learning/thought-leadership/pulse/benefits-disruptive-technologiesprojects Project Management Institute. (2018b). The Project Manager of the Future: Developing Digital-Age Project Management Skills to Thrive In Disruptive Times. Retrieved from https://www.pmi.org/learning/thoughtleadership/pulse/the-project-manager-of-the-future?utm_source=pmi&utm_medium=website&utm_ term=DXP6950&utm_content=HomePage_HeroImage&utm_campaign=Pulse2018&utm_ aud=PMIwebsite&utm_thm=learning_thought_leadership_Pulse&utm_sdte=09_17_18&utm_dep=mkt PWC. (2017). The future of work: A journey to 2022. Retrieved from https://www.pwc.co.uk/assets/pdf/ future-of-work-report.pdf Ramazani, J., & Jergeas, G. (2015). Project managers and the journey from good to great: The benefits of investment in project management training and education. International Journal of Project Management, 33(1), 41–52. doi:10.1016/j.ijproman.2014.03.012 Ramos, P., Mota, C., & Corrêa, L. (2016). Exploring the management style of Brazilians project managers. International Journal of Project Management, 34(6), 902–913. doi:10.1016/j.ijproman.2016.03.002

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Russell, R. F., & Stone, G. A. (2002). A review of servant leadership attributes: Developing a practical model. Leadership and Organization Development Journal, 23(3), 145–157. doi:10.1108/01437730210424 Ruuska, I., & Teigland, R. (2009). Ensuring project success through collective competence and creative conflict in public-private partnerships - A case study of Bygga Villa, a Swedish triple helix egovernment initiative. International Journal of Project Management, 27(4), 323–334. doi:10.1016/j. ijproman.2008.02.007 Santos, C., Mehrsai, A., Barros, A. C., Araújo, M., & Ares, E. (2017). Towards Industry 4.0: An overview of European strategic roadmaps. Procedia Manufacturing, 13, 972–979. doi:10.1016/j.promfg.2017.09.093 Scaled Agile Inc. (2018). SAFe®. Retrieved from https://www.scaledagileframework.com/ Schneider, P. (2018). Industry 4.0: a managerial research agenda for a nascent filed. Paper presented at the EURAM 2018, Reykjavik, Iceland. Schwalbe, K. (2016). Information technology project management (8th ed.). Boston, MA: Cengage Learning. Sendjaya, S., & Pekerti, A. (2010). Servant leadership as antecedent of trust in organizations. Leadership and Organization Development Journal, 31(7), 643–663. doi:10.1108/01437731011079673 Seymour, T., & Hussein, S. (2014). The History Of Project Management. International Journal of Management & Information Systems (Online), 18(4), 233–240. Söderlund, J., & Lenfle, S. (2013). Making Project History: Revisiting the Past, Creating the Future. International Journal of Project Management, 31(5), 653–662. doi:10.1016/j.ijproman.2013.02.005 Stone, G. A., Russell, R. F., & Patterson, K. (2004). Transformational versus servant leadership: A difference in leader focus. Leadership and Organization Development Journal, 25(4), 349–361. doi:10.1108/01437730410538671 Sudhaman, P., & Thangavel, C. (2015). Efficiency analysis of ERP projects—Software quality perspective. International Journal of Project Management, 33(4), 961–970. doi:10.1016/j.ijproman.2014.10.011 Svejvig, P., & Andersen, P. (2015). Rethinking project management: A structured literature review with a critical look at the brave new world. International Journal of Project Management, 33(2), 278–290. doi:10.1016/j.ijproman.2014.06.004 Taylor, K. J. (2016). Adopting Agile software development: The project manager experience. Information Technology & People, 29(4), 670–687. doi:10.1108/ITP-02-2014-0031 Turetken, O., Stojanov, I., & Trienekens, J. J. M. (2017). Assessing the adoption level of scaled agile development: A maturity model for Scaled Agile Framework. Journal of Software: Evolution and Process, 29(6), 1–18. doi:10.1002mr.1796 VersionOne Inc. (2018). The 12th Annual State of Agile Report. Retrieved from https://explore.versionone.com/state-of-agile/versionone-12th-annual-state-of-agile-report

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Vinekar, V., Slinkman, C. W., & Nerur, S. (2006). Can Agile and Traditional Systems Development Approaches Coexist? An Ambidextrous View. Information Systems Management, 23(3), 31–42. doi:10 .1201/1078.10580530/46108.23.3.20060601/93705.4 Weber, B., Butschan, J., & Heidenreich, S. (2017, June 8-10). Tackling hurdles to digital transformation - the role of competencies for successful IIoT implementation. Paper presented at the 2017 IEEE Technology & Engineering Management Conference (TEMSCON). West, M. (2017). Scrum Is Not Enough: Essential Practices for Agile Success. Gartner. Retrieved from https://www.gartner.com/doc/3614417/scrum-essential-practices-agile-success Winston, B., & Fields, D. (2015). Seeking and measuring the essential behaviors of servant leadership. Leadership and Organization Development Journal, 36(4), 413–434. doi:10.1108/LODJ-10-2013-0135

This research was previously published in Agile Approaches for Successfully Managing and Executing Projects in the Fourth Industrial Revolution; pages 99-118, copyright year 2019 by Business Science Reference (an imprint of IGI Global).

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Agile Approaches for Successfully Managing and Executing Projects in the Fourth Industrial Revolution Alexius A. Emejom University of the People, USA Carl Burgess University of North Texas at Dallas, USA Donna Pepper Benedictine University, USA Joan Adkins Colorado Technical University, USA

ABSTRACT The fourth industrial revolution utilizes artificial intelligence by automating large quantities of numbers to increase the chances of project success. The Project Management Institute lists examples of project outcomes, including but not limited to the Pyramids of Giza, the Great Wall of China, the Panama Canal, and the placement of the International Space Station into Earth’s orbit. This chapter highlights how the fourth industrial revolution (Industry 4.0) impacted the evolution of agile project management practices. It discusses how these could be applied in conjunction with traditional waterfall project management or as a standalone approach. Topics discussed include a definition and elements of project management, waterfall vs. agile project management, transitioning to agile methods, developments in agile project management, agile practices, and leading agile projects and project managers.

DOI: 10.4018/978-1-7998-8548-1.ch076

Copyright © 2021, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

 Agile Approaches for Successfully Managing and Executing Projects in the Fourth Industrial Revolution

INTRODUCTION Project management (PM) practitioners often discuss the waterfall PM approach, which involves progressive elaboration of plans and scope of project (Project Management Institute, 2017). The evolving complex business environment requires a different set of knowledge and skills to effectively manage projects. Therefore, the agile PM approach serves as a bridge to connect the waterfall to the demands of the current business environment. Industrial revolutions have been characterized by technological leaps and bounds that have led to paradigm shifts. According to Lasi, Fettke, Kemper, Feld, and Hoffmann (2014), the fields of mechanization, water power, and steam power represent the first industrial revolution. The intensive use of electricity through mass production and assembly lines represent the second industrial revolution (Lasi et al., 2014). The widespread use of digitalization and automation is the third industrial revolution. The fourth industrial revolution is advanced digitalization with the combination of Internet technologies and future-oriented technologies in the field of smart machines and products. Cyber-physical systems manage projects during the fourth phase. Iterative systems, like robotics and real-time cloud computing, are intertwined state-of-the-art autonomous frameworks offering a new approach for uncovering the possibilities in data (Reynolds, 2016). Pinkham (2017) wrote that the fourth industrial revolution is commonly referred to as Industry 4.0. This chapter draws from researchers’ extensive review of literature on PM practices, the waterfall method, and agile PM approaches. Researchers have synthesized their findings to highlight how the fourth industrial revolution supports agile PM as both a standalone and in conjunction with the waterfall PM methodology.

DEFINITION AND ELEMENTS OF PROJECT MANAGEMENT A project is defined as a set of unique temporary interrelated activities that are executed within a fixed time (schedule), meeting a certain cost, and following limitations (scope) to achieve a specific goal (Project Management Institute, 2017b). PM is the application of knowledge, skills, tools, and techniques to project activities that meet the desired project requirements on time, on budget, and within a defined scope (ibid, 2017). Although used by industries for many years, PM received minimal recognition until the 1950s and 1960s (Loucks, 2008). Over the last 25 years, PM has adapted to changes in society by increasing professionalism in special projects (Thomas & Adams, 2005). Organizations set and achieved goals with PM through an iterative four-step process of plan, do, check, and act (PDCA). These steps fall into the following process groups in PM: (1) initiating; (2) planning; (3) executing, monitoring, and controlling; and (4) closing (Project Management Institute, 2017b). These phases help the project manager understand the project scope, recognize challenges, and resolve issues connected to PM (Melton, 2004). This process has also assisted businesses and industries to recognize (rather than repeat) mistakes (Owen & Burstein, 2005). The initiating process group is the first phase for PM. During this phase, the project manager communicates with other members of management to establish objectives and determine their project needs (Suttle, n.d.). When the team has decided on whether to accept or reject the project, they may use descriptive analytics, predictive analytics, and prescriptive analytics (Kelly, 2017). Descriptive analytics 1530

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provide data aggregation and data mining to inform team members on information concerning the past. Predictive analytics use statistical modeling to understand the future. To assist in possible outcomes, project managers may use prescriptive analytics to optimize and simulate algorithms (Kelly, 2017). The creation of a project plan may vary between organizations (Allen, McLees, Richardson, & Waterford, 2015). According to Kerzner (2009), the key elements of a project plan are “project requirements, project management, project schedules, facility requirements, logistic support, financial support, manpower, and organization” (p. 5). Allocating the right resources at the right time ensures a plan’s successful. Planning increases the chances for project success. Failure to plan is planning to fail (Kerzner, 2009). The planned work begins during the executing process group phase. The project manager, having analyzed the project’s plan, scope, and schedule, determines if the baselines will be impacted by available information and begins its execution (Alecu, 2011). During project execution, the project manager monitors and controls tasks to ensure that they remain within the scope, cost, and schedule. In addition, they manage the success of the project’s overall objective. Monitoring and controlling the project addresses issues as they arise. Management should be a daily task achieved through monitoring the work, identifying and resolving issues, tracking the project, and taking corrective action to resolve issues (Young, 2007). The closing process group, which is the final phase of a project, occurs for two reasons. The first reason to close a project is because the phase or project is complete. In this case, the project adhered to the plan and met its objectives (Young, 2007). The second reason for closing a project is because the activities across the process groups have been completed and the project is accepted by the owner. The owner’s acceptance is because the project objective has been met or the project has become obsolete/ irrelevant to an organization’s objective (Project Management Institute, 2017b; Young, 2007). PM has 10 knowledge areas (Project Management Institute, 2017b): 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

Integration management Scope management Time management Cost management Quality management Human resources management Communication management Risk management Procurement management Stakeholder management

These knowledge areas are often used on projects. All five process groups must be applied from start to finish (ibid, 2017). Table 1 shows a mapping between knowledge areas and process groups. It also includes activities conducted in the process groups. The activities outlined in Table 1 are key to ensuring that any project is managed effectively. Activities can be seamless when project managers understand interactions between the knowledge areas and process groups, as well as implement the outlined activities to effectively communicate and gain knowledge by leveraging the power of cloud computing, Internet connectivity, and process integration (Cervone, 2011). This will ultimately increase the chances of completing a successful project.

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Table 1. Mapping of PM process groups and knowledge areas Project Management Process Groups Knowledge Areas

Project Integration Management

Initiating Process Group Develop Project Charter

Planning Process Group

Develop PM Plan

Executing Process Group Direct and Manage Project Work

Monitoring and Controlling Process Group

Closing Process Group

Monitor and Control Project Work Perform Integrated Change Control

Close Project

Project Scope Management

Plan Scope Management Collect Requirements Define Scope Create WBS

Validate Scope Control Scope

Project Time Management

Plan Schedule Management Define Activities Sequence Activities Estimate Activity Duration Develop Schedule

Control Schedule

Project Cost Management

Plan Cost Management Estimate Costs Determine Budget

Control Costs

Project Quality Management

Plan Quality Management

Perform Quality Assurance

Project Human Resources Management

Plan Human Resource Management

Acquire Project Team Develop Project Team Manage Project Team

Project Communication Management

Plan Communications Management

Manage Communications

Project Risk Management

Plan Risk Management Identify Risks Perform Qualitative Risk Analyses Plan Risk Responses

Project Procurement Management

Plan Procurement Management

Conduct Procurements

Contgrol Procurements

Plan Stakeholder Management

Manage Stakeholder Engagement

Control Stakeholder Engagement

Project Stakehlolder Management

Identify Stakeholders

Control Quality

Control Communications

Control Risks

Close Procurements

Source: Project Management Institute (2017b)

WATERFALL VS. AGILE PROJECT MANAGEMENT Waterfall PM is a traditional method used in software development (Pedersen, 2013). It requires many phases to implement, including conceptualization and requirements determination, designing, implementation, verification, and maintenance (Ledbrook, 2012). The waterfall method requires extensive

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planning in the early part of the project. It can effectively and carefully monitor systems to avoid mistakes (Russell, 2012). There are both benefits and disadvantages to this method. The waterfall method has three benefits: (1) reduction of repetitive work; (2) reduction of bottlenecks; and (3) delivery of value to the project before completion. This method will reduce repetitive work by allowing separate teams to work on parallel sections of the project, which reduces the cost and time it takes to complete the project (Jackson, 2012). Waterfall delivers value to PM through interaction and communication. Waterfall also allows project managers to adjust resources as needed and notify stakeholders of changes (ibid, 2012). Teams can communicate data on a regular basis to assist in early detection of issues. Cost prohibitive disadvantages of the waterfall method include external factors influencing the project, a lack of adapting to change during phases, a poorly structured system, time wasted on documentation, and premature software testing (Pedersen, 2013). Agile PM is also used in software development. This approach is more flexible and quickly adapts to changes during development (Pedersen, 2013). The agile approach has an increased return on investment, as well as early detections to help the team adapt to customer needs (Kataria, 2016). This improves customer service and project control. The agile framework includes an agile development measurement index and a four-stage process to adapt to changes (Pedersen, 2013). This component measures a company’s agility with three components. The first component assesses and measures an organization’s potential for agility. The second measures and identifies the agility level for a project that aspires to adopt an agile method. Third, it assists in developing teams with essential agile qualities to reach certain objectives and measure organizational agility (Sidky, 2007). The four-stage process consists of: (1) the identification of discontinuing factors; (2) project level assessment; (3) organizational readiness; and (4) reconciliation. Stage 1 identifies whether the organization is capable of transitioning to agility. The organization must decide if the journey is worth the time and money. In addition, the organization must determine if it is ready for these changes. Once the decision has been made to proceed with the project, stage 2 focuses on identifying and assessing factors outside of the organization’s control. The PM team researches factors that could jeopardize a successful agile practice (Sidky, 2007). During stage 3, the team decides if the organization is ready to adopt the project’s objective. The team will perform an assessment to address how agile practices fit with the organization’s operational procedures. Stage 4 reconciles differences between the two levels. The coach will apply the agile adoption framework after the issues are presented (Sidky, 2007). Both waterfall and agile methods are important in PM. However, the agile approach is more user friendly than the waterfall method. The agile approach adapts to the needs of the organization in real time, which increases return on investment, project control, and customer service (Kataria, 2016). The waterfall method focuses on plans, tools, and templates. It has the potential to reduce repetitive work, decrease bottlenecks, and deliver value to the project before completion (Project Management Institute, 2017a).

WHY THE AGILE APPROACH? From a strategic and technological perspective, the 21st century requires businesses to restructure and reengineer their processes and procedures to adhere to clients who demand superior, low- cost products that are explicit to rapidly changing needs in their markets (Gunasekaran, 2001). Agile methods address challenges faced by today’s businesses. For example, Gunasekaran (2001) described agile manufactur1533

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ing as the ability to survive and flourish in markets that drive change due to quick response in a world of rapid and unpredictable change. A group of PM professionals was asked, “What does agile mean to you?” Astonishingly, no one had the same answer (Fewell, 2017). First, individuals and/or organizations must recognize the need for an agile approach: • • •

Does your software project need correlation? Does your model require significant change? Is product quality a concern?

Once these questions are answered, individuals and/or organizations can explore value-oriented procedures. An organization may need a skilled facilitator to assist with identifying the source of the problem. After an organization tailors a technique to addresses its needs, then it can begin to modify specific parts to emphasize and/or ignore using an agile approach (Fewell, 2017). It is key to concentrate on identifying and solving one problem at a time (Fewell, 2017). Businesses must push the envelope in response to delivery time, increased product quality, and excellent customer service and satisfaction (Gunasekaran, 2001). Agile approach ingenuity harnesses new opportunities offered by the fourth industrial revolution to meet the challenges of physical and digital technological needs. Industry 4.0 digitally establishes an interconnectedness fostering capabilities for improved, well-informed decision making. The age of the agile approach and Industry 4.0 is merging physical and digital technologies by integrating analytic systems to process data with advanced algorithms. Cognitive computing offers a different approach by looking within and across unrelated data sets (i.e., rich media and text). Cognitive computing recognizes conflicting data, uncovers surprises, looks for patterns and context, and offers suggestions and solutions (Reynolds, 2016). Artificial intelligence (AI) applications, which include automated supermarkets handled with limited (or no) human supervision, are sparking new debates due to extreme automation (Özdemir & Hekim, 2018). The IoT is built on broadband wireless Internet connectivity, which utilizes miniscule sensors implanted into both animate and inanimate objects (Özdemir & Hekim, 2018). The agile approach and Industry 4.0 are creating systems that are digitally and physically connected. These systems improve operations, production, innovation, and advancement as they use data to drive intelligent action throughout the value chain (Denning, 2017). In addition, during the unstoppable 4.0 revolution, manufacturing will witness the invention of cyber-physical systems (Loffler & Tschiesner, 2013). Industry 4.0 is creating intelligent systems connecting machines, systems, and work pieces to control each system autonomously (Loffler & Tschiesner, 2013). Possibilities include the decentralization of production control, smart machines that predict future complications and prompt maintenance procedures autonomously, or systems that react to the unexpected (Loffler & Tschiesner, 2013). In fact, some organizations have already been using the agile approach. Businesses like Apple and Samsung use technology that customers can tailor to meet individual wants and needs (Denning, 2017). Companies like Tesla, Saab, and Ericsson are applying advancements to cars, planes, and networks, which continue to upgrade by delivering software to these products via the Web (Denning, 2017). Some businesses cross over to agile methods more slowly as they reflect on their traditional management methods. However, they face frustration when they realize they are seeing the same problems repeat as their solutions fail.

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Cyber-physical systems merge physical and software components to operate on different scales. For example, some of the combined systems include self-governing automobile systems, medical monitoring, process control systems, robotics, and automatic pilots. These systems come together in the IoT, which is operated through sensors and actuators in both wired and wireless networks over the Internet protocol (Loffler & Tschiesner, 2013). This new paradigm shift allows team, units, and complete enterprises to agilely upgrade and adapt products and services through quality improvements or new products (Denning, 2017). Some firms embrace this shift. Others resist the change. Mature organizations that have successfully operated in a more traditional manner may find it hard to change their processes, routines, attitudes, and values (Denning, 2017). Managers who have not embraced the new technology noted that they do not see how the momentum came from software development, especially because this was an area with no prior “reputation for excellence” for managers (Denning, 2017). Many doubted that they could learn management skills based on software development and technology. In reflecting on managing in the 20th century, managers flourished in their careers based on concepts they were taught (and are still being taught) in business schools (Denning, 2017). These organizations do not understand that access to evolving technology is not the complete answer. Instead, agility and willingness to adjust to technology allows their organizations to best meet customers’ needs (Denning, 2017). To understand the agile approach, managers must start a conversation about why different habits, processes, attitudes, and principles are necessary, and why a shift in software is fundamentally creating a different approach to management (Denning, 2017). Managers and teams must explore quality-oriented techniques and procedures that pair team members with differing processes. These strategies allow an organization to accept the paradigm shift as businesses become software dependent and the agile platform accelerates (Denning, 2017).

TRANSITIONING TO AGILE METHODS Numerous challenges related to agile transition are related to humans as noted in a grounded theory study conducted in 13 countries with 49 agile experts (Gandomani & Nafchi, 2016). The challenges can be categorized into “impediments to agile transition” and “perceptions about the change process” (Gandomani & Nafchi, 2016). The list includes: • • • • •

Deficiency in knowledge Cultural problems Resistance to change Flawed attitude Nonconformity to collaboration

Data analysis of the grounded theory study revealed that individuals held flawed attitudes toward agile transition based on misguided information, apprehension about the transition, uncertainty and indifference to change, and improbable expectations (Gandomani & Nafchi, 2016). Although an organization’s agile transition may seem straightforward, the process can be challenging (Gurses, 2006). A flawless transition sounds good, however, organizations can make avoidable mistakes. 1535

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The biggest mistakes include pilot projects with poor time management, new methodologies, inadequate research, the wrong people, tight deadlines (Gurses, 2006). Organizations should proceed carefully and include stakeholders in the development of the agile transition. Established networks and processes are required, as well as consideration of the corporate culture. This includes project planning, teamwork, employee attitudes, departmental politics, and project pace. It is beneficial to hire staff with previous agile transition design experience. Therefore, organizations should hire a consultant (Gurses, 2006). Key factors to a smooth transition are “up front analyses and constant real-time monitoring” (Gurses, 2006, p. X). A case study was conducted on 17 organizations that used agile methods for more than three years. The study’s compilation of serious challenges listed the following as the top problems: (1) people; (2) recruitment; (3) training; (4) motivation; and (5) performance evaluation (Conboy, Coyle, Xiaofeng, & Pikkarainen, 2011). Current trends related to agile method transitions focus on alignment through customers, suppliers, consultants, and the public sector (Conboy et al., 2011). In the fourth industrial revolution, software will monitor conditions and supply diagnoses. This permits systems to self-monitor and self-predict, as well as allows managers more insight into the health of processes and procedures. The Allegro Group, according to Raczka (2015), needed a system to respond quickly to changes in organizational operation and market response. The organization realized that their waterfall process was no longer meeting their needs. However, some individuals in the organization were not committed to the adoption of an agile method (Raczka, 2015). Within two days, an experienced agile coach met with employees from all levels and departments of the organization to review the flow of the transition. This coaching approach immediately addressed issues related to communication and team members’ availability (Raczka, 2015). Management had the opportunity to make changes using their management style in collaboration with the agile approach. Overall, the Allegro Group’s transition was not difficult. However, the organization’s initial agility was problematic (Raczka, 2015). Managers had to adjust their way of thinking as they recognized and acted on necessary changes. Adapting to new situations is not always easy. Yet seeing successful results in an agile transition is encouraging. The Allegro Group enjoyed shorter times to market, experienced a renewed relationship between business and information technology, and saw satisfied employees (Raczka, 2015). Agile transitions should be completed in several phases (Couture, 2013). For example, after using a two-week development plan and a two-week quality assurance (QA) approach, one business identified key capabilities that could be finalized through a four-week development plan (Couture, 2013). After four weeks, the business team established capabilities from “data intake to data transformation to reports” (Couture, 2013, p. X). Transitioning to agile methods is not simply adopting a different approach. Agility must be adapted to fit an organization’s specific needs and challenges, as well as prepare for future changes (Couture, 2013).

DEVELOPMENTS IN AGILE PROJECT MANAGEMENT PM has gone through several changes to include the development of agile practices. This section reviews current developments, trends, and events in agile practices. According to Stankovic, Nikolic, Djordevic, and Cao (2013), the development of agile PM provides stability in many software applications. Stankovic et al. (2013) opined that agile PM has been successful in many countries, including Yugoslavia. Stankovic

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et al. (2013) presented current phases of development in agile PM and discussed how the phases improve an organization’s efficiency and effectiveness through:    • • • • •

Software development Product development Opportunities created by agile PM Values and principles driving management Organizational growth through agile PM

A 2012 IBM Institute study on agile practices showed the impact and trends embraced by society, including mobile devices, unstructured data, intelligent and connected devices, and sensors. According to Randall (2014), most managers endorse these trends due to competitive advantages. Although advantageous, Randall (2014) reported that only 25% of teams have effectively implemented agile practices. Due to ineffective implementation, industries have experienced execution gaps and missed opportunities in management. When used correctly, agile practices (i.e., cloud computing and cognitive computing) can reduce waste by prioritizing business values through redesigned projects in a more efficient climate. Project managers will be required to use impactful agile practices to lead teams through problems and issues in an open and transparent environment. Randall (2014) noted that organizations must develop dynamic capabilities to achieve open and transparent environments. Industry 4.0 enables agile project managers to provide a correlation of shared computing services in cloud computing and mobile applications. Khalid, Zara, and Fahad (2014) explained that development teams must face several complications to advance successful mobile applications. If facilitated correctly, agile teams can successfully manage the 4.0 environment in relation to innovative standards, changing platforms, and efficient interface.

Current Developments Agile practices have been influential in software development and a forward-thinking industry. Azuara (2015) stated that agile practices gave birth to essential software applications, including mobile devices, extreme programming, and scrum. Agile teams have adapted extreme programming and scrum to provide a basis to assign and complete work in an incremental and interactive process with frequent customer consultation (Bond, 2015). Stakeholders can communicate and coordinate tasks, as well as set goals for usability and feasibility. Turk, France, and Rumpe (2005) characterized these new applications within Industry 4.0 as: (1) sustained interactions over processes and tools; (2) active and efficient software providing comprehensive documentation; (3) enhanced customer collaboration over contract negotiations; and (4) responses to change regarding planning stages.

4.0 Revolution Industry 4.0 has a huge impact on agile PM practices. Organizational project managers use agile practices to react accordingly to architecture, increase technology, and assist in the development of techniques and methodology. Ghilic-Micu, Stoica, and Uscatu (2014) concluded that agile practices enhance Industry 4.0 regarding cloud computing, cognitive computing, cypher- physical systems, and the Internet of things (IoT). Project managers use these practices to increase productivity through technological advancements in robotics, artificial intelligence, nanotechnology, quantum computing, and biotechnology. For instance, 1537

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cloud computing has provided agile implementation to organizations in the form of easier access and flexibility. Ghilic-Micu et al. (2014) stated that cloud computing is one example of how agile practices have positively impacted today’s society. The following list offers concepts and advancements through agile management impressions (Ghilic-Micu et al., 2014): • • • • • • •

Information technology provide service-oriented approaches Scalable and massive infrastructures Shared, configurable, flexible, and dynamic resources Internet access to all devices A platform providing self-management and autonomy Employment model based on self-service Billing based on measured services

Communication Poor communication during the process of change can present challenges and dissatisfaction. Connecting the agile practice framework to traditional techniques contributes to a challenging paradigm related to communication connectivity (Jamieson & Fallah, 2012). Bjarnason, Wnuk, and Regnell (2012) stated that stakeholder communication can cause negative aspects of team involvement if stakeholders fail to reach a goal agreement. This can cause an excessive burden on the scope of the project. Improper communication can also result in adverse aspects of customer feedback. Agile practices provide essential interactions and flexibility within the project team. As a result, teams can identify customer needs and/or wants. Conversely, Olsson, Alahyari, and Bosch (2012) opined that the project may fail if the team does not promptly receive customer feedback.

AGILE PRACTICES Agile practices are a set of management practices that involve collaboration between self-organizing and cross-functional teams. It provides stability and productivity in communication, as well as allows for adaptive planning and continuous improvement (Denning, 2017). Turk et al. (2005) identified the following principles instigating project performance: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 1538

Customer satisfaction through efficient software delivery Support of customers’ competitive advantages Frequent and efficient deliveries Enhanced collaboration between team members, developers, and consumers Improved PM Autonomy in the project team, including trust and support Face-to-face conversations to communicate project information Sustainable development Technical excellence Simplicity

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11. Organized teams Most studies have covered outcomes associated with three processes: (1) scrum; (2) kanban; and (3) lean. The processes work together to deploy teams that provide interactive, real-time perspectives to customers and vendors. • • •

Scrum: An interactive product development strategy in which a development team works as a unit to reach a common goal and challenges assumptions of the traditional, sequential approach to product development. Kanban: A scheduling and inventory-control system used to manage the delivery of work. By controlling the amount of work, the approach aspires to increase the flow of finished work. Lean: Continuous improvement and respect for people. This process is associated with improvements, reduced cycle time, and improved efficiency.

In today’s Industry 4.0 society, these practices optimize tools that place organizations in a certain mindset. For example, managers can focus on customer and user innovations rather than short-term profits (Denning, 2017). Second, agile practices question how managers view themselves in relation to PM. Managers can draw on the full talents and capacities of their employees. Drury-Grogan (2014) opined that agile practices provide stability and productivity in communication, coordination, and team objectives. Moreover, agile practices allow for autonomy, which leads to the pursuit and accomplishment of mission-critical tasks.  McHugh, Conboy, and Long (2012) explained that organizations using agile practices measure a level of self-management in which teams are empowered and responsible for their projects and goals. However, the most critical mindset is the advocacy of transparency. This results in improved products and services like software applications. Holmstrom, Fitzgerald, Agerfalk, and Conchuir (2006) stated that agile practices decrease the reduction of temporal, geographical, and subcultural issues in software. Teams gain empowerment and responsibility in goal attainment. To further illustrate the provision of agile practices, Table 2 and Table 3 provide beneficial characteristics to PM teams. Table 2. Attributes of agile practices in Industry 4.0 Proactive

Anticipation of problems related to change. A solution of change-related issues. Personal initiative.

Adaptive

An interpersonal approach allowing for spontaneous collaboration, flexibility, and autonomy.

Resilient

Positive attitude toward changes, ideas, and technology. Tolerance to uncertain and unexpected situations. Assistance in coping with stress.

Collaborative

The ability to collaborate with other teams, functions, and organizations.

Source: Sherehiy, Karwowski, and Layer (2007)

Agile Practices and Traditional Project Management Traditional practices of PM have been countered using agile PM methodologies. Issues such as a changing work scope, early part design freeze, infrequent customer interaction, and a rigid development process

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Table 3. Benefits of agile teams Purposefulness

Positive self-concepts to endure ambiguousness and stressful work situations.

Awareness

Active learning and an openness to ever-changing environments.

Action-Oriented

Taking initiative, acting or reacting as necessary, and establishing high levels of productivity while minimizing setbacks.

Resourcefulness

Ability to secure resources, talent, and support to meet goals.

Networking

Active relationships in the community. Creating a sense of connectedness and meaning.

Source: McCann and Selsky (2012)

can result in excessive rework and a dissatisfied customer due to a missed target (Serrador & Pinto, 2015). Traditional PM has been designed to be efficient and linear (Conforto, Rebentisch, & Amaral, 2016). According to Conforto et al. (2016), innovative and sophisticated products require new processes and methods to fulfill the need for flexibility and improved flow of information. Teams benefit from the agile project through the completion of project requirements regarding iterations and/or reducing and limiting uncertainty in projects. What is the difference in performance measures? Agile practices tend to encounter higher risks than traditional projects. However, they also provide more flexibility to easily adjust to changes in project requirements (Wysocki, 2006). In general, project managers reduce risk and preserve constraints of time and money. These elements are required to realize success, goal and project completion, and customer satisfaction. Agile project managers provide an extra level of management control and success by focusing on deliverable business values and budget, which alludes to the delivery of the product. Traditional methods are known to adhere to a set process. Wysocki (2006) explained that agile project teams require colocation of team members and staff to embrace change and rapidly produce increments. By doing this, projects are worked in multiple locations, which allows for teams to use agile methods in each location. Since agile team members are called to take on more significant roles, the commitment level must be higher to provide flexibility and improved flow of information (Conforto et al., 2016).

LEADING AGILE PROJECTS According to Nerur, Mahapatra, and Mangalaraj (2005), the current business environment is dynamic. Organizations strive to adapt their structures, strategies, and policies to suit this ever-changing environment. Allen (2017) noted that the changing environment reflects the industry 4.0 revolution’s paradigm shift in industrial manufacturing. This shift has been analogue to digital transformations that connect a supply chain and the enterprise resources planner (ERP) to the production line to form an integrated, automated, and potentially autonomous process to improve the use of resources. In comparing Industry 4.0, traditional PM approaches focus on the implementation of plans, best practices, and procedures with the use of specific tools and templates (Project Management Institute, 2017b). It follows a plan when responding to change and/or contract negotiations with customer collaboration (Cervone, 2011).

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The agile PM approach is characterized by adaptability during the project lifecycle (Špundak, 2014). The focus shifts to adapting and responding to change vs. following the plan, individuals, teams, interactions, and communication over processes and tools, as well as a functional deliverable vs. comprehensive documentation (Cervone, 2011; Špundak, 2014). This shift in focus captures the essence of Industry 4.0, which marks the end of traditional centralized applications for production control. It focuses on a vision of an ecosystem of smart factories that respond in real time to customers’ demands for tailored products (Almada-Lobo, 2015). Špundak (2014) opined that the agile approach to leading projects requires a change in the project manager’s thought process. Emphasis is placed on formal and/or informal communication and collaboration between project team members in the decision-making process (Nerur et al., 2005; Project Management Institute, 2017a; Špundak, 2014). This is a shift from the traditional, inflexible PM (Nerur et al., 2005). With Industry 4.0, agile PM integrates cyberphysical systems as it fuses the physical and virtual worlds to define target objectives and plan a transformational roadmap (Almada-Lobo, 2015).

AGILE AND PROJECT MANAGERS The wave of new agile PM approaches has caused a paradigm shift from predictive project lifecycle to adaptive project lifecycle (Nerur et al., 2005). Each lifecycle contains one or more project phase associated with the development of the product, service, or result. These phases may be predictive, iterative, incremental, adaptive, or a hybrid of predictive and adaptive. In predictive lifecycles, according to Nerur et al. (2005), changes to the project scope, schedule, and budget can be determined during the early phases of a project lifecycle. These triple constraints can be carefully managed. Iterative lifecycles determine the project scope early in the project; the schedule and budget estimates are routinely modified. Incremental lifecycles produce the deliverable through a series of iterations that add features and functionalities to meet customers’ needs within a predetermined timeframe. Adaptive lifecycles are agile, iterative, or incremental. The detailed scope of this lifecycle is clearly defined and approved before the start of an iteration. This is the agile or change-driven process (Project Management Institute, 2017a). The hybrid of predictive and adaptive lifecycles considers the aspects of the project that have fixed requirements to follow a predictive lifecycle; evolving aspects of the project follow an adaptive lifecycle (Cervone, 2011; Nerur et al., 2005; Serrador & Pinto, 2015). Overall, the PM team, with the leadership of the project manager, determines the best lifecycle for each project. Regardless, the project lifecycle needs to be flexible to accommodate the variety of business environmental changes. How can project managers leverage Industry 4.0 in managing agile projects through adaptive lifecycles? This is possible due to the benefits of connectivity and advanced computing power, which is the main idea behind the fourth industrial revolution (Lasi et al., 2014). According to Almada-Lobo (2015): …manufacturing equipment will turn into cyber-physical production systems (CPPS)—software enhanced machinery, also with their own computing power, leveraging a wide range of embedded sensors and actuators, beyond connectivity and computing power. (p. 17) This means that Industry 4.0 has the potential to give way to mass customization. Each product at the end of the supply chain has unique characteristics defined by the end user. 1541

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SOLUTIONS AND RECOMMENDATIONS FUTURE STUDY The purpose of this chapter looks beyond what currently exists in agile PM. It imbeds knowledge and understanding of agile approach frameworks to create successful, horizontal discussions in managing and executing projects in the fourth industrial revolution. Based on extensive review of the literature the study revealed that organizations, after identifying the necessary agile approach, are willing to try several techniques. It is also recommended that organizations should allot time between each project phase to identify high-end quality services and/or processes derived from least cost. In summary, organizations considering adapting an agile approach should focus on a key problem, implement an agile approach best suited to solving their problem, and keep moving forward (Fewell, 2017). As for future study, the researchers recommend replicating this study by applying a phenomenological research design with an appreciative inquiry approach. According to Heidegger, Stambaugh, and Schmidt (2010), this design would allow participants to be engaged in such a way that would allow them to offer their lived experiences in response to the research questions (Creswell, 2014).

CONCLUSION The fourth industrial revolution, or Industry 4.0, has advanced digitalization with the combination of Internet technologies and future-oriented technologies in the field of smart machines and products (Pinkham, 2017). These advancements have led to a paradigm shift in business management, particularly PM. Agile PM integrates tools and templates applicable to various agile processes (i.e., waterfall, scrum, Kanban, and lean) while leveraging the benefits of technological advancements delivered by the fourth industrial revolution.

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Jamieson, J. M., & Fallah, M. H. (2012). Agile quality management techniques. Software Quality Professional, 14(2), 12-21. Retrieved from https://search-proquest-com.proxy.cecybrary.com/docview/196 3800521/96988BB3942745BAPQ/1?accountid=144789 Kataria, N. (2016). Implementing agile development at scale: An industry case study. Kelly, W. (2017, January 3). What project teams need to succeed in the fourth industrial revolution [blog post]. Liquid Planner. Retrieved from https://www.liquidplanner.com/blog/preparing-project-teamsfourth-industrial-revolution/ Kerzner, H. (2009). Project management: A systems approach to planning, scheduling, and controlling (10th ed.). Hoboken, NJ: John Wiley & Sons. Khalid, A., Zara, S., & Fahad, K. M. (2014) Suitability and Contribution of Agile Methods in Mobile Software Development. I.J. Modern Education and Computer Science, (2), 56-62. Retrieved from http:// www.mecs-press.org/ijmecs/ijmecs-v6-n2/IJMECS-V6-N2-8.pdf Lasi, H., Fettke, P., Kemper, H., Feld, T., & Hoffmann, M. (2014). Industry 4.0. Business & Information Systems Engineering, 6(4), 239–242. doi:10.100712599-014-0334-4 Ledbrook, L. (2012). Waterfall project management: An overview. Retrieved from http://projectcommunityonline.com/waterfall-project-management-an-overview.html Loffler, M., & Tschiesner, A. (2013, June). The Internet of things and the future of manufacturing [online interview]. McKinsey & Company. Retrieved from https://www.mckinsey.com/business-functions/ digital-mckinsey/our-insights/the-internet-of-things-and-the-future-of-manufacturing Loucks, A. R. (2008). A study of project management: The exploration and recognition of a culture within the profession. McCann, J., & Selsky, J. W. (2012). Mastering turbulence: The essential capabilities of agile and resilient individuals, teams and organizations [Kindle ed.]. San Francisco, CA: Jossey-Bass. McHugh, O., Conboy, K., & Long, M. (2012). Agile practices: The impact on trust in software project. IEEE Software, 29(3), 71–76. doi:10.1109/MS.2011.118 Melton, C. (2004). Lessons in project management. Software Quality Professional, 7(1), 47. Nerur, S., Mahapatra, R., & Mangalaraj, G. (2005). Challenges of migrating to agile methodologies. Communications of the ACM, 48(5), 72–78. doi:10.1145/1060710.1060712 Olsson, H. H., Alahyari, H., & Bosch, J. (2012). Climbing the “stairway to heaven” - A multiple-case study exploring barriers in the transition from agile development towards continuous deployment of software. In 2012 38th Euromicro Conference on Software Engineering & Advanced Applications (pp. 392-399). doi:10.1109/SEAA.2012.54 Owen, J., & Burstein, F. (2005). Where knowledge management resides within project management. Hershey, PA: IGI Global. doi:10.4018/978-1-59140-351-7.ch009

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Özdemir, V., & Hekim, N. (2018). Birth of industry 5.0: Making sense of big data with artificial intelligence, ‘The Internet of things’ and next-generation technology policy. OMICS: A Journal of Integrative Biology, 22(1), 65–76. doi:10.1089/omi.2017.0194 PMID:29293405 Pedersen, M. (2013). A quantitative examination of critical success factors comparing agile and waterfall project management methodologies (Order No. 3602588). Available from ProQuest Dissertations & Theses Global. (1468678982). Pinkham, M. (2017). Industry 4.0: The digitalisation of manufacturing. Metal Bulletin, 39-41. Project Management Institute. (2017a). Agile practical guide. Newtown Square, PA: Project Management Institute. Project Management Institute. (2017b). A guide to the project management body of knowledge (6th ed.). Newtown Square, PA: Project Management Institute. Raczka, M. (2015). Becoming agile. PM Network, 29(8), 70–70. Randall, R. M. (2014). Agile at IBM: Software developers teach a new dance step to management. Strategy and Leadership, 42(2), 26–29. doi:10.1108/SL-01-2014-0003 Reynolds, H. (2016, January). Big data and cognitive computing-Part 1. KM World, 25(1), 6-7. Retrieved from http://www.kmworld.com/Articles/Column/Cognitive-Computing/Cognitive-computing-Big-dataand-cognitive-computing–Part-1-108248.aspx Russell, M. (2012, November 30). Waterfall project management - Can it work for you? Retrieved from http://execprojectmanager.com/waterfall-project-management Serrador, P., & Pinto, J. K. (2015). Does agile work? A quantitative analysis of agile project success. International Journal of Project Management, 33(5), 1040–1051. doi:10.1016/j.ijproman.2015.01.006 Sherehiy, B., Karwowski, W., & Layer, J. K. (2007). A review of enterprise agility: Concepts, frameworks, and attributes. International Journal of Industrial Ergonomics, 37(5), 445–460. doi:10.1016/j. ergon.2007.01.007 Sidky, A. (2007). A structured approach to adopting agile practices: The agile adoption framework. Špundak, M. (2014). Mixed agile/traditional project management methodology. Reality or illusion? Procedia: Social and Behavioral Sciences, 119, 939–948. doi:10.1016/j.sbspro.2014.03.105 Stankovic, D., Nikolic, V., Djordevic, M., & Cao, D. (2013). A survey study of critical success factors in agile software projects in former Yugoslavia IT companies. Journal of Systems and Software, 86(4), 163–1678. doi:10.1016/j.jss.2013.02.027 Suttle, R. (n.d.). What is the initiating process in project management? Chron.com. Retrieved from http:// smallbusiness.chron.com/initiating-process-project-management-36001.html Thomas, M., & Adams, J. (2005). Adapting project management processes to the management of special events: An exploratory study. Academy of Strategic Management Journal, 4, 99–114.

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Turk, D., France, R., & Rumpe, B. (2005). Assumptions underlying agile software-development processes. Journal of Database Management, 16(4), 62–87. doi:10.4018/jdm.2005100104 Wysocki, R. (2006). Effective software project management. Indianapolis, IN: Wiley Publishing. Young, T. L. (2007). The programme and project processes and techniques. In Executing the project work (Ch. 9, Revised, 2nd ed.). London, UK: Kogan Page.

ADDITIONAL READING Adams, S. L., & Anantatmula, V. (2010). Social and behavioral influences on team process. Project Management Journal, 41(4), 89–98. doi:10.1002/pmj.20192 Chonko, L. B., & Jones, E. (2005). The need for speed: Agility selling. Journal of Personal Selling & Sales Management, 25(4), 371–382. Golden-Biddle, K. (2013). How to change an organization without blowing it up. MIT Sloan Management Review, 54(2), 35–41. Hodgson, D., & Briand, L. (2013). Controlling the uncontrollable: “Agile” teams and illusions of autonomy in creative work. Work, Employment and Society, 27(2), 308–325. doi:10.1177/0950017012460315 Ibbs, C. W., Kwak, Y. H., Ng, T., & Odabasi, A. M. (2003). Project delivery systems and project change: Quantitative analysis. Journal of Construction Engineering and Management, 129(4), 382–387. doi:10.1061/(ASCE)0733-9364(2003)129:4(382) Lydon, B. (2017). Industry 4.0 for Process. InTech, 64(3), 10–15. Maruping, L. M., Venkatesh, V., & Agarwal, R. (2009). A control theory perspective on agile methodology use and changing user requirements. Information Systems Research, 20(3), 377–399. doi:10.1287/ isre.1090.0238 Pfeffer, J., Kilmann, R. H., Pondy, L. R., & Slevin, D. P. (1977). The management of organization design: Strategies and implementation, Volume 1. Administrative Science Quarterly, 22(4), 677–682. doi:10.2307/2392410 Tynan, B., Adlington, R., Stewart, C., Vale, D., Sims, R., & Shanahan, P. (2010). Managing projects for change: Contextualized project management. Journal of Distance Education, 24(1), 187–206. Yi, Z., & Sang-Hoon, L. (2009). Implementation of project change management best practice in different project environments. Canadian Journal of Civil Engineering, 36(3), 439–449. doi:10.1139/L08-138

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KEY TERMS AND DEFINITIONS Agile Methodologies: A framework with four ideologies: (1) response to change; (2) adaptive planning; (3) speedy delivery; and (4) constant improvement. This project management approach is implemented through software development. Cloud Computing: An Internet service platform (generally, a pay for storage). Cognitive Computing: A system that analyzes data by looking for and identifying potential conflicts, patterns, solutions, and suggestions. Cyber Physical Systems: A system used to manage projects like robotics, cloud computing, and other autonomous frameworks. Industry 4.0: Referred to as the integration of computers and automation, Industry 4.0 is the meeting of autonomous computer systems. Project Management (PM): Application of tools, templates, and skills to ensure that an organization achieves a predefined objective within budget, scope, and schedule. Software Development: A set of processes used for testing, designing, conceiving, and fixing frameworks and/or other software applications and components. Waterfall: First introduced in 1970 by Dr. Winston W. Royce, this software development uses a particular cascading of steps.

This research was previously published in Agile Approaches for Successfully Managing and Executing Projects in the Fourth Industrial Revolution; pages 1-19, copyright year 2019 by Business Science Reference (an imprint of IGI Global).

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Strategic Management in SMEs in Industry 4.0 Mustafa Atilla Arıcıoğlu https://orcid.org/0000-0001-6477-832X Necmettin Erbakan University, Turkey Büşra Yiğitol Konya Food and Agriculture University, Turkey

ABSTRACT It is envisioned that the fourth industrial revolution contains many concepts such as modern automation and production systems, data collection, data processing, analysis, and data transfer and consists of intelligent factory applications such as augmented reality, the internet of things, cyber physical, and cyber security systems. It reveals the fact that a new era awaits enterprises in the relationship between technology and production due to these predictions for future changes. SMEs are one of the important segments that these triggers, which are the precursors of structural change, will affect. So how will SMEs experience the Industry 4.0 process? What do unmanned factories mean for SMEs? Which countries/ SMEs will have the Industry 4.0 technology and Industry 4.0 infrastructure which require high capital, Which of them will create opportunities? In this chapter, the problems that SMEs will face in the digital transformation process and the political and strategic approaches that can be developed to deal with these problems will be evaluated.

INTRODUCTION Toffler describes his book “Shock” (1970-p.10-12), “This book tells what happens to people who are changing”. It focuses on our reasons for adopting or failing to adapt to the future. Much has been written about the future. Nevertheless, much of what is written about the world of the future is the product of a simple and rigid approach. However, Toffler (1970) says that the following pages are about the humane and “soft” side of tomorrow and the speed of change in our age is a fundamental force in itself. Furthermore, Toffler (1970) continues his words “this accelerating impulse has personal and psychoDOI: 10.4018/978-1-7998-8548-1.ch077

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 Strategic Management in SMEs in Industry 4.0

logical consequences as well as sociological consequences “. These words are not said by targeting at any time. In other words, Toffler (1970) explains how the strategy should be learned and taught and explains that if the more accurate the concept of the future is learned in the dynamic structure of time, the more harmonious it will be. Toffler’s words and a significant part of his writings in 1970 are meaningful before and after the time he wrote. It reminds us that the concept that will govern the change in the future is a strategy, and when the existence of the strategy is considered, it is a combination of a strong plan and smart application beyond being a prediction. Or strategy is change management itself. This issue is not clear whether this change is humanistic and soft as Toffler said, or is rigidity as Taylor said. The Industry 4.0 process or concept introduced by Hannover 2011 raises more new questions than the increasing amount of discussions and inferences in the constantly renewed scientific literature. The efforts to understand and answer the questions are carried out through large scale enterprises rather than small scale enterprises. When the concepts are tried to be understood or executed, the concepts that will come up are: the fourth industrial revolution or digital transformation, modern automation and production systems, data collecting, data processing, analysis, and data transfer, augmented reality, Internet of Things, cyber-physical and cybersecurity systems, and integration of intelligent factory applications. In this case, it will be inevitable to discuss what strategy should be developed for SMEs in the context of Industry 4.0.

INDUSTRY 4.0 Bringing together energy, raw materials, technology, and human beings forms the basis of the theoretical structure of industrialization. Although human beings are more binding and governing rather than being a component, he constantly seeks space for himself in the existence of the first three elements. In the theories of organization, while pioneers of classics see man as a part of the machine, Bourdie considered production as a broad conceptual basis from the capital to strategy, and Galbraith thinks that making human beings ordinary and absentaneous in the development of the industry is as “the age of doubt”. The privatization and naming of the space include a new perspective with the development of the industry. the rationale for scaling and valuation also emerged with industrialization. This process led to emerging of workshops and then the factories and then the existence of integrated facilities in the rapidly future. electricity, sheet metal, motor and the story of engineer/technician/worker develops through these readings. The existence of speed is a time measurement for large-scale enterprises, whereas for smallscale enterprises, it means the limit between asset and absence is narrowed. SMEs who have survived with the first three revolutions thanks to their curvature, adaptability, and mobility, are preparing for a new but perhaps more difficult test.

What is the Concept of Industry 4.0? When the concept of industry 4.0 is searched in the literature, the followings can be seen; § § §

Big Data and Analytics Autonomous Robots Simulation 1549

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§ § § § § §

Internet of Things (IoT) CyberSecurity Augmented Reality Cloud Computing Additive Manufacturing Horizontal and Vertical System Integration

The definition of the fourth technological revolution is based on the general concepts and technologies such as the above concepts • • • • • • • •

Big data: including data collection, storage, analysis, and data collection Robots; autonomous, flexible and highly efficient machines capable of working in collaboration with people in their production processes) Simulation: enactment of the production system in a virtual environment, identification and forecasting of behaviors and movements. Internet of Things: RFID, Wi-Fi, such as Bluetooth, technological applications through the technologies within the manufacturing process to communicate with each other and to be able to work in a coordinated state. Cybersecurity: technologies that involve data collection, storage, and security that are becoming more important with digital transformation. Augmented reality: technologies that allow products to be produced and tested in a virtual environment. Cloud computing: technology where data is stored and transformed into information when needed. Additive production: production can be done with the help of three-dimensional (3D) printers.

It is possible to summarize the story of the process until this conceptual flow: I. industrial revolution is a process that began in England in 1763 with the discovery of the steam engine by James Waat and the invention of vehicles and then spread to Europe and the United States. With this process, mechanization has started to spread rapidly, there has been a radical change in production and it has affected both the economic world and the social structure. This process lasted until the 1830s. The period of the II Industrial Revolution, which coincided with the years 1840 to 1870, was called the technology revolution. With the development and use of electrical technology in machinery, machines were developed and production started in large quantities. The foundations of III. Industrial Revolution was laid in the 1950s when digital technology developed. During this period, computer and communication technologies developed and these technologies gradually started to be included in human life. IV. The Industrial Revolution, also known as Industry 4.0, was first introduced by Bosch at the Hannover Fair in 2011, and the machine-machine and machine-human knowledge are discussed through the relationship between numbers and artificial intelligence. In the report prepared by TUSIAD and Boston Consulting Group (BCG), The radical change in the paradigms of the business world is explained through environmental factors and it is suggested that we should understand this change with four main trends (TUSIAD and BCG, 2016): §

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Regional trends - Increase in social interaction and trade between countries

 Strategic Management in SMEs in Industry 4.0

§ § §

Economic trends - Rising new strong economies and increasing globalization with financial resource flows Technological trends - Increased connectivity and development of platform technologies Meta trends - increasingly scarce resources, increasing concerns about the environment and safety

Figure 1. Historical overview of industrial development (Kesayak, 2019)

Figure 2. Trends shaping the future of the world (TUSIAD and BCG, 2016)

For the question of what these reasons teach for Industry 4.0, it is first necessary to ask the opposite question: For who? For what? These two questions are valuable. Because the Industry 4.0 process is not only an increase in productivity, it is a journey that creates higher value-added, creates its economy, fundamentally changes the established value chains and most importantly, it reaches a much more important point in the need of qualified manpower (TUSIAD and BCG, 2016). In other words, the objectives of Industry 4.0 are to provide a higher level of operational efficiency and productivity and also to achieve a higher level of automation (Thames and Schaefer, 2016). The system accommodates fewer people, allows shorter production times and it allows the factory areas to shrink. However, in contrast to this shrinkage, the variety of products produced, the quality and quantity of production increased and new products are introduced to the market rapidly. It takes into account revolutionary developments in the design and manufacturing processes, operations and services of manufacturing and product systems (Tjahjono et al., 2017These features are not only closely related to

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internet technologies and advanced algorithms but also show that Industry 4.0 is an industrial valueadded and knowledge management process (Lu, 2017). In other words, Industry 4.0 technologies enable continuous interaction and exchange of information not only between people but also between people and machines, even between machines (Cooper and James, 2009) and continuous communication over the internet. The following 5 main features are mentioned in industry 4.0 (Roblek et al., 2016; Posada et al., 2015); • • • • •

Digitization, optimization, and privatization of production Automation and adaptation Human-machine interaction Value-added services and businesses Automatic data exchange and communication.

In this context, while discussing the components of Industry 4.0, a common definition has not yet been reached. Drath and Horch (2014) refer to the fourth industrial revolution and It is understood as the application of general concepts of cyber-physical systems (CPSs) on industrial production systems (cyber-physical production systems). In some sources, the concept of industry 4.0 is defined as the use of advanced data analytics for transformational business results, and as the term used for the internet of things, machines, computers, people involving the realization of intelligent industrial processes (IIC, 2015). There is vital application of data analytics in the era of industry 4.0 where polluted information is prevalent in nature (Iqbal & Nawaz, 2019). According to Iqbal & Nawaz (2019), drawback of industry 4.0 is rising quantity of polluted information. Tjahjono et al. (2017) define industrial 4.0 as a global transformation of the manufacturing industry with the introduction of digitalization and the Internet. On the other hand, Müller et al. (2018) approached industry 4.0 from a technical, economic, ecological and social framework and included the concept of industry 4.0 as an economic process in which processes are transparent and interconnected, enabling optimization by increasing efficiency, flexibility, quality and personalization. Beyond a common definition, if we discuss concepts that created a definition and the content of the process, we find ourselves amid the actual debate. The theme of this is what has been said about the expected positive and negative aspects of Industry 4.0. The features that Müller et al. (2018) have compiled from various articles are summarized in Table 1. In general, apart from this summary table, it is possible to explain the positive and negative characteristics of industry 4.0 in the following items. •

•

•

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Industry 4.0 enables the innovation of existing business models (Arnold et al., 2016). Not only does it increase global competitiveness, but it also enables strategic differentiation in new business models (Laudien et al., 2017). Therefore, it poses a threat to existing business models. It is also an opportunity to create new business models with a change in the business strategy of the enterprise. Industry 4.0 not only provides enterprises with innovation on competitiveness business strategies but also enables them to gain strategic value in global competition (Brettel et al., 2014). businesses gain the opportunity to create value for the customer through data tracking and analysis and the active use of this information in the improvement of production processes. While Human-machine cooperation and even communication and coordination between machines ensure speed and flexibility in production processes (Stock and Seliger, 2016), the quality of

 Strategic Management in SMEs in Industry 4.0

Table 1. Positive and negative aspects of Industry 4.0 (Müller et al., 2018) Possible Opportunities

Possible Threats

New business models with Industry 4.0

Existing business models at stake

New value proposals for improved competitiveness

Loss of flexibility

Increasing productivity

Standardization

Reduce costs

Transparency

Higher quality

High implementation efforts such as costs and standardization

More speed and flexibility

Employee Fears and Concerns

Load balancing and inventory reduction

Lack of expertise

Reduction of monotonous studies Reducing environmental impact

•

•

•

•

products produced (Meyer et al., 2011) and productivity increase is expected. In this case, also it requires less manpower and provides cost advantage as it allows a reduction in error rate. The inclusion of information and communication systems in the industrial network also leads to a sharp increase in the degree of automation (Sanders et al., 2016). Intelligent and self-optimizing machines on the production line can synchronize themselves with the entire value chain, from ordering from supplier to receipt and delivery of goods to customers (Spath, et al., 2013). Providing automation in production processes provides ease of balancing in terms of production planning. Based on the fact that Industry 4.0 is a radical change process, it provides real-time production planning based on dynamic optimization in contrast to conventional forecasting based production planning (Sanders et al., 2016). Since manufacturers have the advantage of calculating the number of products produced per unit time at a rate close to zero error, they can perform production rapidly according to the demand situation and reduce their product stocks (Oettmeier and Hofmann, 2017) and carry out resource planning effectively. Thanks to devices and processes such as artificial intelligence, internet of things, cloud computing and 3-d technology provided by Industry 4.0, employees’ motivation and work efficiency increases (Hirsch-Kreinsen, 2014). Accordingly, there are no focus and satisfaction problems in getting rid of monotonous and uniformity. Intelligent and autonomous production systems provide higher employee satisfaction and motivation for paying more attention to monotonous and repetitive tasks (Müller et al., 2018). Besides, environmental sustainability can be achieved through production processes that reduce environmental hazards to a lesser extent, particularly reducing the carbon footprint (Peukert et al., 2015). The disadvantages of Industry 4.0 in general under current conditions and in the future for enterprises and employees are the lack of validity of the existing system and thus the endangering of the assets of the existing systems. Digitization and data monitoring and recording, as well as automation and communication technologies, increase the risk of eternal cyber attacks. Transparency of each business activity through online platforms increases the risk of attack and causes them to become open targets for external threats (Müller et al., 2018). Therefore, the necessity of data security applications increases.

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• •

•

In terms of cost, the digitalization process requires new technological structuring in industrial production processes. Conducting activities through adaptation through the existing system is both a challenging and cost-increasing situation for enterprises (Müller et al., 2018). Industry 4.0 and the digitalization and mechanization lead to the most questionable questions in terms of employment. Personnel competencies and competencies required for the use of new technology, such as data, network technologies, data analysis and processing (Erol et al., 2016), can be alarming for employees and increase the concern for a job loss of existing personnel. The fact that employers are not able to provide qualified personnel or do not provide the necessary training to existing personnel can lead to a complex situation (Kiel et al., 2017). Industry 4.0 enables companies to gain important information about the methods to be applied in their internal processes and thus to increase added value for companies (Safar et al., 2018). However, as in any system or the process of change, the age of digitalization contains opportunities and threats. Therefore, companies need to analyze how and what the impact and opportunities will be on the sector and business models in which they operate in this transformation journey and to follow a technological roadmap and draw a strategic roadmap.

SMEs in Industry 4.0 While being demand medium-high technology investment for a new period based on technology and production relationship, this prerequisite will lead to the difference between companies and countries. In other words, while the management and production of technology lead to geometric growth, it is the harbinger of structural change.

*Concept of SMEs According to Turkey SME Definitions, Small and medium-sized enterprises (SMEs) employ less than 250 workers annually and have an annual financial balance not exceeding TL 250,000,000 (two hundred and fifty million) and are classified economic unit as micro-sized enterprises, small-sized enterprises and medium-sized enterprise (Wikipedia, 2019). Small and medium-sized enterprises (SMEs) are generally owned and controlled by an entrepreneur and a significant portion of the hierarchical decision-making and executive bodies in SMEs are family members (Arıcıoğlu and Yiğitol, 2019). Table 2. Comparison of the Turkey SME Definitions (Small and Medium Enterprises Development Organization of Turkey, 2005) Size Category

Staff headcount

Turnover

Medium-sized

50-250

≤ 125 Million TL

Small

10-49

≤ 25 Million TL

Micro

1-9

≤ 3 Million TL

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Table 3. Comparison of the EU SME Definitions (European Commission, 2019) Company category Medium-sized

Staff headcount < 250

Turnover ≤ € 50 m

Small

< 50

≤ € 10 m

Micro

< 10

≤€2m

The concepts commonly used in the definition of SMEs are related to the qualitative and quantitative characteristics of SMEs such as their share in the sector, their degree of expertise, manager-owner relations, managerial problems, quantity of capital, production amount, total assets, sales volume (Arıcıoğlu and Yiğitol, 2019; Özgener, 2003). Small and medium-sized enterprises (SMEs) are considered as the backbone of the economy because they are strong as employers; therefore, they attract the attention of both policymakers and scientists (Safar et al., 2018). Table 4. SWOT analysis results of SMEs (Kalpande et al., 2010) Opportunities Expert market Support of government Raising the ceiling for SMEs. Government reserves of material items. Strengths Flexibility Owner manager Low labor Low costs Simple structuring Cooperation with employees

Threats Financial tightness. Technological obsolescence. Ignoring the mechanic preparation Increase in the cost of information resources Political peace and instability. Weaknesses Lack of quality consciousness. Lack of financial strength. Lack of correct business culture. The lack of educated workers. Lack of technology superiority Lack of proper management tendency. High score of key personnel. Missing planning. Lack of long-term strategic focus.

The fact that SMEs have various structural problems makes it difficult for them to keep up with the change. Especially when changes in information and communication technologies, complex business life, new business models and globalization are added to the environmental dynamics, the insufficiency of the knowledge and skills of the owner and family members becomes more difficult to adapt to the transformation process. SMEs may experience some problems, especially in the application of new technologies and adaptation of these technologies to business models compared to large enterprises. Some of these problems are the lack of resources, skills and time (Anonymous, 2018). In the report that prepared for SMEs by The Interreg North Sea Region (NSR) project GrowIn 4.0 (2018 shows the barriers to SMEs in general; •

Lack of commercial support,

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• • • • • •

Be cautious about investments in Industry 4.0, Lack of correct qualifications, Lack of digital standards, Threats due to cybersecurity, Lack of funding Lack of the right business tools. Opportunities mentioned in the report (Anonymous, 2018);

• • •

Efficiency increasing effect Increase competitiveness and support global competitiveness Growth support

SMEs are again attracting the most attention in the Industry 4.0 digitalization process and are asking questions about the process. So how will SMEs experience the Industry 4.0 process? What do unmanned factories mean for SMEs? Which countries/SMEs would have opportunities thanks to the Industry 4.0 technology and Industry 4.0 infrastructure that require high capital? Will new developments require a new model of cooperation? Does Industry 4.0 have a different sectoral transformation foresight? The answers to all these questions highlight five points for SMEs: •

• • • •

Firstly, the trend and preference of unmanned factories (especially some countries such as the USA, Germany, Japan, Korea prefer this way while being competition with China which uses the cheap labor argument) are likely to lead to changes in the SME scale and classification. In particular, the micro-scale and the small-scale are referred to together, while the medium-scale seems to be considered as a separate class. It is inevitable that SMEs, which are an important supplier and support element for the main industry in international competition, will turn to new technology-based collaborations. The risk areas of the medium-sized companies that seek to achieve global adaptation by producing their technology independently from their countries or transferring them from the international market will expand. A new type of network-oriented and innovative technology / SMEs will emerge. Ultimately, the digital transformation will change the way SMEs do business and organizational structures, and those who cannot adapt to change will accelerate and gradually disappear.

It is not surprising to anticipate that SMEs will have a challenging test with industry 4.0 based on these expected developments. In this context, SMEs need to make their positions regarding the digital transformation process especially according to their position in the supply chain. Solidarity from the increased competition is a necessity for the successful management of the industry 4.0 process. While the high cost of using and transferring technology through solidarity/collaboration is minimized, S and M synchronism in the regional context can be an important opportunity for competitive advantage. It should be noted that the adaptation process required for SMEs should also be discussed through new business models that will emerge, and that sectors must perceive this change in different ways. In particular, the change in production-based works and the privilege of the service sector in this context should be monitored more carefully. It can even be foreseen that the change process related 1556

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to agriculture should be monitored more carefully. Because the distribution of countries and concrete outputs related to the transformation of the manufacturing sector are seen more clearly, in the service and agricultural sector, it will not facilitate the harmonization of country differences and transformation between countries or companies. In the service and agricultural sector, it will not facilitate the harmonization of country differences and transformation between countries or companies. While the emergence of employment policies in labor-intensive agricultural areas and increasing political concerns are some of the barriers, the difference in farmer’s asset level in agriculture is an important obstacle to the globalization of investment in this field. It is expected that the transformation in the services sector will be faster in developed economies and slower in others. In the context of Industry 4.0, it is possible to list the triggering forces and restrictive factors as follows (Anonymous, 2018); Triggering forces; § The power of in-house triggers, such as reducing production costs, improving the quality of products and services, increasing employee productivity and reducing production time § The power to increase the efficiency of the production system and to provide the advantage of timely production differentiation in the market § The power of the legal requirements of sectors such as construction and health, and the power of legal triggers for environmental and sustainability § The power of the market due to the changing needs of the market; meeting the changing demands, reduction in waste amounts, the success of intelligent automation systems in mass production § The power of technological changes; increased access to digital technologies, especially in recent times, makes the implementation of industry 4.0 more accessible Restrictive factors; § Lack of support in adapting to new technologies § Excessive prudence on investment § Lack of talent in the workforce § Slow development of digital standards, § Information security / cybersecurity concern § The problem of access to finance § The low share of technological investments in investments In this new process that SMEs will face, some factors that play a role in terms of sustainability are preference and promotion of public policies, large/global enterprises’ process orientations and pressures, special cases of the sector, competitors’ position in this process and ultimately the preference of enterprises to use such investment resources. Factors affecting the application of Industry 4.0 elements in small and medium-sized enterprises (SMEs) can be grouped as follows (Vrchota et al., 2019);

Literature Review Related to SMEs in the Context of Industry 4.0 Nuroğlu and Nuroğlu (2018a; 2018b) indicated that industry 4.0 transformation is closely related to that SMEs capture the ındustry 4.0 process in countries such as Turkey that the majority of manufacturing 1557

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industries network consists of SMEs. In general, the appearance in SMEs is described as uneasiness regarding the process. The factors that they deem critical in this process of digitalization; to train consultants to support the digital transformation of SMEs and to open digital transformation centers, to be target-based rather than uneasiness, to be more supportive for SMEs in focus sectors such as chemicals and pharmaceuticals, motor vehicles, machinery and equipment, semiconductors and electronics and food and beverage, and to include Clustering model in support program. Table 5. Factors influencing the introduction of Industry 4.0 elements in small and medium-sized enterprises (SMEs) (Vrchota et al., 2019) Machine

Labor

Technical information (Know-how)

Financial planning

Process management

External factors

Services

Recruitment

Opinions

Resources

Personal Characteristics of Managers

Development

Product and Machine Quality

Development

Market Opportunities

Risk management

Process Tuning

Market

Access to Services

Qualifications

Integration Opportunities

Productivity

Corporate Social Responsibility

Government

Optimal Location

Workforce Resources

Rewarding System

Competition

Critical Points for Sustainability

Ecology

Production Chain Security Level

Table 6. SWOT Analysis of SMEs in the context of Industry 4.0 (Created by authors) Opportunity Reduces production costs Easy adaptation to market demands Flexibility Differentiation in the market Increases productivity Increases competitiveness Provides growth Strengths

Flexible structures The vision of technological enterprise Easy adaptability Entrepreneur spirit Cost advantage in Industry 4.0 technologies due to their small size Competitive advantage with collaborative structures Ability to work with low labor Ease of internal communication

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Threats Ability to survive in a new competitive environment due to faster adaptation by large scale firms Information security More financing needs Cost-increasing feature in the installation The threat to the existing order and business models Employee anxiety and concern Failure to take part in the supply network Weaknesses Lack of resources, skills and time Lack of support in adapting to new technologies Excessive prudence on investment Lack of talent in the workforce Slow development of digital standards, Information security / cybersecurity concern The problem of access to finance The low share of technological investments in investments Lack of vision of managers Unaware of the strategic management process Lack of strategic awareness Access to databases

 Strategic Management in SMEs in Industry 4.0

Cevik (2019) evaluated the applicability of Industry 4.0 in SMEs from the managers’ perspective. In a study conducted in Turkey, it is stated that SMEs are not ready and will have difficulty in providing the necessary information equipment and technological infrastructure in the context of Industry 4.0. Also, the areas where small/medium-sized enterprises have the highest expectations due to new technology applications are as follows; to gain innovative production concepts and to produce better quality products, to make improvements in non-value added works and to help institutionalization of enterprises. Üçler and Vayvay (2018) evaluated the application of Simultaneous Engineering Approaches in SMEs. A simultaneous engineering approach is based on the logic of product and process development of experts and managers from different disciplines by creating cross-functional teams working together simultaneously. Therefore, it is stated that it supports Industry 4.0 studies depending on these characteristics. It is noted that it also supports Industry 4.0, especially because it focuses on horizontal collaboration and integration of distributed technologies with digital technologies (Brettel et al., 2014). Apilioğulları (2018) examined the relationship between the concepts that should be applied to SMEs to be successful in the digital transformation process. In this study, a road map model is tried to be outlined. As a result of this study, it is underlined that a systematic approach and methodology should be followed in the process of digitalization, the importance of strategic approach, that companies should not be involved in the process without examining, interpreting and constructing standard business processes, and that it is necessary to be informed for setting standards and technological change. The aim of Safar et al., (2018) is to design a business organization model for existing or newly established SMEs considering traditional approaches to business models and Industry 4.0 requirements to guide SMEs in the early stage of industry 4.0 implementation. In the model obtained as a result of the study, it is stated that § § § §

additional software and cloud information technologies will be required for both existing and new initiatives, this planning should be made at the beginning, enterprises should make investments that require a high amount of financing even if they have existing investments, production costs and times will shorten as a requirement of the current notion of the smart world

The study also emphasized the need to adapt to new technologies not only for enterprises but also for other market participants such as banks, tax offices, and other institutions to participate in the global network. Vrchota et al. (2019) aimed to identify the factors that affect small and medium-sized enterprises (SMEs) in the Industry 4.0 process. In general, the study was carried out concerning the factors that limit the integration of SMEs into Industry 4.0 in contrast to the factors that may be impulsive for SMEs. The results showed that the awareness of Industry 4.0 is related to the size of the enterprise. While the majority of medium-sized enterprises are considering implementing digitalization and robotization technologies over the next 5 years, this has been observed in less than half of the micro-enterprises. In the report presented by Schröder (2016), it is covered that review of the literature showing the application of Industry 4.0, typical barriers and challenges in the implementation of Industry 4.0, highlighting the importance of engaging employees in increasing the success of innovation processes in the company. The report provides policy recommendations for overall adaptation and improvement of the process.

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In their study, Sevinç et al. (2018) analyzed the push forces of Industry 4.0 adopted in small and medium-sized enterprises. By analyzing the difficulties in the transition process of small and mediumsized enterprises (SMEs) to Industry 4.0, it contributes to the determination of strategic steps by considering these results. In the study, a hierarchical structure was established under the four main criteria (innovation, organization, environment and financial aspects) and the relative weight of these criteria and sub-criteria was calculated. The surveys conducted on business managers were evaluated using the analytic hierarchy process and analytical network process and multi-criteria decision-making methods. Müller et al. (2017), in their work, discuss the cooperation strategies of SMEs to overcome the lack of resources and information. In this context, jointly developing business models as well as commonly purchased technologies are considered appropriate strategies for SMEs in this process. In this study, the advantages of cooperation between SMEs are discussed. With this cooperation, while providing a cost advantage to small and medium-sized companies, it is mentioned that benefits such as facilitating information and data flow, synergy formation between enterprises, resource transfers and technical information transfer can be provided a competitive advantage.

SMEs in Industry 4.0: Strategies and Policy Effective implementation of strategy and strategic management differs according to many characteristics such as the size of enterprises, sector, organizational structure and managerial capabilities besides strategic awareness or long-term vision in the process (Arıcıoğlu and Yiğitol, 2019). Therefore, when compared to large scale enterprises, the understanding of strategic management in SMEs differs. In particular, SMEs being local-oriented and ownership and management functions being carried out by the owner/entrepreneur are effective in determining the concept of strategy and the scope of strategic management. In this context, it is not wrong to define the concepts of strategy and strategic management in SMEs as an informal character that includes personal qualities, experience, creativity and intuition in a dynamic and heterogeneous work environment (Todorov, 2014). In other words, it is not possible to talk about comprehensive strategic behavior independent of the manager for SMEs. Strategy formation and decision-making are in the hands of the owner/entrepreneur, and this process is usually carried out for individual purposes (Morden, 2007). In short, it is possible to talk about operational activities and efficiency for SMEs, not strategic management for SMEs. Besides, it is possible to talk about a strategic understanding that can be used either as a follower of the strategies of industry leaders (large scale enterprises) in SMEs or by forming partnerships or clusters. Based on these explanations, it is important to discuss which strategic behaviors and policies SMEs will adopt and implement in the industrial 4.0 process and how they can survive in the competitive environment of the digital industry. Particularly, as the industry tries to build a new world with a digital transformation, the components of new balances in competition and the sustainability concern of labor-oriented sectors/enterprises become obvious hard questions for SMEs. With Industry 4.0, possible outcomes are seen from the SMEs perspective: • • • • • 1560

A more planned business life Learning innovative business forms Return of cost advantage to technology in labor-intensive areas Obligation to register Difficulty in acquiring data habit

 Strategic Management in SMEs in Industry 4.0

• •

More efficient time and production management for deadlines From a supplier role perspective, some reasons such as the transfer of the main industries to unmanned factories and outsourcing and logistics costs will lead to a narrower / regional selection of the supply network. This is inherently at risk of expulsion/withdrawal of SMEs within supply networks.

Industry 4.0 is a digitalization based on information technologies and technological investment is required and SMEs need to overcome the lack of resources and information. In this context, in addition to widely purchased technologies, jointly developing business models, ie collaborations, have strategic importance. Successful and trust-based cooperation provides a cost advantage in technological investments as well as the advantage of distributing the possible risk rather than carrying it on one shoulder (Müller et al., 2017). Collaboration facilitates the exchange of ideas and facilitates synergies between SMEs. Mutual exchange can not only benefit one partner but can also provide benefits to all collaborating partners (Müller et al., 2017). Other advantages of cooperation are applications that can provide strategic advantage such as focusing on core competence, sharing technical knowledge and transferring resources. Briefly, the industrial 4.0 revolution will develop new forms of cooperation and may further develop the areas of cooperation. Besides, while the main/large industry is now producing in unmanned factories, the competition of subindustry in terms of cost and price will gain a new dimension. Or the real question arises as follows: Is there a need for a sub-industry, and if so, what kind of a definition of a sub-industry emerges? One of the most important inferences that SMEs, which are the main actors of the economy, learned in the competition process is to determine their core competence and to be involved in this process in line with this core competency. The gains achieved to become more evident with Industry 4.0, but also this may involve new areas of risk with the transformation in production, and this situation makes enterprises necessitate to re-perform risk analysis. In this context, specialized works formats should be correctly understood and re-analyzed for each SMEs. Even, it should find answers to questions such as who saves flexible production, where should I be in the sector’s time planning, where should I be just in time, what should be the new ranges and technical equipment for efficiency measurement. Another very important title concerns everyone. What about humans? Will technology change, particularly in labor-intensive sectors, reveal new sets of solutions in production modes? new questions may be added with these questions asked since the beginning of the 18th century. this should be questioned with a new mind especially for each SME member of the cluster. Naturally, one of the important factors that will determine this can be explained by the direction and preferences that large scale enterprises give to their production styles. Defining the use of machinery through an integrated process and the presence of new mechanical villages consisting of factories open the discussion of the positioning of people again. It is seen that from Taylor to today, business owners and entrepreneurs looking for pure efficiency in factories will seem to pursue a new quest. One of the important factors at the center of the discussions about Industry 4.0 is the curiosity of what will happen to a human, however in the new system, there are discussions about the quality of employment rather than quantity. With the new system, technical workforce and expertise knowledge emerge as the star shining areas. Therefore, the evolving of the employment structure in this direction is an inevitable necessity. Several qualitative and quantitative competencies are deemed necessary in the labor force in the Industry 4.0 process. These abilities are indicated in table 5 (Anonymous, 2018) 1561

 Strategic Management in SMEs in Industry 4.0

PERSONAL

TECHNICAL

Table 7. Qualitative and quantitative competencies of labor in the Industry 4.0 process (Anonymous, 2018) Competencies that must be definitely

Competencies that should be

Competencies that may be

IT knowledge and usability

Knowledge management

Computer programming and coding capability

Data and information tracking and analysis

General and interdisciplinary knowledge about technology and organization

Knowledge of technological expertise

Statistical information

Expert knowledge on production activities and processes

Ergonomics awareness

Organizational and process understanding

Cybersecurity and data protection awareness

Awareness of legal affairs

Ability to interact with modern interfaces (human-robot, humanmachine)

Trust in new technologies

Adaptability to change

Conscious of continuous development and lifelong learning

Teamwork ability Social skills Communication skills

One of the important features of SMEs is the habit of keeping some or part of the economic activities off the record. Although there are reasons such as providing cost advantages or reducing the cost burden on sustainability, this preference is problematic in the legal context. However, digital transformation limits or even eliminates it. In this case, it is necessary to prepare for joining the economy and to determine the real profit/profitability targets with in-house transactions. Being an SME is a pre-growth scale. To know how and why it grows, it is necessary to keep the data record and calculate the magnitude over an accurate time frame. when companies are small scale, the more accurately planned and managed data retention habits, the more effective the power of future projection will be when they grow. Industry 4.0 now realizes process management through error-free production planning. Since production planning and process management related to production will be done by artificial intelligence at the next level of the production era, the Industry 4.0 process should be understood and adapted for SMEs as soon as possible. This can be overcome with an integrated program or process development that can be achieved through programming that used currently such as Similar to ERP, MRP. In the final analysis, with this process, it will not be surprising that SMEs will suffer losses within the framework of sectoral and regional factors and some of them will probably come to the final stage of their lives. Structural changes, flexibility advantages, and renewed perspectives are hints of how they can provide sustainability. Some of these will not be very easy in alone and it is possible to say that the collaborations will have life-saving importance in the new period. Even the forms of cooperation will develop and they will try to find a place in a strategic roadmap. Because togetherness has a life-saving feature especially for SMEs.

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SOLUTIONS AND RECOMMENDATIONS Interreg North Sea Region (Nsr) Project GrowınG 4.0 (2018) has prepared the following strategic behavioral recommendations for industrial SMEs; § § § § § § §

Define which business model will be used for improved or new offers. Create the technological basis such as tool base for analytics. Build the right organizational structure and capabilities. To develop the necessary partnerships in the digital world. Join and shape technological standardization. Improving technological infrastructure such as fixed and mobile broadband services. Infrastructure should be fast, secure and reliable enough for companies to depend on real-time data. Acquire the right skills and therefore adapt the school curriculum, education, and university programs, and strengthen entrepreneurial approaches to increase it-related skills and innovation capabilities of the workforce and improve the existing workforce.

It is seen that a new way of doing business is evolving from transfer to use of technology, from labor to process management. Differentiation in the structure of competition and differentiation İN the scales / classification of enterprises is another possible development. In this context, having these forms of behavior over collective intelligence and collective leadership will provide an advantage in terms of new forms of competition. It is possible to list the following predictions for new smes or renewed smes; • •

• • •

•

•

While there may be a new scale on SMEs, micro and small scales can be considered as a group and medium-sized enterprises can be considered as a separate group. Collaborative structures will be of great importance for SMEs to survive in the foreseeable future. Therefore, clustering studies are an important move in the application of Industry 4.0 technologies and breaking the competitiveness of large scale enterprises. It is a prediction that medium-sized companies will expand their risk areas due to both competition and human resources regarding the process management. The new economic order will allow new technology-based SMEs to derive. Choosing new business models instead of existing business models will be a necessity. When compared to other sectors, the manufacturing sector appears to be the sector that will be most affected and affected by this process. Considering the easier adaptation of the financial and service sectors with this process, it will not be misleading to foresee that online SMEs will become increasingly widespread in these areas. It is a very strong possibility that the existing production integrations of the factories will be operated as an internal factory system in the form of SMEs through outsourcing. As a matter of fact, in the existing automotive factories, the cost-oriented way of working as an internal supplier (integrated trials already exist) will become widespread by going one step further than employing the workers of the sub-industry companies The agricultural sector will determine the automation-based way of working as a new mode of operation, from crop planting to development, from development monitoring to harvest, the process will be possible with less employment and more technology. 1563

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•

SMEs, who need to keep up with the change in education areas, will learn to employ flexible ways of employing engineers or foreman integrated into engineering knowledge.

FUTURE RESEARCH DIRECTIONS Confronting with the future for SMEs goes at a geometric pace. this situation causes enterprises to discuss new process management/methods in the procurement process. Efforts to achieve sustainable success by using new tools of competition-oriented minds are adding new ones to the working areas. •

The fact that the factories gradually draw the supply network into the factory and re-integrate them with artificial intelligence, the operation of the factory units with outsourcing. The emergence of new forms of organization, Apart from the blue and white-collar in human resources, artificial intelligence users who will take part in the online process will become widespread, Online SMEs concept, Change of SMEs scales, The development of existing forms of cooperation and the emergence of new forms of cooperation, in particular, the use of cluster forms online, Fractal market forms of competition

• • • • • •

All of these are some of the most important issues to be studied.

CONCLUSION The concept of Industry 4.0 is a term that describes a process of digitalization of network and computer systems that encompasses the entire production process to the industry. With this digitalization, industrial automation and the production of highly mechanized products emerge as a dreamed or even partially realized future. In the anticipated future manufacturing processes, the equipment, machinery, materials and end products perceive the status of environmental conditions and process through sensors, communicate with embedded software, and thus optimizes the production process in an unprecedented way (Schröder, 2016). In this way, not only efficiency is provided in technological factories, but also it is possible to produce personalized products with higher quality, faster, less costly according to customer needs. Basic fiction is based on big data applications and data analysis. The emerging new business models allow the evaluation of the data provided and the processing and use of this data. The exchanger power of the new industrial world on enterprises is a deep-scaled and radical change. Therefore, it is not easy for companies to adapt to this process. New strategies for a sustainability need to be developed with the abandonment of existing schemes and the replacement of new business models and technologies. Otherwise, it is unlikely that enterprises that cannot take the right and timely step will be able to survive. The enterprises should take care of transformational dynamics in earlier and adapt business strategies accordingly. Furthermore, they need to have a good political, legal and infrastructural framework to keep up with the Industry 4.0 issue (Schröder, 2016).

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In this process, as well as large-scale enterprises, the situations of small and medium-sized enterprises appear as a critical issue. SMEs are an indispensable element for the economies of the country and play an important role in an economic revival, structural change, and adaptation to technological innovations (Cin, 2012). Especially in the process of change in industry 4.0, The role and the attitude of SMEs (due to the most important role as a backbone of the economy of many countries, including Turkey) in the process are important. However, the structural problems and administrative deficiencies of SMEs make it difficult for them to adapt to the change process. Especially the changes in information and communication technologies, complex business life, new business models and adaptation to environmental dynamics such as globalization are among the main challenges in this the change process. It is possible to summarize possible problems in the context of Industry 4.0 (Kleindienst and Ramsauer, 2016); Ø Horizontal Integration Perhaps one of the biggest problems of SMEs related to digitalization is the lack of specialized personnel in information technologies. SMEs generally specialize in basic software. However, to achieve horizontal integration, they need to gain expertise in different software that can interact with each other. The first step is to define special transfer parameters that will allow data exchange between different departments and companies, especially in creating value. Ø Consistency in Lifelong Engineering Applications The biggest limiting factor of not spreading models such as simulation, planning and descriptive models among SMEs is low awareness of such systems. Therefore, the possible advantages are often overlooked. SMEs need to be effective in such applications. Ø Vertical Integration and Cross-Linked Production Systems Specifically, provision, analysis, and evaluation of real-time data related to production, assembly, logistics or machiner is a situation that increases the flexibility of SMEs in the process of creating value. However, most SMEs do not have the skills to operate and maintain their systems. As can be seen, industry 4.0 technologies envisage radical changes in manufacturing processes such as the transition from the provision of simple components to complex engineering systems based on Cyber-Physical Systems. It requires real-time production planning as well as optimization. This enables a flexible and fast response to customer needs. thus, it can provide competitive advantages for SMEs. Reducing manufacturing times with Industry 4.0 applications can help them acquire the new skills needed to gain a competitive advantage on a global scale. Therefore, adapting to this process is as profitable as it is difficult to adapt. Both large-scale enterprises and SMEs are required to make an assessment and prepare themselves through the dynamics of this process. Otherwise, it is unlikely that some businesses will survive within the framework of sectoral and regional factors in the new competitive system. Structural changes, flexibility advantages and renewed perspectives are hints of how they can gain a place in the process and how they will provide sustainability. In this process, SMEs should do their strategic positioning well and take some strategic steps in a timely and accurate manner. Strategic and political steps to be implemented or to be considered; 1565

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• • • • • • • • • •

• • • •

•

Flexible organizational structures and business models should be adopted. Areas of expertise should be well defined Focus on core competence, Collaborative structuring should be given the strategic importance The position within the supply network must be maintained To be included in the formal economic system not Focus on production and process planning. The specialist workforce should be employed Integrated programs should be included in the production process SMEs’ owners/managers need to have strategic awareness. Managers should evaluate the dynamics, the opportunities, and threats of changing technology in the context of the structuring of their enterprises. Also, managers should ensure that smart choices and the right business models are selected and implemented. Priority should be given to making necessary investments in information technologies and making the recruitment of specialist workforce needed relevant technologies by either internally through training or externally through re-employment. Cybersecurity applications, secure interface technologies should be developed or these requirements must be met externally within the framework of the collaborative structure. Expertise is required for effective management of the logistics network. One of the last and perhaps most critical two factors is the establishment of consultancy centers that can provide planned specialist training to SMEs. Also to the establishment of these centers, these centers should be included in the collaborative network and permanent consultancy services should be provided. The other is the need to revise the educational activities to the desired criteria. Besides, the educational activities should be focused on establishing the expert workforce that enterprises can benefit from in their future employment.

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Morden, T. (2007). Principles of strategic management. Ashgate Publishing Limited. Müller, J. M., Kiel, D., & Voigt, K. I. (2018). What drives the implementation of Industry 4.0? The role of opportunities and challenges in the context of sustainability. Sustainability, 10(1), 247. doi:10.3390u10010247 Müller, J. M., Maier, L., Veile, J., & Voigt, K. I. (2017). Cooperation strategies among SMEs for implementing industry 4.0. In Proceedings of the Hamburg International Conference of Logistics (HICL) (pp. 301-318). Academic Press. Nuroğlu, E., & Nuroğlu, H. H. (2018a), Endüstri 4.0’ı Türkiye’nin Dış Ticareti İçin Bir Fırsat Penceresine Dönüştürmek. Yönetim ve Ekonomi Araştırmaları Dergisi, 16(1), 329-346. Nuroğlu, E., & Nuroğlu, H. H. (2018b), Türkiye ve Almanya’nın Sanayide Dijital Dönüşümü: Yol Haritaları Ve Şirketlerin Karşılaştırması. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 23, 1537-1560. Oettmeier, K., & Hofmann, E. (2017). Additive manufacturing technology adoption: An empirical analysis of general and supply chain-related determinants. Journal of Business Economics, 87(1), 97–124. doi:10.100711573-016-0806-8 Peukert, B., Benecke, S., Clavell, J., Neugebauer, S., Nissen, N. F., Uhlmann, E., & Finkbeiner, M. (2015). Addressing sustainability and flexibility in manufacturing via smart modular machine tool frames to support sustainable value creation. Procedia CIRP, 29, 514–519. doi:10.1016/j.procir.2015.02.181 Posada, J., Toro, C., Barandiaran, I., Oyarzun, D., Stricker, D., de Amicis, R., & Vallarino, I. (2015). Visual computing as a key enabling technology for industrie 4.0 and industrial internet. IEEE Computer Graphics and Applications, 35(2), 26–40. doi:10.1109/MCG.2015.45 PMID:25807506 Roblek, V., Meško, M., & Krapež, A. (2016). A complex view of industry 4.0. SAGE Open, 6(2). doi:10.1177/2158244016653987 Safar, L., Sopko, J., Bednar, S., & Poklemba, R. (2018). Concept of SME business model for industry 4.0 environment. TEM Journal, 7(3), 626. Sanders, A., Elangeswaran, C., & Wulfsberg, J. P. (2016). Industry 4.0 implies lean manufacturing Research activities in industry 4.0 function as enablers for lean manufacturing. Journal of Industrial Engineering and Management, 9(3), 811–833. doi:10.3926/jiem.1940 Schröder, C. (2016). The challenges of industry 4.0 for small and medium-sized enterprises. Bonn, Germany: Friedrich-Ebert-Stiftung. Sevinç, A., Gür, Ş., & Eren, T. (2018). Analysis of the difficulties of SMEs in industry 4.0 applications by analytical hierarchy process and analytical network process. Processes, 6(12), 264. doi:10.3390/pr6120264 Small and Medium Enterprises Development Organization of Turkey. (2005). Küçük ve Orta Büyüklükteki İşletmelerin Tanımı, Nitelikleri ve Sınıflandırılması Hakkında Yönetmelik. Retrieved from https:// www.kosgeb.gov.tr/ Content/Upload/Dosya/ Mevzuat/KOBI%CC% 87%E2%80%99lerin_Tan% C4%B1m%C4%B1,_ Nitelikleri_ve_S%C4% B1n%C4%B1fland% C4%B1r%C4%B1lmas% C4%B1_ Hakk%C4% B1nda_Yo% CC%88netmelik.pdf

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Spath, D., Gerlach, S., Hämmerle, M., Schlund, S., & Strölin, T. (2013). Cyber-physical system for selforganized and flexible labor utilization. Personnel, 50(22). Stock, T., & Seliger, G. (2016). Opportunities for sustainable manufacturing in industry 4.0. Procedia CIRP, 40, 536–541. doi:10.1016/j.procir.2016.01.129 Thames, L., & Schaefer, D. (2016). Software-defined cloud manufacturing for industry 4.0. Procedia CIRP, 52, 12–17. doi:10.1016/j.procir.2016.07.041 The Union of Chambers and Commodity Exchanges of Turkey. (n.d.). Retrieved from https://www.tobb. org.tr Tjahjono, B., Esplugues, C., Ares, E., & Pelaez, G. (2017). What does industry 4.0 mean to the supply chain? Procedia Manufacturing, 13, 1175–1182. doi:10.1016/j.promfg.2017.09.191 Todorov, K. (Ed.). (2014). Handbook of research on strategic management in small and medium enterprises. IGI Global. doi:10.4018/978-1-4666-5962-9 Toffler, A. (1970). Future Schock. New York. TUSIAD & BCG. (2016). Sanayi 4.0 Raporu. Retrieved from http://www.tusiad.org/indir/2016/sanayi-40. pdf Üçler, Ç., & Vayvay, Ö. (2018). KOBİ’lerde Eş Zamanlı Mühendislik: Otomotiv Tedarikçi Sektöründe Bir Uygulama. Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi, 10(2), 76–91. Vrchota, J., Volek, T., & Novotná, M. (2019). Factors Introducing Industry 4.0 to SMES, Social Sciences, MDPI. Open Access Journal, 8(5), 1–10. Wikipedia. (2019). Kobi Tanımı. Retrieved from https://tr.wikipedia.org/wiki/KOBİ.

KEY TERMS AND DEFINITIONS Industry 4.0: Industry 4.0 is the fourth industrial revolution that envisages the integration of robots, artificial intelligence and simulation technologies into production processes. SMEs: It refers to small and medium-sized enterprises (SMEs) that employing less than 250 workers and annual financial balance sheet total not exceeding € 43 m. Strategic Management: It is a process containing planning, monitoring, analysis, and assessment of all that is necessary for an organization to meet its goals and objectives.

This research was previously published in Challenges and Opportunities for SMEs in Industry 4.0; pages 205-227, copyright year 2020 by Business Science Reference (an imprint of IGI Global).

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Industry 4.0-Based Enterprise Information System for Demand-Side Management and Energy Efficiency K. S. Sastry Musti https://orcid.org/0000-0003-4384-7933 Namibia University of Science and Technology, Namibia Helvi Iileka Namibia Energy Institute, Namibia Fenni Shidhika Namibia Energy Institute, Namibia

ABSTRACT The supply demand gap in energy sector in any country is a major challenge. Demand side management (DSM) and energy efficiency (EE) are the well-known solutions in the short term, and capacity addition is the long-term solution. However, both DSM and EE initiatives require significant investment and logistics if implemented in the traditional approach. The contemporary Industry 4.0 principles can be effectively applied to resolve several issues. This chapter proposes a novel enterprise information system (EIS) by treating the modern power systems as cyber physical system and to manage the processes of DSM and EE. A prototype system is suggested to pave the path for EIS, and the functional characteristics are illustrated with a few data visualizations.

DOI: 10.4018/978-1-7998-8548-1.ch078

Copyright © 2021, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

 Industry 4.0-Based Enterprise Information System for Demand-Side Management and Energy Efficiency

INTRODUCTION Modern power systems are very complex in the sense, they are fed from different forms of renewable energies through micro and smart grids (on the generation side) and supply linear and non-linear loads (at the customer end) through different power network components such as lines, transformers etc. It is normal to see that some of the generation plants are owned and /or operated by different companies, which are also known as independent power producers (IPPs). Both generation plants and customers are geographically wide-spread and values of generation and loads dynamically vary. In other words, there are several electrical parameters such voltages, currents and powers at several different locations and these are continuously changing. In general, utilities may have several different forms of measuring instruments, right from legacy analog equipment (non-smart, cannot communicate) to modern equipment (capable of computing and communicating) that collect data at regular intervals (typically every 15 or 30 minutes) for storage and processing. Over the time, this data gets huge and hence is big-data. Now, IPPs will have different energy production costs and selling tariffs as specified by power purchase agreements (PPAs). Utilities specify different tariff mechanisms for different customers such as industrial, commercial and domestic. Even Time of Use (ToU) based tariff schemes with four different prices for electrical power in a single day is being implemented in different countries. This measure is part of ‘Demand Side Management (DSM)’ to influence customer power usage pattern. DSM, in a sense, encourages customers to exercise caution in using the electric power (Khripko, 2017). On the other hand, it is important that electrical loads have good efficiency and are of good quality to ensure they deliver the expected performance at reduced costs. In other words, inefficient or low quality (or equipment not confirming to set quality standards) may consume higher energy to deliver same, expected output and even may cause fire accidents. The aspect of energy efficiency of power apparatus and / or customer appliances is studied under the broad title Energy Efficiency (EE). Though DSM and EE are different in several aspects, in reality both of them depend on each other. In real world setting, electrical load parameters at the customer end are dynamically changing, which need to be measured by meters (Hemapala et al., 2012). However the data as measured by the meters needs to be processed and stored; and then should be made ready for various forms of computations and analysis. Both EE and DSM also require periodical energy audits (Roshan et al., 2014) to develop or to modify the implementation strategies. Regulatory bodies or organizations specify various quality standards for customer service and even for power network maintenance activities. Both IPPs and power utilities have to follow the specifications set by respective state regulatory bodies. Customer awareness and free flow of information exchange is utmost important. Most regulatory bodies across the world entrust this responsibility to IPPs and utilities. Different strategies for DSM and EE have been adopted by various power utilities across the world as can be seen from the literature. These strategies depend on various factors including local power tariffs, power network capabilities, and economic status of stakeholders with customers. However, for successful implementation of DSM and/or EE a lot of data (which is authentic) is required be collected and processed from the customers. Big data tools have been recently used in power system monitoring control (Zobba, 2018; Zhou et al., 2016) for various purposes. Several computing architectures (Zhang, 2108) and data analytics have been suggested for composite systems. However, enterprise information systems, or data driven intelligence and analytics specifically for DSM and EE have not been presented in detail. From the above, it is clear:

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• • • • •

There is a significant interest in implementation of DSM and EE. Advising and educating customers on DSM and EE is now an obligation. DSM and EE are data driven. There is a significant interest in applying big-data and data analytics in power systems. A well-defined enterprise information system is required to assist various stakeholders in implementing DSM and EE.

This chapter makes an attempt to explain both DSM and EE from the big-data point of view and suggests a framework for an intelligent information system that accomplishes the complete cycle from data specification, collection, processing to the end usage of the information obtained from the analytics. Also, the key issue of measurement and verification of the data will be taken up with a novel IoT based system. Versatile visualizations specific to power utilities, IPPs and customers will be presented. To accomplish these objectives, this chapter provides a comprehensive review of recent literature in three areas – viz, DSM and EE implementation experiences, information system frameworks for power systems, data visualizations and analytics for specific goals. Overall content of this chapter is divided into various sections. Firstly, DSM is explained in simple terms, then salient aspects of frameworks adopted by various countries in the adoption and implementation of DSM. Then the chapter focuses on characteristics of a general EIS and explains how DSM requires such EIS. A prototype EIS for DSM is then presented that has several components. Two smartphone applications are proposed for the first time in line with industry 4.0 principles and also to assist energy auditors and wider public to simplify the processes and also as per the latest societal norms and user expectations. A small part of the data is then considered to illustrate common visualization requirements. The section titled discussion provides insights to various aspects of DSM, infrastructure, usage and data related issues.

DEMAND SIDE MANAGEMENT In a typical power system consumers are supplied through ‘feeders’, that are essentially power distribution lines which run from transformers in substations. In a typical urban setting, there will be several different feeders with different voltages (11kV, 400V, 220V etc) for different types of consumers. Each feeder may supply hundreds of consumers. Most often, planning engineers might want to know if it is really beneficial to implement DSM on a specific feeder. For instance, if 40% of the consumers on a feeder are already having solar thermal systems, then base load will be less. In other words, there is no need to push for DSM on that feeder. Similarly, if 30% of the consumers have installed solar PV systems, then peak load on the feeder is already clipped and again there is no need to push for peak-clipping part of DSM. Similarly, a large scale institution or industry installs water chillers based central air-conditioning system, then energy consumption goes down significantly. This is due to the fact that water chillers consume 40% of energy, only a third of water when compared to conventional electric air conditioners. And their efficiency is very high. Further, replacing the electric air conditioners with water chillers is one of the popular DSM activities too. Figure 1 shows three important DSM strategies; viz., peak clipping, load shifting and energy conservation.

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Figure 1. (a) Peak clipping (b) Load shifting (c) Energy conservation

From the above, it can be see that it is important to understand how existing consumers are operating and what their energy consumption patterns are. Interestingly, utilities also want to know how much energy is consumed for what purposes and also what are the appliances residential consumers have (Ahmed et al., 2018). How to obtain such detailed information? Energy audit provides an excellent inputs to both DSM and Energy Efficiency – Appliance Labeling (EE-AL). Several authors (Gan, 2012; You et al., 2018; Padhy, 2002) clearly indicated that a majority of the DSM implementations have seen poor results due to lack of implementation experience and lack of awareness. However, energy audits and related analysis are very data oriented. Firstly customers are more in number, data (such as details of all the appliances, monthly billing information etc) needs to be collected from each and every customer. And then consumers may add new apparatus, replace existing ones and then bill amounts vary every month. This indicates that the nature of consumer data is voluminous and slowly changing with respect to time; and that energy audits should be undertaken periodically at least once in three years. If the consumers are generating electricity, then data needs to be collected too. Typically solar PV data is collected once in every 15 minutes or even less like 10 minutes, due to random nature of the class of solar energy. Hence energy audit by itself is a heavily data driven process and indeed is continuous process. To advise consumers properly about energy efficient apparatus, life-span, quality and performance ratings etc; a national policy needs to be in place. And then there should be a laboratory facility that is equipped to test various apparatus and properly accredited for quality processes. Such facility should proactively undertake testing of various appliances that belong to different brands and different capacities. In the days of competition, it is possible to have understated (or under quoted) name plate capacities at the lower prices. Poor making and low quality insulation might result in apparatus failures and/or even result in safety issues such as fire accidents. Results of all such testings along with the labels should be properly made available to the public so that they can plan on their shopping. Further consumers want detailed information on planning certain aspects. For instance, a commercial consumer wants to add solar PV system, but before that wants to investigate cost-benefit analysis. Such analysis depends on ToU tariffs; rules for net-metering/ feed-in metering; size of the PV system, battery, charge controller etc. Even this analysis is also data driven. It is also usual that consumers may want to visit existing installations and discuss with owners to understand the ownership experience. Having a comprehensive information system of various appliances, real life case studies and scenarios is essential (Roshan et al., 2014; Shipman et al., 2013). Testing labs need to stay active as many new products come into the market round the year; and hence testing (and updating information) is continuous process.

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Expectations Of DSM DSM is implemented in most countries through a national policy framework (Apajalahti et al., 2015; Stotzer et al., 2011). Framework policy is a general and more abstract set of principles and long-term objectives that guide the development of and form the basis of specific programs. It creates institutions establishing relevant laws and programs. The framework policy can also be in the form of a binding quantitative national savings target. Targets may be set as a capped value or volume, typically for a longterm period in the future or as a goal compared to a base year or projected baseline. They are defined in terms of a specific energy or demand goal (kWh or kW), savings (% as compared to base or baseline), emissions reduction or energy intensity (kWh per GDP). Detailed discussion of DSM policy frameworks of individual countries is out of the scope. However, following points summarize internationally adopted frameworks and hence are important for making a case for EIS for DSM. Information processed/ produced by EIS for DSM should help in: • • • • • • • • •

A plan to achieve energy independence and promote the efficient and rational use of electricity in all sectors of the economy. Achieving energy efficiency market transformation through standard practice based on trends. Providing data driven energy audits for end-users. Conducting energy efficiency demonstration projects. Creating case studies of large energy-intensive industries,, corrective actions with penalties for non-compliance. Reporting an annual energy consumption footprints of large consumers in terms of tonnes of oil equivalent as part of national and international expectaions. Creating an assorted repository of energy ratings, EE labels for most commonly sold appliances along with energy performance certificates for various commercial, residential and government buildings. Providing the information on various DSM initiatives undertaken by utilities and other stakeholders; and the corresponding results/ benefits. Assisting analysts, energy managers, energy auditors, regulator and common citizens with usable, and reliable information.

DSM can get as complex as it can be. Utilities across the globe found the compelling need for engaging a separate organization to deal with DSM. Education and capacity building measures are soft measures that help minimize possible rebound effects and induce long-term behavioral changes of end users. They include education, public outreach and awareness campaigns, training programs, detailed billing and disclosure programs, etc. However, education and / or training wider community is not an immediate function of the utilities. At present energy information centers have been established, however a properly designed EIS for DSM can potentially integrate and digitally transform such centers to eliminate the requirement of human intervention. Since the EIS for DSM umbrella is vast, the need for a separate organization to champion the cause of DSM arises. However, a few operational challenges need to be resolved. For example, the SCADA and real-time metering infrastructure remain with the utilities. Data collection and energy audits need legitimate access to customer premises. Such factors point to the need of greater cooperation between various stakeholders in seamless exchange of data and infrastructure.

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Characteristics of Data of a Typical Power Distribution System It is essential to examine the data characteristics so that appropriate platforms and technologies can be chosen to process the data to extract information. In power distribution systems data is measured and transmitted to respective engineering centers for processing purpose. Contemporary power distribution systems will have metering infrastructure as part of Supervisory Control And Data Acquisition (SCADA) systems. Future power systems will employ Industrial Internet of Things (IIoT) based measuring infrastructure. This infrastructure may co-exist along with SCADA or even may function independently. Volume and Velocity: Power networks expand regularly; new customers get added continuously and hence number of data acquisition points only increase over the time. As stated earlier, data is collected once in 30 minutes or even 15 minutes from each measuring point. Hence the data that comes from very large number of points quickly adds to the volume, though frequency and velocity is almost constant. Some portions of the data may be redundant; so care must be taken while processing the data to eliminate redundancy. Usually redundancy of data measurements may be deliberate (especially in case of electrical power distribution) to overcome the situations of faulty measuring equipment; or to detect power pilferage; or even for verification purposes in some cases. It can be understood that data volumes can be very high, which is one of the major attribute for this data to be classified as ‘bigdata’. Variability: The patterns of customer load consumptions do follow a general l trend over the day, though the trend itself may (will) vary from place to place depending on type of consumers. This variation of power consumption is typically represented with ‘daily load curves’. Usually during the midday (from 11am to 3am) power consumption will be peaking in most cases. However, averages, peaks of the power consumptions will be different from month to month, depending on local climatic conditions. For instance, during summer season, air-conditioner loads will be predominantly causing higher consumptions, similarly during winter season the heating loads will result in higher loads. Utilities monitor peaks and averages such as ‘daily peaks’, ‘monthly peaks’, ‘monthly averages’ etc. Also it is important monitor the peaks and averages over the weekends. The reason behind looking for such information is different tariff structures. Based on the usage patterns, tariff structures for the next year will be determined. Hence, the data of a power distribution system varies, but not a high frequency. And variation of the data is essential to the utility and even for the state regulator. Variety: The key parameters in any power systems are current (Amps), Voltage (Volts), active power (kW), reactive power (kVAR), power factor. Measured values of these parameters from various feeders, transformers or major load points constantly relayed. In the modern smart systems, same data for various loads (lighting loads, cooling/ heating loads etc) may be measured and relayed. However, it should be noted that measurement or individual loads is almost not possible and practicable. Hence tools such as energy audits are used to estimate the numbers, types and purpose of various loads in the customer premises. Such variety is essential for DSM to deploy appropriate measures. For instance, it is prudent to push for solar water heaters where electrical energy is predominantly used for heating the water. It can be seen that variety of data is present, but the variety itself is constant. Another important data is from network switching. Engineers do switch on/off circuit breakers and/or switches frequently as part of real-time operation for various reasons. With this, relationship between network components might change. For example, a group of consumers may be put on a different feeder over a period for some maintenance purposes. This does not necessarily add to the spread of variety, however is different data from above stated parameters. One of the DSM initiatives - ripple control (explained in later sections) is an example of network switching on/off. 1575

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Another aspect of variety comes into effect when DSM initiatives are taken up. For instance, installation of solar water heaters (SWH) in industry 4.0 setting will bring a host of parameters that are not essentially electrical, but physical. Similarly solar PV installations require measuring the energy generated over the sun-shine hours. Accuracy and Authenticity: Measurement and Verification (M&V) process is the heart of the data collection in power distribution. For this meter will be periodically checked and even calibrated on a random basis. This field practice needs to continue to ensure trust on the data. However, in big data context, it is possible to deploy various algorithms to detect any abnormalities. Even block-chain technologies are suggested by a few authors, however such discussion is out of scope for this chapter. Veracity: As above characteristics of data tend to get stronger, the confidence or trust in the data gets reduced. Usually the questions– how reliable or how meaningful this data is for business analysis? ; Can we rely on the trends provided by the information to take power purchase decisions over the next year?; what methodologies were followed in summarization (or information creation) - need to find convincing answers. Hence it is important that the data is processed carefully and the information is properly acknowledged (periodically) by field engineers. End Use and Business Value: Several core business requirements of power utilities such as plotting various load curves, determination of network loading conditions, power flows through various distribution lines, generating wide ranging information for reporting to public and the regulator etc; depend on the data and information. These activities are time consuming and data intensive. Load forecast, day ahead planning, fund allocation for future purchases (in short and long terms), determination of tariffs (time of use, real time and critical peak timing) etc. depend on the information generated by the data. Hence, end use and business value of data collected in a typical power distribution environment is very high. From the above it can be seen that data of a typical power distribution system has most common features of bigdata. Hence, appropriate bigdata platforms and tools need to be used for developing EIS for DSM so that information can be extracted in an efficient manner.

Impact of DSM Initiatives on Data Monitoring and Logging Though, there are many DSM initiatives, three major aspects viz., installation of solar water heaters, solar-PV systems, ripple control and replacing existing lamps with energy efficient ones; will be taken up for discussion. Solar water heaters (SWH) are very popular in energy saving. Hence, one of the major DSM initiatives is to install solar thermal systems on roof-tops to reduce electricity use for water heating purposes. Solar thermal systems are proven means of saving energy costs. Typical payback periods of solar installations will be anywhere between 2 to 7 years depending on the capacity, overall costs and local climatic conditions. However, there are associated challenges do exist. Consumers need precise information about payback times, maintenance issues, ownership experiences and even information on how to select the size. These challenges obviously point to lack of information and awareness. Then to encourage the consumers on installing solar water heaters, it is important to provide a simple handout (or even a smartphone app) that helps consumers in sizing the system for their specific purpose. Further, showing all the solar water heaters in their own community both in numbers and also on digital maps will have a positive impact. Naturally modern day consumers want to see all such cost saving measures and installations in smartphones with wide raging smart visualizations and apps. This obviously creates a need for a custom smartphone app. Though there are a very a few apps that are available at present, however 1576

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they need local climatic data especially the monthly meteorological data to start with. DSM enterprise systems need local meteorological data in any case for accomplishing the tasks like short term and long term capacity addition. Even the modern day SWHs are smart and do come with an array of sensors. The parameters that are continuously monitored include - flow rate of the hot water in liters/hour, water flow rate in various other loops, power of hot water consumption in kW, temperature of the hot water in the collector, temperature of cold water in the storage tank. Most importantly the local climatic parameters such as – global radiation in the specific location, wind speed etc., will also be monitored and actually need to be processed to estimate the cost benefits over the time. On the other hand, summary of this information needs to be properly channeled to the consumers to educate and thus encourage them invest in SWHs. From the above it is clear that every component such as a solar thermal system, that is added to the system, needs to be monitored. Essentially each component will have its own parameters and hence a lot of data will be generated on a continuous basis. The data should be collected in real time for processing, storage and further analysis. Ripple control is a known solution and hence indeed an integral part of DSM. This technique is used to control a large part of the uniform loads such as street lights, water heaters in a given area. As stated earlier, ripple control involves network switching and hence data acquisition is essential to determine the overall savings. Ripple control also results in reducing the energy consumption as well as in shifting load from peak time to off peak time or standard time by remotely switching off electrical geysers at households and business at specific times. It is essential to provide the information related to energy savings through versatile data visualizations, as shape(s) of load curve(s) will be altered significantly.

Enterprise Information Systems An enterprise information system (EIS) is broadly defined as “a software package that helps a business perform and oversee certain processes, gathers data about these processes for analytics and maintains clear transparent records of these transactions” (Smyth, 2019). From the above sections of this chapter, it can be seen that DSM involves significant data collection, storage operations, computations and analytics. Also, there are means and ways that the data for DSM is collected, particularly in modern smart grid environments. Measurement and verification is major component of DSM and indeed is the driver of the trust for energy sales. On top of that smart control techniques are also part of DSM. Hence, in the context of DSM, EIS can be defined as “a large scale cyber-physical system that helps various stakeholders in the smart, modern energy markets that oversees various processes and provides information through data visualizations in a seamless manner”. The chief characteristic of EIS is the centralized database and real time operation. DSM is all about integrating several databases (power network database, available generation data, consumer database, tariff database, historical data, consumer load data, consumer own generation data etc) and data computing models and information extraction processes. The three major business categories (Smyth, 2019) of generic EIS are (customer relationship management (CRM), supply chain management (SCM) and enterprise resource planning (ERP). Even in the case of DSM all the three categories are present. The aspect of CRM in power utilities is critical and well published area (Rahman et al., 2016; Sastry, 2007). Worldwide, it is a standard for the utilities to serve customers through a dedicated call center to address grievances. CRM aspect alone requires a well-designed information system that essentially integrates various databases and e-CRM of power 1577

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utilities indeed need to operate in real time. Some DSM activities include encouraging the customers to install solar thermal systems. For that several case scenarios need to be developed to advice on size of the system, capital costs, return period etc. SCM in modern, smart grids refers to continuously varying quantities of load and generation. In case of smart grids, even consumers also produce energy and thus reduce the supply-demand gap particularly during day time or peak load hours. Utilities are expected to operate their systems carefully through proper real-time monitoring and control. At times, utilities need to resort to load shedding to disconnect the customers to save the power networks from overloading conditions. This again might result in revenue losses in terms of energy sales; however capital damages can be avoided. ERP in DSM context is a continuous activity. Both load shedding and supply-demand gaps indicate the need for adding new capacity at the earliest. However, utility needs compressive information (obtained from reliable data/ accurate M&V). Cost benefit and payback time studies are required to properly plan the capacity addition. However capacity additional requires time and the interim solution to manage the system is the DSM. Usually utility engineers need to go through costing, projections, revenue estimates for planning the expansion of the system (Koh et al., 2016).

EIS For DSM This section proposes a novel EIS framework for DSM. This EIS framework consists of a central database (that extracts data from different sources, including real-time M&V, a custom built test facility), IIoT based M&V system, two smartphone apps, computing models and data visualizations. The overall objectives of EIS for DSM include assisting consumers in: •

Selecting an appropriate size and energy efficient electrical apparatus with details of energy label /ratings. Understanding the aspects of net-metering and/ or feed-in-tariff systems. Understanding daily energy consumption patterns in various ToU tariff zones, monthly and yearly values of energy units consumed as well. Understanding energy saving opportunities through cost benefit analysis based on historical data for investing in solar PV or solar thermal systems. Evaluating their appliances for energy efficiency through smartphone apps. Obtaining energy ratings for the existing power apparatus. Shopping for new apparatus or replacing the existing ones.

• • • • • •

EIS should also assist utilities in: • • • • •

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Planning ripple control circuits on the power network and devise strategies to implement. Undertaking ripple control on a daily basis during overloading or low tariff times. Adjusting ToU tariffs based on consumer energy consumption patterns in a given day, month or season. Announcing energy saving schemes with incentives for those consume more. studying network overloading and thus determine specific interim DSM strategies such as peak clipping, load shifting.

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• • • •

determining the overall energy savings for each control action through before and after case scenarios. Planning the capacity addition through additional energy resources such as solar PV or wind. Determining the need to add a base load plant and/or a load following power plant. Periodical assessment of revenues and operating expenses.

Then DSM is also expected to assist regulatory authorities in following various trends energy consumption and generation, flow of cash and revenues in a seamless fashion (Rosenow & Bayer, 2016). Further, it is expected to assist energy auditors in: • • •

Undertaking a detailed customer load survey to capture types, numbers and capacities of apparatus and appliances. Capturing the data directly from the customer location using smartphones. Providing information on energy efficiency of existing customer apparatus.

Assist organizations (like NEI) in organizing educational, informative workshops to wider public, which is one of the major DSM initiatives. With huge data, a well-designed EIS can convey a lot of knowledge through wide ranging possible case scenarios. Careful planning is required to develop utility standard EIS for DSM, just as the case for EIS for any other purpose. Interestingly, building a prototype is the significant part since user requirements can vary widely. In other words, it is not possible to capture requirements that are 100% satisfying to everyone. And the users are from different walks of society, as utility engineers, common public, owners of IPPs, industry consumers, energy auditors and regulatory officials etc. Process of building EIS becomes more complex especially when a separate organization such as NEI takes up the responsibility, as real-time monitoring and control systems are owned and operated by the utility. Hence it is important to design and develop a prototype with most expected features and then let it mature through exhaustive usage, testing and improvising processes.

Prototype EIS for DSM Building a functional prototype for EIS for DSM is critical. To begin with, firstly, the biggest challenge lies in difficulty in identifying accurate, final user requirements. Secondly, the subject of DSM is too wide to comprehend and most consumers may not have complete understanding in any case. Hence, it is not very well possible to capture user requirements easily in the initial phases. If there exists a prototype, then the users operate and then may be able to suggest their needs or even possible customizations. Figure 2 shows a typical schematic for the proposed prototype. As can be seen from figure 2 the prototype consists of DSM servers - a database server (for prototype, any license free database such as MySql can be considered), application server for data abstraction, computing, information processing, creating visualizations; and then different databases such as electrical power network, customer information, tariff structures etc. IoT based M&V infrastructure involves measurement of various parameters at key places in the network. For instance, real time readings are required at the upstream, that is at the substation. This may initially look redundant as utility already has the metering infrastructure. However, it is important to have IoT based, low cost infrastructure that is part of the prototype for comparison and benchmarking purposes. The parameters measured here should be 1579

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tallied with its own readings at various points in the downstream, including at the consumer locations. It is recommended that utility grants access to prototype infrastructure to be installed after the current transformers and voltage transformers located in the substations. Similarly IoT based M&V apparatus is required to be placed at all industrial and commercial consumers as their tariff structures are different from residential consumers. Indeed, revenues from these two category consumers to the utilities are quite significant when compared to residential consumers. (Koh et al., 2016). Details of circuitry and IoT infrastructure are avoided, as this chapter focuses on overall framework for EIS. EIS Prototype for DSM should have a majority of the functional characteristics of utility scale EIS. For instance, functions like - data collection, fog / edge computing, pre-stored case scenarios and visualizations etc should be more or less same. Utility scale EIS will essentially be equipped with big data and high performance computing platforms; where as prototype may be designed to operate on scaled down platforms such as desktop/ network technologies (Yu et al, 2015, Tu et al, 2017). Prototype EIS may be initially designed to operate with static (or slowly changing) but huge volumes of data. During the transition to utility scale many hardware and software layers may have to be changed for various reasons mainly for data processing and computing efficiencies. Big data tools, platforms and technologies have to be essentially used in corporate scale EIS (Munshi et al., 2017; Schuelke-Leech et al., 2015). A significant investment needs to be made towards wide area communications and cyber security purposes. It is also common to see special budgeting needs to be put in place for additional cooling for data storage facilities and routine maintenance activities as the scale of system grows over the time. Figure 2. Schematic diagram for prototype EIS

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MOBILE APPS FOR ENERGY AUDITS AND ENERGY EFFICIENCY Energy audit and power apparatus labeling are time consuming activities that require a lot of human efforts and involve other logistics. For instance, energy audit teams need to visit each customer premises to make a comprehensive record of each and every load. Two smartphone based apps are proposed for the first time; one for conducting energy audit and other for knowing the energy efficiency of the power apparatus and appliances. Figure 3 schematic of the EIS along with the two proposed apps and the data flow. Essentially data is collected energy audit app and sent as input to the EIS. Energy efficiency app serves the consumers in understanding patterns of their own consumption (if they have home automation systems) and the efficiencies of various appliances; from the stored information as collected from previously conducted lab tests and labels given. The energy efficiency app essentially works on both fog and edge computing principles to show the necessary information. For instance, customer end data is analyzed locally within their own automation infrastructure and only the summary (or required) information is sent to the EIS. The app will extract the required information on each update (for information on various apparatus) from the cloud that EIS operates. The feedback loop from energy efficiency loop is meant for knowing what type of apparatus and what systems consumers are looking for. With such information, EIS can be further augmented in its functionality. Figure 3. Schematic for smartphone apps for energy audit and energy efficiency

The energy audit app essentially should be used by trained and certified energy auditors and volunteers. The reason why this cannot be used as ‘crowd sourcing’ app to collect the data is – integrity of the data is very important. Data cleaning alone takes up significant efforts. Data collection using the contemporary crowd sourcing principles may not be necessarily suitable for DSM / EE-AL. Then EE-AL app is open to everyone as it does not accept any inputs from users and essentially to advice on various aspects as discussed above. 1581

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Table 1 shows an example of the information that is usually collected for energy audit purposes. It should be noted that this is only an example for the purpose of illustration and in an ideal situation, more detailed information may be required to be obtained in order to fulfill the objectives of energy audit and energy efficiency studies. Table 1. Customer information Customer Type:

Meter Number:

Address:

Name of the Consumer:

Average Monthly Utility Bill:

Email / Phone Contact:

Thanks to the technological revolution, the penetration of smartphones is very high even in the semiilliterate population. Android platform and the apps are so intuitive that users do not require any formal training to operate. It is possible to use smartphone apps to undertake the data collection for both energy audit as well as energy efficiency studies. The Energy Audit and Apparatus Labeling (EAAL) app should be designed to collect the details of types of lighting loads, their numbers, ratings of power apparatus such as washing machines, air-conditioners, microwaves, electric geysers, electric stoves, entertainment / electronic loads such as television, music systems etc. Table 2 shows a sample for a typical template that can be used for customer load survey. Table 2. Template for customer load data Quantity

Total Watts

Incandescent Lamps Fluorescent Lamps LED Lamp Refrigerator Air-conditioner Microwave Water Heater Solar Water Heater Room Heater Electrical Stove

Users must be able to input the details by themselves and also upload the images (using the camera of the smartphone) of name plates showing the electrical ratings of their apparatus. Even the address can be obtained by location feature of the phone. The app should be simple to operate collect the information of the consumer information. Appropriate care should be taken in handling the data (name, email / phone contacts and location etc) and users must be provided assurance when they download / install the app. To sensitize about the app, local authorities should issue notifications through print and electronic media.

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The information collected from the EAAL app should be used to provide diverse insights and solutions that can benefit consumers as well as utility. Once user base increases, EAAL app will be collecting huge amounts of information about different types, brands and sizes of the electrical apparatus. The energy efficiency test center (EETC) would have conducted the necessary tests under standard operating conditions on various brands and would have designated the appropriate labels. Now, the back office personnel need to examine each consumer load to attach the energy rating label. This information is valuable to the customer and also to the utility in general; in terms of replacing inefficient apparatus with more efficient ones. The benefits can be provided as ‘Information as Service’. Typically for individual consumers, the app should be able to provide information on ratings/ or labels of each existing device based on the photos of the name plates and also the devices themselves. In case consumers want to buy a new apparatus (for example an air-conditioner) In doing so, the EAAL app may provide links to local market vendors with costs. This brings in some revenue to the EAAL app as well besides guiding the consumers. Over the time, consumers may add new apparatus and/ or replace the existing apparatus with new ones. Hence, EAAL should be designed in such a way that it is friendly enough for the users to update their information on regular basis. From the above, it can be seen that an app like EAAL that works on crowd sourcing principles has good potential to assist consumers and also increase their awareness. The information generated by the data of EAAL can be great resource for the utility as well. It is possible to observe the relation between load consumption (from the bills and meters) to the EAAL data. If majority consumers in a specific area are using electrical geysers, then utility may plan for ripple control. On the other hand, it is possible to observe if there is any pilferage or significant changes in the power consumption in the feeders. For example, sudden drop in the consumption is generally flagged for further probing towards pilferage. However, if a good number of consumers on a feeder install solar water heaters, then it leads reduced power consumption. Energy audits also help utility to plan expansion of existing and/or new power networks.

DATA VISUALIZATION FOR DSM Now, the output of DSM is as seen from above, is data visualization intensive. To illustrate load research, a typical city feeder is considered along with its daily load data. It supplies a small urban area with a combination of small-scale industries, commercial establishments and residential customers. Using this example, data visualizations are developed to demonstrate the effectiveness of the prototype, its data models and computing features. Details of integration of data sources, database design and relational aspects are avoided. Figure 4 shows a part of a sample data. for both load and solar PV generation at every 15 minute interval. Now for the purpose of utilities (or other stakeholders), overall load composition over the day is shown. Then DSM is always represented with daily load curves and hence even the EIS (or the prototype) is expected to produce same visualization. Figure 5 shows the composition of the overall daily load. This information is useful to determine the extent of ripple control and even energy conservation parts of DSM. Figure 6 shows actual load, values of PV generation and the net load over the day. It can be seen that the visualization matches the theoretical approach specifically in this case, the achievement of peak clipping.

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Figure 4. Data for a small urban feeder (partly shown)

Figure 5. Representing composition of overall feeder load

Now, if a small number of customers have installed solar thermal systems then there will be lesser energy consumption. Data for this case is shown in figure 7 and the composite visual is shown in figure 8 The above visuals (figures 5 to 8) show the information over a single day and for the entire feeder from the readings captured at the upstream, in the substation. It should be noted that data visualizations shown above are mainly useful for utility and regulatory personnel. For consumers, data visualizations will be different.

TESTING FACILITIES AND ENERGY EFFICIENCY LABELING Energy efficiency and apparatus labeling require good facilities to test various power apparatus under standard, uniform and unbiased operating conditions. For this a centralized energy efficiency test center (EETC) needs to be established on specific requirements that depend on local market conditions and wide

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ranging apparatus that might be in use; in general. Testing standards and protocols must be developed and then revised over the time based on the needs. The common parameters that need to be tested include, but not limited to current drawn, energy consumed over a fixed time and overheating of the surface etc. Further, quality of the build, electrical insulation and general safety issues will need to inspected. After that standard labeling may be done as per the set standard processes. In a way modern smart grids can be benefited with contemporary big data analytics and related technologies (Schuelke-Leech, 2015). Indeed it will be a paradigm shift (Akhavan-Hejazi, 2018). Figure 6. Load curves with and without peak clipping

Figure 7. Load data with solar thermal systems included (partly shown)

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Figure 8. Load curves with and without peak clipping as well as energy conservation

BIG DATA OPERATIONS Considering the EIS data characteristics, several big data related operations need to be undertaken. •

• •

Processing the huge volumes of data generated by various smart measuring equipment requires ‘dimensionality reduction’ to reduce communication costs, computing complexity, and data storage requirements; etc. Random projection technique can be effectively applied to provide a reduced sketch (information) of the measured data. K-Means algorithms based on Fuzzy C-means, ANNs can be used for load classification and even for forecasting the load for short term and long term planning. For determination of network losses, power flows on huge data sets, high performance computing, distributed and parallel computing techniques need to be used.

In any urban area, several feeders will be distributing the electric power. Nature of data collected from the feeders is more or less same. Hence for data aggregation, MapReduce concepts along with parallel processing need to be employed for providing the overall summary. Platforms such as Cassandra database can be considered to store large datasets. Besides these, several opportunities do exist in developing cloud applications based on fog and edge computing models to reduce the communication and storage burden. In fact, the EIS even can deploy a repository for smartphone apps. Figure 9 shows a few apps as already discussed. Using these apps, consumers can investigate the cost benefit prospects of investing in solar PV, SWHs, energy efficient lights etc. Auditors can use the apps for information collection and energy labeling processes. As can be seen from the above discussion, there are several opportunities to develop various algorithms, platforms, smartphone apps etc for data processing, analyzing and visualization. One of the interesting aspects is the custom GIS environment.

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Figure 9. DSM app store

It is common to use google-maps to display geographical location in user interfaces; and indeed this feature is one of the essential components in data visualizations. As far as DSM is concerned, geographical location is important for several reasons. Some of the reasons include – consumer location, network component (such as substation) location, mapping of SWHs in a given area, network zones effected by ripple control, mapping of solar PV systems etc. System planners and even consumers may want to know the areas that have heavy penetration of SWH or solar PV installations for various reasons. Since early 2018, google maps are no longer free and are priced for most usage purposes (Maps, 2019). This poses challenge to the system development as GIS platform is now priced as per the usage, though mobile usage of the google-maps is still free. This challenge naturally leads to a new opportunity for developers to consider designing custom GIS platforms. Though this can be laborious, at the end, sustainable software development environment can be achieved.

DISCUSSION From the above sections, it can be seen that DSM strategies in modern smart grids are different and require even a smart EIS. Data collection, real-time M&V and information visualization are the essential elements. Then there are several challenges in implementing DSM too. In a typical market driven conditions, utilities depend heavily on electricity sales to the consumers. More the energy consumed, more is the revenue to the utility. When DSM is implemented, consumers tend to ‘shift’ to lower tariff times in the day to save on energy bills simply due to ToU tariffs. Due incentives and/or encouraging conditions a good number of consumers might have installed solar thermal systems; and thus consume less energy, which accounts up to 20% to 30% of their original consumption. With ripple control method, heavy consumer loads such as air-conditioners and water heaters will be switched off; and hence very

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low energy consumption again. Replacing and/or using LED lamps will lead to lower energy consumption too. From the above it can be seen that DSM actually results in lesser revenues to the utility. Even though there are known methods (such as decoupling the tariffs) to compensate the revenue losses, most utilities tend to show lesser interest in DSM implementation. On the other hand, if the utility is able to add more generation (which is very expensive than energy saving) and consumers so that it can sell more energy then it will be an excellent revenue proposition. Important point that needs to be noted is adding generation that has load following characteristic (such as solar PV) will be of great help. Hence, utilities must consider establishing more solar PV plants to supply the loads that peak during mid-day. DSM also might result in load shifting (as consumers move to cheaper tariff times) that in turn will push the base load up. In this case, utilities are left with only option of adding base load generation capacity (such as coal / thermal power plants)to meet the load that exists round the clock. Normally, cities expand continuously due to people migrating from rural areas and thus energy demand in the cities always increases and hence, DSM is a continuous process. DSM results can vary from place to place and may depending on various parameters. The role of EIS is vital as it should be able to quantify the benefits in an appropriate manner using reliable data. The success and usability of EIS depends on the user participation and dependability of its output. To achieve the best results, prototype EIS should be put to rigorous tests and then appropriate modifications to make it acceptable to all the stakeholders. Some aspects of energy audit and energy efficiency from regulatory framework are to be noted here. Time of Use (ToU) tariffs are applied to both industrial and commercial consumers. Utility bills are very high when compared to domestic consumers due to ToU tariffs. Usually these consumers are expected to undertake energy audit studies through licensed firms/ or consultants /or energy auditors on regular intervals and furnish the reports to the regulator. Energy auditors also examine safety aspects also during the inspections. Energy audit reports are expected to contain the Energy Saving Opportunities (ESOs), safety aspects, and actions to taken or to be taken to achieve those opportunities or to improve the performance of operation. With such regulatory requirements in place, most industrial and commercial consumers will be able to meet energy efficiency and operational safety requirements. This process also help consumers to save significant amount of money as the overall electrical power consumption reduces. Domestic consumers are typical category in the sense, they are in majority in numbers and ToU tariffs generally are not applied. Physical visits to the customer premises require advance notification to the public through news and print media to sensitize about the purpose of Energy Audit and Energy Efficiency projects. Customer reactions and level of cooperation may differ greatly from individual to individual. It should be noted that visits to commercial and industrial consumers will be generally, relatively easier when compared to domestic consumers due to various reasons including socio-economic and educational backgrounds. On the other hand, the major objective of DSM is spread awareness about lessening the energy usage and increasing the energy efficiency. Combining home automation with DSM will be the next stage of development. As principles of fourth industrial revolution (or simply Industry 4.0) are applied in every walk of life, chances are high for utilities to implement DSM in near future from the industry 4.0 context. DSM requires large databases, computing technologies, continuous, real-time monitoring (eg: energy measurements, status of apparatus etc), physical control loops (eg: ripple control), versatile data visualization formats, mobile apps and of course communication systems etc. Thus DSM is a perfect cyber-physical system and indeed is a good example candidate for industry 4.0. IIoT can be effectively used for real-time monitoring and control purposes. Cloud based home automation systems that provide access to clients through mobile apps to control their home appliances are already available. This is better known as ‘Home Energy Management 1588

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as a Service HEMaaS’. Implementing DSM from the point-of-view of industry 4.0, will be more cheaper as IoT can help mitigating costs in lieu of expensive (and legacy) SCADA systems. The modern market driven energy environment is very competitive and success of the utilities depends on cost effective capacity addition as a long term solution; and meeting the energy needs of the consumers through effective DSM initiatives. In most situations, utilities need to engage both long term and short term strategies simultaneously. EIS plays a significant role in accomplishing both of the tasks. Though utilities may be opposing aggressive form of DSM (due to fears of losing revenues), the success depends on finding new consumers and channeling the saved energy, let us from peaking clipping.

CONCLUSION DSM and EE are indeed very complex, especially in modern smart grids that are driven by market conditions. Relevant international trends, practices and experiences from various countries have been presented. A strong case is made for EIS for DSM, as it provides an essential solution for managing various processes such as data collection, real-time M&V and information visualization. Significant logistical and physical initiatives such as solar thermal installations, ripple control have been explained along with associated impact on data generation. Various challenges in implementing DSM and EE are discussed. Data visualizations based on day’s information are provided to illustrate the effectiveness of the prototype. With due diligence and careful planning, it is possible to build a prototype EIS and then further develop it to utility scale EIS for wider acceptance. Further, a prototype EIS can essentially support educational awareness and information dissemination.

REFERENCES Ahmed, N., Levorato, M., & Li, G. P. (2018). Residential consumer-centric demand side management. IEEE Transactions on Smart Grid, 9(5), 4513–4524. doi:10.1109/TSG.2017.2661991 Akhavan-Hejazi, H., & Mohsenian-Rad, H. (2018). Power systems big data analytics: An assessment of paradigm shift barriers and prospects. Energy Reports, 4, 91–100. doi:10.1016/j.egyr.2017.11.002 Apajalahti, E., Lovio, R., & Heiskanen, E. (2015). From demand side management (DSM) to energy efficiency services : A Finnish case study. doi:10.1016/j.enpol.2015.02.013 Diamantoulakis, P. D., Kapinas, V. M., & Karagiannidis, G. K. (2015). Big Data Analytics for Dynamic Energy Management in Smart Grids. Big Data Research, 2(3), 94–101. doi:10.1016/j.bdr.2015.03.003 E. C. B. (2018). City of Windhoek electricity tariffs 2017 / 2018. Author. Gan, C. K. (2012). A review on micro-grid and demand side management and their related standards. Power and Energy Conversion Symposium (PECS). Hemapala, K. T. M. U., & Kulasekera, A. L. (2012). Demand Side Management for Microgrids through Smart Meters. doi:10.2316/p.2012.768-060

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Khripko, D., Morioka, S. N., Evans, S., Hesselbach, J., & De Carvalho, M. M. (2017). Demand side management within industry : A case study for sustainable business models. Procedia Manufacturing, 8, 270–277. doi:10.1016/j.promfg.2017.02.034 Koh, S. L., & Lim, Y. S. (2016). Evaluating the economic benefits of peak load shifting for building owners and grid operator. Proceedings - 2015 International Conference on Smart Grid and Clean Energy Technologies, ICSGCE 2015, 30–34. 10.1109/ICSGCE.2015.7454265 Li, C., Yu, X., Yu, W., Chen, G., & Wang, J. (2017). Efficient Computation for Sparse Load Shifting in Demand Side Management. IEEE Transactions on Smart Grid, 8(1), 250–261. doi:10.1109/ TSG.2016.2521377 Lin, C. C., Deng, D. J., Liu, W. Y., & Chen, L. (2017). Peak Load Shifting in the Internet of Energy With Energy Trading Among End-Users. IEEE Access : Practical Innovations, Open Solutions, 5(c), 1967–1976. doi:10.1109/ACCESS.2017.2668143 Maps. (2019). Google maps is no longer free. Retrieved from https://www.shoredigitalinc.com/googlemaps-is-no-longer-free/ Merrick, J., Ye, Y., & Entriken, R. (2017). Assessing the System Value of Optimal Load Shifting. IEEE Transactions on Smart Grid, 1–10. Munshi, A. A., & Mohamed, Y. A. R. I. (2017). Big data framework for analytics in smart grids. Electric Power Systems Research, 151, 369–380. doi:10.1016/j.epsr.2017.06.006 Ozadowicz, A. (2017). A new concept of active demand side management for energy efficient prosumer microgrids with smart building technologies. Energies, 10(11), 1771. doi:10.3390/en10111771 Padhy, N. P. (2002). Demand Side Management and Distribution System Automation : A Case Study on Indian Utility. National Power Systems Conference (NPSC), 308–311. Prodanuks, T., & Blumberga, D. (2015). Methodology of demand side management Study course. experience of case studies. Agronomy Research (Tartu), 13(2), 520–525. Rahman, A., Islam, R., Sharif, K. F., & Aziz, T. (2016). Developing Demand Side Management Program for Commercial Customers : A Case Study. 2016 3rd International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), 1–6. 10.1109/CEEICT.2016.7873061 Rosenow, J., & Bayer, E. (2016). Costs and Benefits of Energy Efficiency Obligation Schemes. The Regulatory Assistance Project. RAP. Roshan, C., Kinley, W., & Sastry, M. K. S. (2014), Home Energy Audit: A Case Study of Phuentsholing, Bhutan. IEEE International Conference on Communication Systems and Network Technologies (CSNT). DOI: 10.1109/CSNT.2014.208 Sastry, M. K. S. (2007). Integrated Outage Management System: An Effective Solution for Power Utilities to Address Customer Grievances. International Journal of Electronic Customer Management, 1(1), 30–40.

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Schuelke-Leech, B. A., Barry, B., Muratori, M., & Yurkovich, B. J. (2015). Big Data issues and opportunities for electric utilities. Renewable & Sustainable Energy Reviews, 52, 937–947. doi:10.1016/j. rser.2015.07.128 Shipman, R., Gillott, M., & Naghiyev, E. (2013). The Mediterranean Green Energy Forum 2013, MGEF13 SWITCH : Case studies in the demand side management of washing appliances. Energy Procedia, 42, 153–162. doi:10.1016/j.egypro.2013.11.015 Shyam, R., Ganesh, H. B. B., Kumar, S. S., Poornachandran, P., & Soman, K. P. (2015). Apache Spark a Big Data Analytics Platform for Smart Grid. Procedia Technology, 21, 171–178. doi:10.1016/j. protcy.2015.10.085 Stötzer, M., Gronstedt, P., & Task, E. T. G. (2011). Demand side management potential a case study for Germany. 21st International Conference on Electricity Distribution. Tu, C., He, X., Shuai, Z., & Jiang, F. (2017). Big data issues in smart grid – A review. Renewable & Sustainable Energy Reviews, 79, 1099–1107. doi:10.1016/j.rser.2017.05.134 You, A., Be, M. A. Y., & In, I. (2018). Assessment of utility side financial benefits of demand side management considering environmental impacts Assessment of Utility Side Financial Benefits of Demand Side Management Considering Environmental Impacts. doi:10.1063/1.5022897 Yu, N., Shah, S., Johnson, R., Sherick, R., Hong, M., & Loparo, K. (2015). Big data analytics in power distribution systems. 2015 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference. doi:10.1109/ISGT.2015.7131868 Zhang, Y., Huang, T., & Bompard, E. F. (2018). Big data analytics in smart grids: A review. Energy Informatics, 1(1), 1–24. doi:10.1186/s42162-018-0007-5 Zhou, K., Fu, C., & Yang, S. (2016). Big data driven smart energy management: From big data to big insights. Renewable & Sustainable Energy Reviews, 56, 215–225. doi:10.1016/j.rser.2015.11.050 Zobaa, A. F. (2018). Big Data Analytics in Future Power Systems (1st ed.). CRC Press; doi:10.1201/9781315105499.

This research was previously published in Novel Approaches to Information Systems Design; pages 137-163, copyright year 2020 by Engineering Science Reference (an imprint of IGI Global).

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Human Capital Management in the Fourth Industrial Revolution Vivence Kalitanyi https://orcid.org/0000-0002-0140-6649 University of Johannesburg, South Africa Geoff A. Goldman University of Johannesburg, South Africa

ABSTRACT This chapter identifies the drivers and challenges of the fourth industrial revolution. The fourth industrial revolution consists of artificial intelligence, big data, robotics, and many others technological innovations. The recent transformation in the global environment is affecting the way businesses are conducted, managed, and the way governments and societies are run. Today, business analysts are faced with the challenge of managing both human and digital workforce effectively without making any stakeholder in the business environment worse off. Hence, human capital management in the fourth industrial revolution involves effective development and deployment of human resources, artificial intelligence, and robotics to achieve organisational goals and objectives. It is expected that the principles underlying human capital management—planning, staffing, development, compensation, and investment in digital workforce—will become more intense and complex.

INTRODUCTION Klaus Schwab, the Founder and Chairman of the World Economic Forum and the International Organization for Public-Private Cooperation, states that throughout history, there have been four industrial revolutions, with the fourth being the present. Schwab (2016) describes the Industrial revolution as the appearance of “new technologies and novel ways of perceiving the world which triggers a profound change in economic and social structures”. The First Industrial Revolution was characterized by steam power, while the Second Industrial Revolution is referred to as the age of science and mass production. DOI: 10.4018/978-1-7998-8548-1.ch079

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The Third Industrial Revolution was marked by the digital revolution, and now we are living in the Fourth Industrial Revolution of dramatic technological expansion and social change (Trailhead, 2019). The objective of this chapter is to create an awareness of new technology revolution and outline the essential practices for managers and Human Resources practitioners about the changes that are brought about by technological advancements, as these changes require constant adaptations and adjustments. Human capital is key to the success of every business, and its effective management is a shared responsibility between the human resources department as well as line management. Since human capital is a source of competitive advantage, its effective management becomes a crucial function to the organization, and this task seems to be becoming more complex in this 4IR whose technology is having significant consequences on our daily lives. At the work place for instance, the actions and behaviors of human capital (ordinary employees* and managers) is influenced by technology in terms of information exchange and performing daily duties for both sets of human capital for the organizational success, hence its adaptation to new realities becomes imperative. Human capital is regarded as the job-relevant knowledge, skills, abilities, energy, commitment and capacity to develop and innovate, possessed by people in an organization (Nel, & De Beer, 2014). Barnes (2008) considers human resources as one of the transforming resources that form part of the input resources in the transformation process that constitutes any operation. Human resources should develop a can-do attitude and provide a company with the much needed competitive advantage if they are properly managed. In his book; “Competitive advantage through people and the human equation: Building profits by putting people first”, Professor Jeffrey Pfeffer of Stanford University emphasizes the above argument when he states that the distinction between top-performing companies from their competitors, lies in the way they treat their human resources (Erasmus, Strydom & Rudansky-Kloppers, 2016).

HUMAN CAPITAL MANAGEMENT Human Capital Management (HCM) is a comprehensive set of practices for recruiting, managing, developing and optimizing the human resources of an organization (Rouse, 2019). By this definition, it is depicted that management has to adopt an approach to human resources management that perceives employees as assets that can be invested in and managed in order to effect maximum profit for the success of the organization. Swanepoel, Erasmus, Schenk and Tshilongamulenzhe (2014) had a similar perspective in defining Human Capital Management (HCM) from a business-based perspective and pointed out that “it is a philosophy of people management based on the belief that human capitals are uniquely important to sustained business success. An organization gains competitive advantage by using its people effectively, drawing on their expertise and ingenuity to meet clearly defined objectives. Human Capital Management (HCM) is aimed at recruiting capable, flexible and committed people, managing and rewarding their performance and developing key competencies”. This task of managing human capital encompasses a number of functions, starting from bringing people into the organization, then train, develop and retain them. Employees need to be fully integrated into the socio-cultural environment of the organization, though these main tasks seem to be costing management’s time, they are expected to perform even harder with the 4th Industrial Revolution.

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The 4IR is about artificial intelligence, abundant information as well as robotic systems, which implies the use of technology in all spheres of human endeavors (Pribanic, 2018). If Human Capital Management (HCM) function is to be relevant in the 4IR by fulfilling its main role - recruitment and selection, training and development as well as retention - for the company’s success, it will also have to embrace technology for example by using available application software in all its activities. In the past, Human Capital Management (HCM) software could be obtained as part of the “Enterprise Resource Planning (ERP) package, and though it is still available, Software as service (SaaS) has superseded it in recent years, and it is most suitable for on premise Human Capital Management (HCM) (Ey Global, 2019). In general, the functions of Human Capital Management (HCM) software are arranged in various categories as described below (ERP, 2018): • • • •

Core Human Resources: Including payroll, benefits administration, onboarding, compliance management and maintenance of employee data. Talent Management: The collective term for the process of recruitment, developing and retaining employees. Talent management suites consists of distinct, yet integrated modules of recruitment, performance management, compensation management, learning and succession planning. Workforce Management: The set of functions for deploying employees with the necessary skills to particular regions, departments or projects. It includes time and attendance management, workforce planning, labor scheduling and budgeting. Service Delivery: Which includes the HR help desk, intranet portals, employee self-service and manager self-service.

These Human Capital Management (HCM) suites also have technologies that cut across functional areas, notably analytics, social media, collaboration and employee engagement. A number of them also allow mobile access to Human Resources (HR) data and applications, especially the self-service features. As a reminder, this chapter discusses in more detail, the way these major Human Resources (HR) functions will be performed in the 4IR.

The Critical Role of Properly Managing Human Capital Authors such as Pribanic (2018) highlighted the critical role of managing human resources. In his view, human capital management is an important task which allows the company to further its goals. He goes on to ascertain that with efficient human capital management system in place, the organization will be able to create and sustain a successful and thriving workforce. Pfeffer as cited by Erasmus, Strydom and Rudansky-Kloppers (2016), argues further that companies that invest in their human capital, are capable of creating a long-lasting competitive advantage that is difficult for their competitors to duplicate. Erasmus, Strydom and Rudansky-Kloppers (2016) highlighted the role of human resources management in the effectiveness of an organization. They state that for an organization to be really effective, top managers need to treat human resources as the key element of that effectiveness. The contribution of human capital management in the organizational effectiveness includes among other aspects: • • • 1594

Assisting everybody in the organization to achieve stated objectives Making efficient use of skills and abilities of the human resources Providing the organization with well-trained and motivated employees

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• • • • • •

Assisting employees in attainment of job satisfaction and self-actualization Developing a quality of work life that makes employment in the organization desirable Assisting with the maintenance of ethical policies and socially responsible behavior Managing change to the mutual advantage of individuals, groups, organizations and the public Executing human resources functional activities in a professional manner, and Being involved in strategic decision-making and strategy formulation of the organization.

Human resources management has been performing these activities in a traditional sense and continues to do so at present. Now that the world is changing due to technological advancements, it remains to be seen how the HCM will cope in this new era. And this is what this chapter discusses in more details.

The Nature of Human Capital Management From the First Industrial Revolution to the Fourth As the work environment changes and becomes more complex, so have the practices and considerations about managing human capital throughout the different industrial revolutions. Any environmental change, affects business management, including human resources management and has to mobilize the workforce to adapt to the new conditions. Industrial revolutions have not only brought new techniques of doing work, but have also caused management to think of how employees should be treated. The sections below describe human capital management practices throughout the history of industrial revolutions (ERP, 2018).

Human Capital Management in the First Industrial Revolution (Age of Mechanical Production) Before the advent of the steam engine -in around 1760s -, steam power was the source of energy in all sectors of life from agriculture to textile manufacturing. Societies were mainly agrarian, which means life was centered around farming. However, the use of steam power in those agrarian societies, resulted in urbanization, due to the fact that people started to rely on steam power to run machines. Railroads as well as steamships appeared and helped people to move from point A to B. All these events, marked the birth of the factory (The Economist, 2016). It started with the mechanization of the textile industry, where tasks that were previously done laboriously by hands in hundreds of weavers’ cottages, were put together in a single cotton mill, thereby marking the beginning of the factory (Nieuwenhizen & Oosthuizen, 2017). The nature of work, was mainly mechanical production, but sometimes with the use of steam power. Furthermore, the work was also concerned with the drawing of railroads. The influx of unskilled laborers made labor cheap enough to exploit in difficult conditions, and working hours long (14 hour shifts for both children and adults). On the other hand, the First Industrial Revolution revolutionized work and affected the human capital of the organizations. In these types of operations, people who headed organizations - currently referred to as managers - came to a conclusion that they greatly depended on their employees in order to achieve their goals of providing products and/ or services. During this period, manufacturing businesses shifted from being home-based businesses to operating from a factory setting, thereby becoming labor incentive operations in organizations. Furthermore, managers started to realize the importance of human capital, and their indispensability for producing 1595

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Figure 1. Steam power use in the first industrial revolution

the products and achieving organizational goals (Rouse, 2019). They therefore realized that they should treat them better, especially that advancing industrialization created a middle class of skilled workers. Currently, information about the exact way management interacted with employees, and how workers were generally managed, is very superficial.

Human Capital Management in the Second Industrial Revolution (Age of Science and Mass Production) The Second Industrial Revolution (IR) which started in the mid-1800s, accelerated a number of inventions, such as gasoline engines, airplanes, chemical fertilizers and basically all other inventions that help us go faster and do more. It is important to know that these advancements in the sciences were not limited to the laboratories only. However, some scientific principles were also introduced in factories. Principles such as assembly lines which hugely supported mass production. In terms of work to be performed, it was mainly mass production. This era of the industrial revolution also saw the advent of electrical power as well as the assembly line techniques in production (Author’s notes, 2019). The approach to Human Capital Management (HCM) was the application of scientific methods to ensure efficiency, cooperation and motivation. Managers believed that all these results could be achieved through incentives (Swanepoel, Erasmus, Schenk, & Tshilongamulenzhe, 2014). These new methods affected human capital of organizations and the main approach to managing it was to empower the lower-level managers who dealt with employees on a daily basis. Another key focus to Human Capital Management during this period, was on ensuring harmony between management and workers. In this regard, Frederick Winslow Taylor (1856-1915) argued that it is important to seek the one best way to do the job, determine the optimum work pace, train people to do the job properly, and reward successful performance by using an incentive pay system. It was during this period of the 2nd industrial revolution, that managers started to develop a perception that when workers and managers knew one another’s expectations, it would result in a situation characterized by cooperation and conflict avoidance. Furthermore, managers’ views on motivating employees, was that money, in terms of a salary is all that was needed. The view that employees needed more than money to be or stay motivated, came in at a later stage in the development of employees’ management practices.

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Figure 2. Mass production during the second industrial revolution

Another view on how to manage human capital during the second industrial revolution, came from Frank Bunker Gilbreth (1868-1924). In his view, employee performance could be obtained through reducing unnecessary motions, while limiting fatigue by paying greater attention to total working environment (Ricci, 2012). Though Gilbreth was hugely influenced by his wife’s research in industrial psychology, both claim to have departed from Taylor’s work on human capital’s management theory on motivation. Simply put, the Gilbreths opinion on human capital management, was on work study, a process by which, management conduct a scientific observation and analysis of work, including a study of the nature and contents of a task, with a purpose of developing a more efficient way of doing a piece of work in the shortest possible time, in order to improve productivity. During this period of Second Industrial revolution, another view that has shaken the world is that of Karl Marx (1818-1883). However, at this point, it would be prudent to reflect upon the contribution of Karl Marx in the broader discussion of HCM, as the Marxist doctrine brought to the fore certain tensions Alvesson & Willmott (2012) that are central to the notion of HCM. However, to fully understand the contribution of Marx, one needs to revert back to a short account of the notion of work itself (Dyer, Humphries, Fitzgibbons & Hurd, 2014). Work, in the form of paid employment, is the path to freedom from a neo-liberal point of view. In this sense, freedom should be seen as the bounties associated with receiving remuneration for work done (Alvesson, Bridgman & Willmott, 2009). This freedom allows people to purchase goods and services to fulfil their needs and wants, its provides the opportunity to invest surplus income for future returns. This freedom also implies the liberty to select a preferred vocation and level of commitment to employment (Dyer, Humphries, Fitzgibbons & Hurd, 2014). This neo-liberal perspective portrays work as a noble act, an emancipation that enables the worker to almost transcend to a different level of existence. In reaction to this notion, Marx proposed that the idea of western capitalism produced a distinctive class relationship between the owners of capital (who also own factors of production) and workers (Alvesson & Willmott (2012); (Wren &Bedeian, 2009). Marx posits that workers have only their labor to offer in exchange for wages paid to them by the owners of capital (Dyer, Humphries, Fitzgibbons & Hurd, 2014). For Marx, this is an uneven power relationship which inherently becomes exploitative and alienating toward the worker (Alvesson & Willmott (2012). Within the capitalist conception, all factors of production, including labor, are seen as objects that are arranged into systems that are managed by the owners of capital in order to maximize profits. This, in turn, objectifies the worker in a sense that workers sell their time for a wage, and this time becomes a commodity that can be bought, sold and controlled by

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the owners of capital (Wren &Bedeian, 2009). Marx, therefore, dispels the notion that paid work leads to freedom and emancipation, and rather posits that paid work leads to objectification of the worker, commodification of the act of labor, and exploitation of the working class (Dyer, Humphries, Fitzgibbons & Hurd, 2014); (Alvesson & Willmott (2012); (Wren & Bedeian, 2009). This, in turn, alienates the worker from the organizational institution established by the owners of capital (Alvesson & Willmott, 2012). The work of Marx, and his colleague, Fredrich Engels, had a marked impact on the First and Second Industrial Revolution, and their views largely influenced the rise of the workers’ union movement in (especially) the UK and Europe (Feher, 2009). Most trade unions are to this day still very socialist in their ideology, and to this day the trade union movement sees itself as a watchdog guarding against the exploitative practices of ‘owners of capital’ (i.e. management) and acting in the best interests of the workers. Thus, the legacy of Marx is that his work highlighted the tension between workers and owners of capital in the pervasive, western capitalist society (Dyer, Humphries, Fitzgibbons & Hurd, 2014); (Alvesson & Willmott (2012). The realization of this tension and the implications it has, not only for the labor force, but also on the business organization, is crucial for HCM, as the modern managerial tendency is to place greater emphasis on not alienating and exploiting the workforce. This can be seen as a strange turn, where management can be seen to agree with the Marxist ideas of objectification and commodification of the workforce and the act of labor. A further approach of human capital management during the 2nd Industrial Revolution, was proposed by Max Weber (1864-1920), whereby he suggested that there must be a distinction between power (the ability to force human capital to obey) and authority (whereby orders are voluntarily obeyed to, by those receiving them). In such a system, those in the subordinate role (staff) see the issuing of directives and orders by those in the authoritarian role (managers) as legitimate. In simple words, …” this management approach is based on a formal organizational structure with a set of rules and regulations that rely on the specialization of labour, an authority hierarchy, and rigid promotion and selection criteria” (Nieuwenhuizen & Oosthuizen, 2017. The first and second industrial revolutions have made people rich and more urban.

Human Capital Management in the Third Industrial Revolution (The Digital Revolution) The 3rd Industrial Revolution took its kick off in the mid-1950s, and immediately took momentum, as it was the beginning of automated production including electronics, as well as, the introduction of computers. It brought semiconductors, mainframe computing, personal computing, and the internet, hence the digital revolution. Things that used to be analog, moved to digital technologies, like the old television that was tuned in by antenna (analog) being replaced by Internet-connected tablets that allowed for the streaming of movies (digital) (Florida’s solar progress, 2012). The success of the Third Industrial Revolution was due to the five pillars that set its strong foundation, and each pillar could function in relation to the others (Florida’s solar progress, 2012). Those were: • •

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Shifting to Renewable Energy: Develop massive wind farms, new technology etc. Transforming the Building Stock of Each Continent Into Micro-Power Plants to Collect Renewable Energies On-Site: Building account for about half of energy consumption next to cars. Attaching turbines and solar cells to buildings will eliminate this energy demand and instead fed the power back into the electric grid.

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• •

•

Deploying Hydrogen and Other Storage Technologies in every building and throughout the infrastructure to store intermittent energies. NASA has already done extensive research and development. Using Internet Technology to Transform the Power Grid of every continent into an energy Internet that acts just like the Internet (when millions of buildings are generating a small amount of renewable energy locally, on-site, they can sell surplus green electricity back to the grid and share it with their continental neighbors). Transitioning the Transport Fleet to Electric Plug-In and Fuel Cell Vehicles that can buy and sell green electricity on a smart, continental, interactive power grid. With the development of smart grid, these vehicles will also be able to sell surplus energy back to the grid.

The Human Capital Management approach that prevailed during the Third Industrial revolution, was “Human Relations approaches”. This approach focuses on individuals working in group settings, with a belief that a satisfied employee, will be productive. It is from this perspective that Abraham Maslow (1908-1970), theorized that human needs are ranged in hierarchies, and that management should strive to satisfy them as the appearance of one need actually rests on the prior satisfaction of another, more prominent need. “Man is a perpetually wanting animal” (Maslow, 1943). Figure 3. Electronics in the third industrial revolution

This view of human capital management holds that if a person is without job, and therefore has no money for food and shelter, will certainly focus on the lower two needs (physiological and safety/ security). This is to say that; such a person will not be able to focus on status or self-fulfillment. On the other side, a person who is financially stable might be looking to make some contribution to the societal needs, or securing non-financial rewards. It is the responsibility of managers to know and understand where their human capitals are in relation to their hierarchy of needs, so that they can motivate them. Another approach to management that has marked the Third Industrial Revolution with a focus on human relations, was the Theory X and Theory Y by Douglas McGregor: 1906-1964). It is essential that managers focus on what promotes employees’ commitment and performance. Assumptions that managers make about their human capital, have been identified as a starting point. Those assumptions are centered around the dimensions of:

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• • • • •

Attitude, Direction, Responsibility, Motivation as well as Creativity.

In brief, McGregor theorizes that managers who make Theory X assumptions believe that workers are naturally lazy, dislike work, side-step responsibility and will do as little as they can in most work situations. On the other side, managers who make Theory Y assumptions believe that workers can enjoy work, desire responsibility and want to accept challenges in their work (MacGregor’s XY theory of management, 1960). Figure 4. Artificial intelligence and robotics in the fourth industrial revolution

Merits of the First Three Industrial Revolutions Each of the first three Industrial Revolutions occasioned enormous changes in the socio-economic lives of the people. Life shifted from being all about the farm to all about the factory, and people moved from rural areas to settle into cities with the introduction of mechanical production. Furthermore, the way people lived and worked fundamentally changed with the discovery of electricity which occasioned mass production, and most recently, the digital revolution affected almost every industry, yet again, transforming how people live, work, managed and communicate (Swanepoel, Ersamus, Schenk & Tshilongamulenzhe, 2014).

The Fourth Industrial Revolution (Artificial Intelligence, Robotics) Today, many technologies people dreamt about in the 1950s and 60s, have become a reality. What we experience every day, is the generic sequencing and editing, artificial intelligence, miniaturized sensors,

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and 3D printing, to name just a few. Innovations are unexpected and surprising. This is the beginning of the Fourth Industrial Revolution (The Fourth Industrial Revolution, 2019). The Fourth Industrial Revolution is the core part of this chapter, which therefore begins with a profound exploration of what it is, and its generic effect, before discussing how managers will deal with their human capital in this era. The Fourth Industrial Revolution is an era which is creating and extending the impact of digitalization in new and unanticipated ways. Therefore, people need to take the time to consider exactly what kind of shifts they are experiencing and how they should collectively or individually benefit from what it has to offer (The Fourth Industrial Revolution, 2019). More importantly, managers at different levels of organizations, public and private, should quickly learn and understand how the Fourth Industrial Revolution is changing the world and understand how better to manage their human resources. Table 1. Theory x and y and their areas of focus

Furthermore, the Fourth Industrial Revolution, is seen as the advent of “cyber-physical systems” which involves new capabilities for human beings and machines. It is said that these capabilities rely on infrastructure and technologies that were brought about by the Third Industrial Revolution, and the Fourth Industrial Revolution represents new ways through which technology becomes embedded within societies and human (The Fourth Industrial Revolution, 2019). New forms of machine intelligence, breakthrough materials, genome editing, and approaches to governance that rely on cryptographic methods such as block chain, are just a few examples.

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Figure 5. Maslow’s hierarchy of needs

As pointed out by the United Nations in 2013, more people in the world have access to the mobile phone as opposed to basic sanitation”. At the same time, the Fourth Industrial Revolution is spreading across the globe, when aspects of the Third and Second Industrial Revolutions are only reaching and maturing in some parts of the World and organizations now. This is what the novelist William Gibson said “The future is already here – it’s just not very evenly distributed.” What managers and business owners need to bear in mind, is the fact that all these technologies and their emergent nature makes many aspects of the Fourth Industrial Revolution feel unfamiliar and to many, threatening. Managers, as well as individuals need to understand that industrial revolutions are driven by people’s choices, more importantly the choices of investors, consumers, regulators and citizens who adopt and employ these technologies within their daily lives. To be able to use it to the benefit of the organisations in their process of managing their human resources, managers have to also embrace and adopt technology. Researchers have compared the Fourth Industrial revolution with exogenous forces comparable with the power of a tsunami. However, it is all about people’s choices and desires. And at the heart of the debate around emerging technologies, is a critical and central question: what do we want these technologies to deliver for us?

Human Capital in the Fourth Industrial Revolution During this 4th Industrial Revolution, it is expected that the principles underlying human resources management; planning, staffing, employee development and compensation and governance is going to become more intense and complex. An example to illustrate this is that job seekers around the world have access and the ability to apply for jobs and positions that are open around the world. Management can’t resist considering these applicants, and if some of them are successful, they will come into a new environment with different cultural settings. We therefore believe that “diversity” is one of the thorny issues human capital managers will have to face in the Fourth Industrial Revolution (Author’s notes, 2019).

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Diversity Kondola and Fullerton (1998) define diversity as “the basic concept which accepts that the workforce consists of a diverse population of people. The diversity consists of visible and non-visible differences which will include sex, age, background, race, disability, personality and workstyle. It is founded on the premise that harnessing these differences will create a productive environment in which everybody feels valued, where their talents are being fully utilized, and in which organizational goals are met”. Diversity will play a key role in how businesses run in this fourth industrial Revolution. Due to current technological advancements, there is an increased pool of people available for organizations to choose from. Therefore, organizations will have staff who belong to different cohorts of ages, with a difference of experiences and ambitions. The diversity on age does also imply that human capital managers will have to deal with employees whereby some are more energetic, while others are wiser and self-controlled. Organizations will therefore become multifaceted, and with the recent creation of the Equality and Human Rights Commission, managers will have to efficiently deal with issues of races, religion, ethnic origin, physical disability, age and sexual orientation (Ricci, 2012). According to Mullins and Dossor, rehabilitated offenders are also offered protection discrimination in employment (Mullins & Dossor, 2013). Conceptual skills are said to be the preferred set of abilities that managers, especially at the higher level of the organization, should have the most. However, in the Fourth Industrial Revolution, interpersonal skills must occupy a crucial role in management day to day operations in order to successfully deal with diversity. The Skills Portal (2019) outlines ten sets of skills that are so essential in the Fourth Industrial Revolution: • •

• • •

• •

Complex Problem Solving: Developed capabilities, essential in solving novel, ill-defined problems in complex and real-world settings. People from diverse background will make the workplace complex and create complicated situations that managers will have to deal with. Critical Thinking: A reflective and reasonable thinking, focusing essentially on what to believe in or what to do. In the Fourth Industrial Revolution, managers are exposed to a number of options in their day to day activities. It requires them the ability to make a trade off among those available scenarios. Creativity: Referred to as the inventiveness and the use of imagination or original thought to come up with something new. In this context, managers will have to come up with new methods of dealing and successfully managing diversity. People Management: Broadly speaking, people management would imply a number of practices including talent management, assessment, evaluate, supervise, processing of staff leaves requests, manage employee relations, hire and fire staff as well as advise upper management. Coordinating With Others: The human resources department and human capital managers do not operate in isolation. They work hand in hand with all other departments of the organisation, especially in terms of recruiting and developing the employees. This task will be even more complex in the Fourth of Industrial Revolution as diversity occupies a major role in the organisation. Emotional Intelligence: Diversity in the Fourth Industrial Revolution requires managers to be emotional intelligent. They need to be aware of and in control of their own emotions, other people’s emotions in the organisation, and be able to handle interpersonal relationships with empathy. Judgement and Decision Making: In the Fourth Industrial Revolution where diversity is unavoidable, judgment and decision making skills are essential as they are concerned with making 1603

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• •

•

considered and effective decisions, where some of them will lead to workable conclusions and form objective opinions especially in matters that affect actions. Service Orientation: Majority of businesses in the Fourth Industrial Revolution are services businesses. In these types of businesses, skills of expert are essential, whether working individually or in teams for the benefits of the customers. Negotiation: Negotiation is the art of persuasion. In the Fourth Industrial Revolution, managers will have to have this set of skills for a number of reasons including negotiation to bring key employees in the organisation, negotiation to secure big contracts and so forth. Lack of these skills can be detrimental to the organisation. Cognitive Flexibility: Perhaps not a popular concept, but its meaning and application has been in the business arena for quite some time. In the Fourth Industrial Revolution.

The success of human capital managers in dealing with diversity in this Fourth Industrial Revolution will depend on a number of factors, including the above sets of skills, as well as managers recognize the impact of technology and globalization, and engage staff on how to explore their attitudes, values, beliefs, and prejudices, even if this might be an uncomfortable process. But, this process can lead to many of the people - in the organizations - discovering things about themselves that may cause emotional pain and therefore adjustment of their behaviours.

Abundance of Information: Enhancing Creativity, Flexibility, and Innovation The World Economic Forum has envisaged that by 2020, creative thinking will be third on the list of the most important skills required to survive and thrive in the Fourth Industrial Revolution. Arts Times (2019) defines a “creative economy” as an “economic system in which value is derived from creative and imaginative qualities, instead of traditional sources such as capital, land and labour” (Howkins, 2013). The Fourth Industrial Revolution is due to transform human capital into creative capital. “The Fourth Industrial Revolution is fundamentally disrupting the way we think, work and interact with each other, hence creativity can be one of the major currencies”, argue Prof Richard Haines and Rosemary Mangope. In this Fourth Industrial Revolution, Human Capital Managers are faced with a situation where their employees are curious about learning how to adjust to an increasingly complex and automated way of life. Human Capital managers have to play a powerful role in framing, shaping, communicating and influencing the future of their employees. However, on the other side, every employee has the responsibility to reflect, question, adopt or resist, review and reconstruct when and where it is required. Human Capital Managers have tasks to guide employees towards creative thinking, which is central in todays’ thriving economies, and this “currency” is due to become more influential as we progress through the Fourth Industrial Revolution (Shivan, 2019). What we can call to reimagine the future.

Enhancing Customer Relations In this Fourth Industrial Revolution, customers are more connected than ever: to one another, to products/services, to the brands themselves. As a result, contact centers feel the pinch which sometimes leads to customer crisis. The customer is increasingly taking over some of the responsibilities that were traditionally performed by the business or their call centers. Customers are now capable of deciding 1604

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how and when to contact businesses for services, and can choose which channel to use for complaints. For example, creating potentially damaging or embarrassing situations for the company, their brands or services on social media. Furthermore, customers understand the level of service expected due to access to technology, and they have no patience for companies that are not responsive or those that are not catching up with evolving standards (Customer Service, 2019). In today’s digitalized environment, customers like to connect and personalize their experiences wherever they are. They are at liberty to connect via text, video or social media, as all these options are open to them. It is also important to remember that old ways of communication; telephone calls, snail mail, and e-mail, are still available to them. All these options at the customers’ disposal gives them the ability to see which service provider is advanced with regard to customer service and to compare to ones lagging behind. Customers can therefore use any channel, and a service provider with more channels will have a competitive edge, as they will be able to meet the demands of the customer wherever they want to be, rather than forcing them to use a particular channel, which they might not want to use. Human Capital Managers have the responsibility to train and drive their employees to these new required standards.

Promoting Sustainable Development Technological advancements are also allowing customers to more and more ask for personalized products and services. This aspect is putting companies under lot of pressure, hence industries are racing to adjust to this new way of doing business and building new networks of partners and digitalized operations. At the same time, the challenge of climate change is rapidly expanding, while the demand for raw materials and resources are outpacing the Earth’s ability to replenish them (The Fourth Industrial Revolution and sustainable development, 2019). Today, the industry and manufacturing sectors account for 41% of global gross domestic product (GDP). The production sectors are positioned directly at the nexus of economic impact and resource usage so that tomorrow’s manufacturers don’t run a shortage of the same resources. It is imperative that managers in the Fourth Industrial Revolution bear in mind this fact and manage their employees in a competitive manner while remaining sustainable. However, there should be no worries for businesses, as the Fourth Industrial Revolution offers a way for manufacturing to increase competitiveness and support regional economies, while assisting in delivering on the United Nations Sustainable Development Goals. This will be enabled by a combination of power of digital, physical and biological technologies (Florida’s solar progress, 2012). The attitude of Human Capital Managers in the success of this goal is crucial.

Artificial Intelligence and the Future of Jobs Academics, executives as well as labour activists and policymakers have contradicting views about the future of jobs with the Fourth Industrial Revolution. Some foresee limitless job opportunities in newly emerging job categories and prospects of improving workers’ productivity as well as alleviating their boredom arising from repetitive and routine work. Others, however, foresee massive job losses and displacement of jobs. The data collected by The World Economic Forum clearly paints a picture of varying situations from industry to industry and from region to region. It is also clear that momentous change is underway, and 1605

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consequently, our actions today will determine whether that change mainly results in massive displacement of employees or emergence of new job opportunities. Human Capital Managers need to take urgent action to manage the near-term transition while building a workforce with futureproof skills. If the opposite presents itself, unemployment and inequality will rise, resulting in reduction of customers for businesses. “Chief Human Resources Officers of leading employers who are among those at the frontline of the emerging trends and are key actors in implementing future workforce strategies” (World Economic Forum, 2017).

Team Works Working in teams has always been an important way of work organization, for the fact that individuals can’t work in isolation and hope to achieve organizational goals. It has a purpose of bringing people together, so that they can perform more efficiently than if they acted alone. Moreover, there are tasks that can only be performed by a group of people acting collectively. The philosophy behind teamwork is the belief that when people work together effectively, a significant contribution can be made as opposed to those working individually. Teamwork requires cohesion, complementary skills, but more importantly good leadership (Nel & De Beer, 2014). Earlier in this chapter, it was mentioned that customers are becoming more demanding, while being aware of the kind of treatment they should get. The Fourth Industrial Revolution allows managers and employees to work together - though they not necessarily physically next to one another – to be able to serve customers in a manner that can satisfy them. Suggestions of how human capital managers can help employees to be more team-players in the Fourth Industrial Revolution include: • • • •

Empowering employees to work in groups so that they can become multi-skilled workers, Continuously developing workers’ skills. Assigning tasks to teams rather than individuals Supervisors acting as role-models for workers and as a buffer between managers and workers.

Virtual Teams One of the merits of the Fourth Industrial Revolution manifesting is the creation of virtual teams. The operation of virtual teams relies on electronic mediation rather than face-to-face communications. People are capable of being connected for meetings despite them being separated by both distance and time. There are various means through which virtual teams can function: • • • •

E-mail, Internet message boards, Groupware Audio or video-conference and more.

Many advantages of virtual teams have been mentioned such as people being able to participate while not being able to physically be there. People on virtual networks have also been characterized as “task oriented”. Like many other applications, the use of technology for virtual teams may encounter some hiccups such as the absence of non-verbal signals and lack of opportunity for social interaction, it 1606

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remains a quick way of conducting meetings in this current industrial revolution (The Fourth Industrial Revolution, 2019). To alleviate some of those problems, it is suggested that virtual team members should meet face-to-face at least once, during the existence of the team, or a bit regular if possible. Human capital managers must understand the role of virtual teams in the current management setting. They need to be able to bring people together under the same vision and objective. They have to be motivated and be equipped with the required resources in order for them to perform. Workers join the work place with a set of skills from school/university, but these skills need to be supplemented with practical skills of how work needs to be done. The responsibility of the human capital manager is crucial in transferring these skills and providing adequate technology, both of which are essential for work to be adequately done and meetings to be easily conducted.

Drivers and Challenges of the Fourth Industrial Revolution Schwab contests that the Fourth Industrial Revolution is driven by nine interrelated and interconnected pillars (Bartodziej, 2017; Vaidya, Ambad & Bhosle, 2018): •

• • •

• •

• •

Big Data and Analytics: To support real time decision making, it will become increasingly more important to collect and evaluate data from many, and varied, sources, such as production systems, customer management systems, and enterprise management systems (Rüßmann; Lorenz; Gerbert; Waldner; Justus; Engel & Harnisch, 2015). According to Witkowski (2017), big data is typified by volume of data, variety of data, speed of generating new data, and the value of the data. Autonomous Robots: As robots become more autonomous, and it is foreseen that will interact with one another and work side by side with humans and learn from humans to perform tasks freely, more accurately, safer and faster than humans (Rüßmann et al. 2015). Simulation: A variety of simulations will be used in future in order to mirror the physical world in a virtual one (Rüßmann et el. 2015). By so-doing, different scenarios can be enacted in real time to understand the different outcomes associated with complex decisions. System Integration, Horizontal, and Vertical: It is foreseen that systems will no longer function in isolation in future, but will increasingly communicate, connect, interact and eventually integrate with each other, not only in terms of the entire production process, but also across the entire value chain (Stock & Seliger, 2016). Internet of Things: This refers to a worldwide network of interconnected objects communicating independently with each other by using standard protocols (Hozdić, 2015). Cyber Security and Cyber Physical Systems: Increased connectivity gives rise to a greater need for securing systems and lines of communication from threats. Therefore, secure, reliable communication lines need to be ensured, and at the same time the need arises for more sophisticated identity and access management tools (Rüßmann et el. 2015). Cloud computing: Cloud based IT platforms are seen as the central pillar for the communication and connection of IT based applications (Landherr, Schneider, & Bauernhansl, 2016). Additive Manufacturing: This implies the use of manufacturing techniques where the part to be manufactured is produced by the addition of raw material, tantamount to 3D printing techniques (Rüßmann et el. 2015). This is in contrast to traditional manufacturing that is more subtractive techniques such as milling or lathing. Additive techniques eliminate wastage, enhance quality, eliminate errors, and save time, which implies huge overall cost savings. 1607

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•

Augmented Reality: The use of augmented reality devices will enable people to have access to greater amounts of information at any given time and in any given location, thereby drastically improving the quality and speed of decision making as well as the implementation of decisions (Rüßmann et el. 2015).

Against the backdrop of these drivers of the Fourth Industrial Revolution, a couple of issue become apparent. Firstly, although the Fourth Industrial Revolution allows for rapid learning and communication, and allows numerous prospects for expanding knowledge and best practice, it is obvious to see that advanced societies will have the most to gain (Prisecaru, 2016). In less developed and developing societies, the Fourth Industrial Revolution will remain a double-edged sword, with the promise of vast gains to be made from many opportunities if these societies can gear up for it sufficiently on the one hand, but on the other the stark reality that they will be left far behind if they do not manage to gear up sufficiently. Secondly, although technological progress leads to the reduction of wastage, more efficiently designed production and consumption systems, the innovations brought about by the Fourth Industrial Revolution will place pressure on the job market (Prisecaru, 2016). Many jobs of today will simply become obsolete, others will be taken over by robots, and therefore, the challenge is that of a reduction of the labour force. However, as some authors point out, this, in turn, will bring about new requirements for education. As the nature of work changes, so to the nature of education will have to change as one of the tasks of education – especially tertiary education – is to prepare people for the world of work. Thirdly, the Fourth Industrial Revolutions challenges our conception about the very nature of work itself. In the Forth Industrial Revolution, being connected is crucial, and so physical location becomes less important (Prisecaru, 2016). Being able to access real time data, anywhere and any-time will be crucial, and the ability to be innovative and inventive on the spot will be more important than to mobilise a management team into a meeting room to solve a problem. Complex problem solving and creativity will become the sought after skills of the future.

RECOMMENDATIONS Although there has been a deluge of theories guiding human capital management throughout the world, the current technological waves make it imperative that human capital managers learn new ways of doing their jobs. The discussions in this chapter focused on the evolution of those theories as well as the conditions, challenges and opportunities of technology, and it appears that the awareness of those changes can hugely benefit whoever is involved in managing human capital of any organization. Therefore, human capital managers need to understand that success in their departments will be easier if their operations are guided by technological advancements. Throughout the world, managers as well as employees use technology in many different ways, and they cannot hide their excitement about it. This excitement is pivotal to the success and it needs to be exploited. It is therefore recommended that human capital managers equip themselves with the necessary technological knowledge that is offered by the Fourth Industrial Revolution. A further recommendation to the organizations, is the investment in technology. In today’s competitive environment, organizational success factors are no longer the only employees’ knowledge and their management. The way technology is pervaded in the organization, is taking over. Among some achievements that are brought by technology, there are remote access to the companies’ information 1608

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and products/services, responsiveness of businesses to the customers, better interaction with clients, as well as productivity. A significant investment in technology will therefore enable organizations to score positive points towards achieving their overall objectives. Finally, governments, schools and learning institutions have to teach technology. Technology is changing the people’s minds and is empowering them with necessary skills needed by organizations. The involvement of government in technology diffusion is necessary especially in the provision of the required technological infrastructure. For academics, it remains imperative to develop and teach theoretical and practical technological frameworks, taking into consideration the needs of the organizations that will provide employment for them.

FUTURE AREAS OF RESEARCH Although the Fourth Industrial Revolution is here, it is still in its embryonic phase, which makes it unclear how it will effectively affect the organizations. Furthermore, the pace with which it moves requires anybody to monitor its changes, especially those who use its different applications in various sections of the organizations. As far as human capital management is concerned, research about value creation through technology is an imperative. As articulated by (Prosecaru, 2016), the Fourth Industrial Revolution will place more pressure on the job market that already seems to be saturated in some environments. In other environments, labour movements closely monitor the behavior of employers, and warn against replacing humans with robots and other machines of similar kind. In this regard, future researches should focus on how technology adoption, could not drive job losses, but rather drives skills development and innovations.

CONCLUSION Throughout our existence, human kind has witnessed periods of rapid technological advancement that led to some major changes in almost all areas, and we are presently living in one such period. The Fourth Industrial Revolution which consists of artificial intelligence, big data, robotics and many more to come, is affecting the way businesses are conducted, managed and the way governments and societies run. Today, customers are more connected than ever before, and are exposed to all kinds of practices around the world. The connectivity has also allowed customers to have access to global markets and they can easily interact and buy from any markets. In this digital age, human capital management has a challenging task of leading workers into this new era. In this fight for coping with technological advancements, managers are faced with a problem of “digital immigrants” employees who have difficulties in understanding technology and be able to use it as their “digital natives” coworkers. However, the onus is on them (human capital managers), to bring those employees up to speed, so that today’s customer can remain in touch with the business. This chapter has provided some guidelines human capital managers can make reference to in order to achieve satisfaction of both workers and customers.

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Pribanic, E. (2018). What role does human capital play in an organisation? Available: https://www. techfunnel.com/hr-tech/what-role-does-human-capital-management-play-in-an-organization/ Prisecaru, P. (2016). Challenges of the fourth industrial revolution. Knowledge Horizons. Economics, 8(1), 57. Ricci, T. (2012). Frank Bunker Gilbreth: Biography. Available: https://www.asme.org/engineering-topics/ articles/construction-and-building/frank-bunker-gilbreth Rouse, M. (n.d.). Available: https://searchhrsoftware.techtarget.com/definition/human-capital-management-HCM Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., & Harnisch, M. (2015). Industry 4.0: The future of productivity and growth in manufacturing industries. Boston Consulting Group, 9(1), 54-89. Schwab, S. (2016). The Fourth Industrial Revolution by Klaus Schwab. Available: https://www.weforum. org/about/the-fourth-industrial-revolution-by-klaus-schwab Shivam, N. (2019). Work study: definition, role and objectives. Available: http://www.economicsdiscussion.net/engineering-economics/work-study-definition-role-and-objectives/21681 Stock, T., & Seliger, G. (2016). Opportunities of sustainable manufacturing in industry 4.0. Procedia Cirp, 40, 536–541. doi:10.1016/j.procir.2016.01.129 Swanepoel, B. J., Erasmus, B. J., Schenk, H. W., & Tshilongamulenzhe, M. C. (2014). South African Human Resources Management: Theory and Practice (5th ed.). Juta. The Economist. The Third Industrial Revolution. (n.d.). Available: https://www.economist.com/leaders/2012/04/21/the-third-industrial-revolution The Fourth Industrial Revolution. (n.d.). Available: https://www.weforum.org/agenda/2016/01/what-isthe-fourth-industrial-revolution/ The Fourth Industrial Revolution and sustainable development. (n.d.). Available: https://www.weforum. org/whitepapers/shaping-the-sustainability-of-production-systems-fourth-industrial-revolution-technologies-for-competitiveness-and-sustainable-growth The skills portal: skills for success. (2019). Available: https://www.skillsportal.co.za/content/10-essentialskills-4th-industrial-revolution Trailhead: The four industrial revolutions. (n.d.). Available: https://trailhead.salesforce.com/en/content/ learn/modules/learn-about-the-fourth-industrial-revolution/meet-the-three-industrial-revolutions Understanding how customer service is changing. (n.d.). Available: https://trailhead.salesforce.com/en/ content/learn/modules/customer-service-in-the-fourth-industrial-revolution/understand-how-customerservice-is-changing Vaidya, S., Ambad, P., & Bhosle, S. (2018). Industry 4.0–a glimpse. Procedia Manufacturing, 20, 233–238. doi:10.1016/j.promfg.2018.02.034

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KEY TERMS AND DEFINITIONS First Industrial Revolution: The first period of major changes that started in late 1700s. It was characterized by mechanical production, railroads invention and steam power. Fourth Industrial Revolution: This period marks the proliferation artificial intelligence in various industry, which took place since the beginning of 21st century. It is affecting human lives in many different ways through its aspects of artificial intelligence, big data, robotics and many more to come. Human Capital: A set of knowledge, skills, abilities, or capabilities and experience possessed by an individual or a group of people, considered in relation to the value to the organization or a country. Human Capital Management: A set of practices concerned with managing people as a resource of an organization. The core of these practices is summarized in employee acquisition, employee development as well their development. Industrial Revolution: Major changes in manufacturing, technology, transportation, and other domains that cause major transformation and adaption of human kind. Second Industrial Revolution: The second period of major changes in human kind that started in late 1800s. The major characteristics of this period are mass production, electrical power, and the beginning of assembly line system of production. Third Industrial Revolution: This period started in late 1900s, and was marked by automated production, electronics as well as the beginning of computers’ use.

This research was previously published in Human Capital Formation for the Fourth Industrial Revolution; pages 100-126, copyright year 2020 by Business Science Reference (an imprint of IGI Global).

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Human Capital Formation for the Fourth Industrial Revolution: The Role of Women Paul Adjei Onyina Pentecost University College, Ghana

ABSTRACT This chapter focuses on the drivers of human capital development in the fourth industrial revolution by examining the role of women. It discusses the role of women in economic development since 570BC. Women are ignored in most important areas in society whereas men are found at the frontline. However, available empirical analyses suggest that when women are empowered, they are able to turn the tables in their favour. The chapter outlines development role played by selected women across time and uses data from studies to show poor representation of women on international bodies and parliamentary seats. Selected women that have led and continue to lead various countries all over the world are presented. This chapter argues that women are important stakeholders in economic freedom. The chapter suggests encouraging society and men in particular to help women become front line participants in the human capital development for the fourth industrial revolution.

INTRODUCTION The world had witnessed production process going through changes over time. The changes are put into stages called industrial revolution. As Sentryo (2019) states, water and steam were used to automate production in the first industrial revolution, the second industrial revolution saw the use of electric energy to generate and manufacture in bulk, the third witnessed electronics and information technology to automate production. There is a fourth industrial revolution on going based on building upon the ideas of the third industrial revolution starting from the middle of the last century. However, the role of women in the earlier industrial revolutions is not well known. DOI: 10.4018/978-1-7998-8548-1.ch080

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 Human Capital Formation for the Fourth Industrial Revolution

Throughout history, women have made great contribution to development in various ways, but the role tend to be unnoticed in the earlier revolutions. As noted by Foundations for Western Culture (FWC, 2018), the First Industrial Revolution had different impact on women, however; were mostly employed to work under difficult conditions. Women from less wealthy families worked to earn income to feed their families from 6.00 am and end at 7.00 pm with only a lunch break of 45 minutes; some started to work even as children. As the FWC (2018) narrations show, women worked under dangerous conditions even in some situations women worked with men who were half-naked (in the coalmines) at a lower wage rate. Unfortunately, during the First Industrial Revolution, salaries paid depended on gender at the expense of women. Thus, women earned about a third or half of male counterparts. Low salaries to women and children benefited the employers who made “good” use by exploiting them. Again, available evidence indicates that some women worked in the mines under harsh conditions; they wore belts around their waste with chains in between their legs joined to the carts that transported the coal into the pits. Despite these poor conditions, women worked hard to contribute to development in their era. Some societies relegate women to the background and ignored, thus, women are restricted in doing certain things and must obtain permission from their husbands before they can do certain things; once they are able to go beyond that, they are considered to be empowered (see Rahman, 2007, and Garikipati, 2008 for some findings on women empowerment). It is important to note that though women have not been at the forefront and have been abandoned in terms of leadership position; their contributions have been tremendous to economic development. In the light of this, the chapter is of the view that women’s contributions to the human capital development of the Fourth Industrial Revolution, is very crucial such that all and sundry have to ensure that the hindrances are removed to allow their participation for better society. In the next section, the role played by selected women in economic development is outlined. The rationale is to portray the importance of women in development despite low recognition, and this low recognition must not continue.

ROLE OF SELECTED WOMEN IN HELPING SOCIETY IN ECONOMIC DEVELOPMENT OVERTIME The role of selected women in their quest for helping society is outline in this section of the chapter. This intends to show that women play an important integral part of any society and must be included and recognized in the quest for development. Thus, the role of gender is portrayed here to be important from the beginning of creation. Here specific role played by selected women since 570BC are listed for readers to see the important role that women spearheaded years back and continue to play to date, and thus in the Fourth Industrial Revolution, women cannot be left out. Thus, highlights of various contributions by selected celebrated women whose efforts changed the world are portrayed, they have been grouped into: i) Women Right Activists and Humanitarians ii) Poets and Writers iii) Musicians and Actress iv) Politicians and Leaders – war leaders and queens v) Scientists and vi) Entrepreneurs. Unless otherwise stated, the study adopted the presentation below from Pettinger (2014). Though brief, what most of them did and to some extend how it affected humanity is provided. It ranges from 570 BC to date.

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Women Right Activists and Humanitarians The following are women who were able to change the world on women right activities and or affected people’s life on humanitarian grounds. One of such womenwas Sojourner Truth (1797 – 1883).She was an African-American abolitionist who championed the rights of women; delivered a celebrated extemporaneous speech in 1851 with the title “Ain’t I a woman?”. This speech provided in basic language equality between men and women. Another one is Margaret Fuller (1810 –1850).She was a women’s right advocate. She authored a book Women in the Nineteenth Century (1845) which was very significant in ensuing that people’s view about men and women changed. The book was credited among the pioneer feminist works. Her argument was that men and women were equal and there was no need for women to depend on men. This, in a way put gender issues on its rightful position. Harriet Beecher Stowe (1811–1896) is another woman worthy to present. She led a campaign against slavery throughout her life. Along the same life and time wrote a novel “Uncle Tom’s Cabin” which was a bestseller that became instrumental to the success of the anti-slavery campaign. Her achievements were so strong that Abraham Lincoln later commented that her writings played a significant role in the wake of the American civil war. Here is another hero, a woman leading the fight against slavery, which was anti-human. Elizabeth Cady Stanton (1815–1902) is the next woman on the roll whose effort affected the world. She was an American social activist, and one of the leaders in the early women’s rights movement. She was an influential person who assisted in the establishment of the early women’s suffrage movements in the US. In 1848, she became famous as the writer of “Declaration of Sentiment”. We cannot leave out Florence Nightingale (1820–1910). Florence Nightingale was a British and a world-celebrated nurse believed to be the mother of nursing. During the Crimean War, she was influential in changing the responsibility and awareness of the nursing career. Her committed service won extensive approbation and was responsible for the considerable enhancement in the treatment of injured military men. In addition, Susan B. Anthony (1820–1906) follows. She was another anti-slavery campaigner in the American and fought for the endorsement of women’s and employees rights. Her campaign started within the self-control movement and this persuaded the need for women to take part in an election. She travelled all over the US and presented numerous speeches with human rights as the focus. Another one is Millicent Fawcett (1846–1929). She was another leading suffragist and equal women rights activist. Fawcett was the leader of the Britain’s biggest suffrage movement, the anti-violent (NUWSS) and was at the forefront to ensure that women have the chance to vote. She was a founding member of the Newnham College, Cambridge. Another woman’s activist is Emmeline Pankhurst (1858–1928). She was another British suffragist who devoted her life to the support of women’s rights. She made use of all means of protest such as violence, public demonstrations and hunger strikes. However, she died three weeks prior to the passing of an act in 1928, which gave women above 21 years the right to vote. Emily Murphy (1868–1933) is the next on the list. She became the pioneer woman magistrate in the British Empire. In 1927, together with four other Canadian women, they sought after and tested an old Canadian law that rejected “women as human beings”. Eleanor Roosevelt (1884–1962) is another one. As a wife and an aide de camp of the U.S. President, F.D. Roosevelt, Eleanor Roosevelt made an important input to human rights activities, an area she crusaded during her entire lifetime. At a point in time, she headed the UN Human Rights Commission and led to draw up the UN Human Rights Declaration in 1948. The last but not the least in this group is the cerebrated Mother Teresa (1910–1997). Mother Teresa was an Albanian nun and charity worker. She dedicated her life serving the poor and homeless, 1615

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this made her to be recognised worldwide as an emblem of voluntary service to humanity. During her Missionary of Charities activities, she is remembered for her devotional services caring for several sick and dying people in Calcutta, India. In 1979, she received the Nobel Peace prize.

Poets and Writers In this category, a presentation of women whose poets and books have influenced humanity in so many ways is provided. The first is Sappho (620 - 570 BCE) as stated by Cartwright (2013). She was among the first ever known female writers. Even though a lot of her poetry cannot be traced, her enormous repute has remained. Plato confirmed that Sappho was among the great 10 poets of his time. She is a woman poet who carried of her work over centuries ago, have influenced mankind to date. Hildegard of Bingen (1098–1179) continues the list. She was a mystic, writer and composer. Hildegard of Bingen lived a reserved life; as a living, she was all the time at the back of convent walls. However, her works including the writings, poetry and music were revelatory during her days. She was a consultant to popes, kings and influential people in her time. To date, most people are still influenced by her writings and music. Most male leaders found Bingen a source of reliable consultant for their various activities. The next on the list is St Teresa of Avila (1515–1582). She was a mystic woman, a Spanish poet, and a Carmelite reformer. St Teresa of Avila lived throughout the Spanish inquisition; nevertheless, she was not put to trial despite her magical revelations. She was instrumental in reforming the traditions of the Catholic Church and guided the religion away from passion. Saint Teresa was an influential woman in religion and in the Catholic Church in particular. Another writer is Mary Wollstonecraft (1759–1797). She was an author in England who wrote an important book in the early days of the feminist pressure group. Her leaflet titled “A Vindication of the Rights of Women” prepared the ground for an ethical and useful foundation in expanding human and political rights to women. Mary Wollstonecraft led the way in the fight for female suffrage. A known argument is that the role played by Mary Wollstonecraft was possible because she was at the forefront of women’s activist. Jane Austen (1775–1817) is another woman to have influenced the world. She is one of the celebrated female authors to date; she authored numerous novels well recognized in contemporary days among the well-known works. They include Pride and Prejudice, Emma and Northanger Abbey. At the time of her writings, female authors were not encouraged, but Jane helped to provide a medium for potential writers to follow; this is something that have changed the world. In addition, mention is made ofEmily Dickinson (1830–1886). She was recognised as one of America’s greatest poets; Emily Dickinson lived most of her life in isolation. The poems that she wrote were available posthumously, and received extensive literary admiration for their brave and exceptional approach. The way she presented her poems became a significant legacy in the 20th Century poetry.

Musicians and Actress Few of the women in this group isKatharine Hepburn (1907–2003). She was an actress in American and an iconic personality in the Twentieth Century film industry. In addition to the four Oscars that she won, Katharine Hepburn was noted for twelve Oscar nominations. Her way of life was irregular for the period and all the way through her performing life, she helped redefine customary ideas the role of women in society. Then Billie Holiday (1915–1959) follows. She was referred to as “First Lady of the Blues”, and was American jazz singer. Billie Holiday was generally acknowledged as the famous and 1616

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most expressive jazz vocalist second to none. Her voice was met with touching, in its emotional passion and poignancy. She died at a tender age of 44, she was associated with the definition of jazz era and her albums are extensively at the shops for sales today.

Politicians and Leaders (War Leaders and Queens) This category has legend women who either led people to war or ruled as a Queen, or was in both positions at the same time. Such women ruled over men and or commanded men at the battlegrounds. The first in the series is the influential Cleopatra (69 BCE–30). She was the very last Ptolemaic ruler of Egypt. Cleopatra was able to defend Egypt from the expanding Roman Empire; her efforts ensured that Egypt was not annexed by the then fearful Roman Empire. To ensure this, she made sure to become an ally of the two most powerful and influential Roman leaders, Marc Anthony and Julius Caesar. As a fact to date, allies do not engage in fighting among themselves. An important point here is that, in those days, the Romans over powered the jurisdictions of male rulers, but Cleopatra was a female ruler whose kingdom was intact. Another one is Boudicca (30 – 61 AD); one of the motivating leaders of the Britons during the First Century. She was the pivot in leading a number of tribes’ insurgency in opposition to the Roman occupation. In the beginning, she was victorious, and her army of 100,000 sacked Colchester and then went to London, but was later defeated. An important question here probably is when a woman led an army numbering over 100,000 “Where were the men”? Also, Eleanor of Aquitaine (1122–1204) is one of such women. She was the first Queen of France. Her two sons Richard and John reigned as King of England. As a well-informed, gorgeous and highly eloquent woman, Eleanor became an influential politician in Western Europe with the assistance of her sons. If the sons of a France Queen could rule England, then it is important to say that the woman was instrumental in the development of the two nations. A teenager girl that is worth of mentioning is Joan of Arc (1412–1431). At age 17, Joan of Arc, a patron saint of France, stirred a French rebellion in opposition to the occupation of the English. A not likely hero, the little Joan, under her leadership was able to become victorious at Orleans. She was later tired, found guilty and burnt alive; this only portrayed her heightened and charisma. The question that needs an answer here is “Was Joan of Arc tried and burned alive because the men feared for her leadership”? Catherine de Medici (1519–1589) is another woman that comes into mind. She was an Italian from Florence, the King of France at a time married Catherine when she was 14 years. She participated in endless political manoeuvrings and sought to augment the authority of her favourite offspring who were all male. This resulted in the catastrophic St Bartholomew’s Day Massacre. Another cerebrated ruler is Elizabeth I (1533–1603). She was the Queen of England at some stage in a time of enormous economic and social transformation; she led England to become a cemented Protestant nation. It was her regime that Britain overpowered the Spanish Armada, which was a major step allowing Britain to develop into one of the world’s leading superpower. Here a woman became the leader in the development efforts of the nation. The list continues with Catherine the Great (1729–1796). Catherine the Great was among the greatest political heads during the Eighteenth Century. It is believed that she was instrumental in ensuring that the wellbeing of Russian serfs improved. She positioned the arts and aided to strengthen Russia as a dominant European country. A woman leading the development of Russia to become an instrumental force to reckon with in Europe. Again, Queen Victoria (1819–1901) cannot be left out. She was a British Queen. Her reign as Head of State from 1837 to 1901 saw her rule as one of the biggest empires ever 1617

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seen. Though Queen Victoria was an influential figure in British politics, she was detached from political beliefs; her entire regime became known as the era of Victorian values. She influenced people’s life without political power. A cerebrated woman in the person of Yaa Asantewaa (1840 – 1921) needs to be mentioned. She was the Queen Mother of Ejisu in Ghana. Though a farmer, she was an intellectual politician and a human right activist who led the Asantes to fight the British in a war that became known as the “Yaa Asantewaa War” during the Colonial Days (1900), when the British have arrested and deported all the powerful Asante Chiefs to Seychelles Island. The cause of the war was that the British demanded the Asantes to surrender their Golden Stool to them, which the Asantes refused, to them (Asantes), the Golden Stool is sacred and must not be surrendered to any stranger causing the arrest of all the powerful men. Yaa Asantewaa then, took the mantle as the leader of the Asantes to fight the British. For her brevity, people have named their children after her (See Mensah, 2010 for details). Another ruler is Indira Gandhi (1917–1984). Indira Gandhi was the first woman Prime Minister of India. Her regimes were between 1966–77 and 1980–84. She was regarded as very influential and strict leader; she by a whisker escaped a military take over when she accepted a call for election, when the “emergency period” of 1977 ended. Her Sikh bodyguards assassinated her in 1984 because of her role in the attack, and probably her entry of the Golden Temple1.

Scientists There is another category made up of women scientists. The first on the list here is Elizabeth Blackwell (1821–1910). In Elizabeth Blackwell, she is not only the first Britain born woman to obtain a medical degree in America, but also the first female member to be on the medical register in the UK. She was instrumental in breaking down social barriers, facilitated women to be acknowledged as doctors, and became accepted in the well sought for medical profession in all nations. Then, the Nobel Prize Winer Marie Curie (1867–1934) follows. She was a Polish/French scientist, became the first female to break the men dominated award, not only did she receive the Nobel Prize, but also the first winner of the Nobel Prize for two different categories - the first for research into radioactivity Physics, 1903, and the second for Chemistry in 1911. Some years later, she teamed up with other scientist to develop the first ever X-ray machines. A for-runner as an inventor of medical equipment. It continues with Dorothy Hodgkin (1910–1994). She was a British chemist who received the Nobel Prize based on the significant discoveries of the composition of both penicillin and afterwards insulin. Her discoveries brought a considerable improvement in the delivery of health care services. An exceptional chemist, Dorothy additionally dedicated most part of her life to the peace pressure group and supported nuclear disarmament campaign. Rosalind Franklin (1920 – 1958) is another woman hero. Rosalind was another British Chemist who made great contributions to the composition of DNA and RNA for people to understand, and this paved the way to the discovery of the DNA double helix. Franklin was influential in the chemistry of coal and viruses.

Entrepreneurs The entrepreneurs are the last but not the least. These businesspersons have made great contributions to the development of humanity. One of them is Helena Rubinstein (1870–1965). As a businessperson, Rubinstein established the world’s first cosmetic firms. This American businessperson’s ventures proved 1618

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enormously flourishing and, in later years, she became a philanthropist and use the profit earned to support benevolent enterprises in the area of learning, art and health. Thus, she was a successful entrepreneur. Another one is Coco Chanel (1883–1971). Coco Chanel was French fashion designer and celebrated as a renowned innovative fashion designer. She helped define the role of women as fore –runners in development and was influential in women fashion and wear, throughout the 20th Century. She revealed revolutionary opinions, as an innovation and specific contribution to the society; she redesigned men’s traditional clothes such that they benefited women. In addition to these women of substance, the same applies to Rachel Carson (1907 – 1964) who was an American conservationist and a pioneering environmentalist. Also, Simone de Beauvoir (1908–1986), a French existentialist philosopher, who wrote a book with the title “The Second Sex” which portrayed the customs of sexism that dominated society and history. The first women Prime Minister of Great Britain Margaret Thatcher (1925–2013) is on the list, she ruled for over 10 years and promoted free markets. Anne Frank (1929–1945) of Dutch Jewish ancestry was an author of the most extensively read books in the world. More can be said about Wangari Maathai (1940–2011), who was a Kenyan-born environmentalist, pro-democracy advocator and women’s rights campaigner and a winner of Nobel Peace Prize for hard work to prevent conflict among others. The first woman Prime Minister of a Muslim country Benazir Bhutto (1953–2007) of Pakistan is worth mentioning. She led Pakistan to move from a dictatorship to democracy, became the Prime Minister in 1988, put in place social reforms and assassinated in 2007. There are other women who are living legends as of January 2019 such as Queen Elizabeth II (1926–) of Great British ruling since 1952. Her rule has seen quick social and economic transformation for Britain and her Commonwealth countries. Betty Williams (1943–) joined forces with Mairead Corrigan in a crusade to end the sectarian violence in Northern Ireland. The duo founded the Community for Peace and received the Nobel Peace Prize in 1977 (post-dated for 1976). Then, an Iranian lawyer, Shirin Ebadi (1947–) is another woman in the category. She is a human rights activist in Iran, stands for political dissidents and other activities to support democracy and human rights, won the Nobel Peace Prize in 2003. Oprah Winfrey (1954–) of the US, talk show host and businesswoman is another influential woman activist. She was the pioneer woman to have possession of her own talk show. More can be said about the current German Chancellor Angela Merkel (1954 -). She has ruled since 2005 and have become one of the most influential women in the world. Madonna (1958 –), an American pop star is also another woman living legend. She is believed to be the most successful female musician. She is credited with over 250 million records sold. Additionally, she features in films. The British J.K. Rowling (1965–) author of the extraordinary best-selling Harry Potter series also comes into the picture. Hilary Clinton (1947 –), a US politician who was the first women to context for the high office of US on the ticket of a main political party (Democrats) and lost to Donald Trump. She was the Secretary of State from 2009 to 2013. The list is endless, amongst them female activists who have contributed enormously, influenced their communities, and the world at large not listed above, they affected diverse facets of life despite the challenging moments. As noted earlier, a young woman tried, and burnt alive, but her influential role found in the book of annals. Others being consultants, their clients include powerful men in society such as pope, kings and many more. Some were able to lead their nation to war, activists took a frontline in various campaigns, and scientists invented medicines and machines. Despite these roles undertaken by the female gender, their recognition at local and international levels fall below expectations. The next section presents representation of women at various levels in society and at the international scene. An major outcome is when it will emerge that there is a need for vigorous campaign across the globe to ensure that the important role women play in development are seen in the Fourth Industrial Revolution. 1619

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EVIDENCE OF NEGLIGENCE OF WOMEN IN DECISION MAKING Despite the role played by women in society and how they have influence people, they have very poor representation at various levels in the decision making process. For example, according to Inter Parliamentary Union (IPU, 2016), in 2015, there were two different forms of parliamentary elections held in 58 countries across the globe. First, countries that have quota system, and then those with no quota system. In 28 countries, a type of quota system used was on gender basis; out of the 34 chambers that conducted elections, women won only 28.3 percent. For the remaining 30 countries that did not use any quota system for 36 chambers, women won only 13.5 percent of the representations. This is a clear indication that should there be a quota system based on gender; there is an increase in women representation. Based on the IPU (2016) report, there were some situations where quota for gender representation were ignored; however, the electoral process allowed “more” women to be elected as was the case in Denmark and Finland of the Nordic Region in Europe, and then Argentina and Guyana in Latin America. These arrangements helped women representations to increase to 25.8 percent compared to 22.3 percent in countries that elected women through the majoritarian elections. Table 1on the next page depicts the countries involved and the actual number of women involved in the various elections. As seen from the Table 1, the highest women representation was 50 percent in Mexico’s lower house, the only statutory requirement that shows equal representation. The least was Federal States of Micronesia with no woman representation. Another observation is that only four countries with no quota required, had women representations that crossed the 30 percent mark. However, empirical evidence suggests that countries with discrimination based on gender appear to experienced lower economic growth and poverty reduction compared to countries that have equality between genders; again where there is unequal gender treatment leads to inefficient outcomes (Bradshaw, Castellino and Diop, 2013). The focus now is on the representation of women on both international and governmental bodies discussed below after the table.

Women Representation at International and Governments Bodies This section gives evidence from United Nations and Governments bodies to show low participation of women in the decision making process. The intention is to depict how women have been ignored by the “powers” that have the “ability to bite” but do not take actions. Women representations at various bodies (both international and governments to conferences) have not been encouraging. For example, the United Nations has been campaigning for the increase of women on all bodies and had been guided by the following “Normative Framework” table below to ensure women compete and have equivalent membership as men when it comes to participating in United Nations Governing Bodies activities (see U. N. Women, 2017 for details). Based on the above framework, there are various actions to ensure that the number of women participating in United Nations governing bodies increase. Along these lines, there are several calls for exceptional actions, precise targets and capacity-building programmes (UN Women. 2017). However, there may be the needed legal, executive actions, and other regulatory frameworks. Through the above framework portrayed in the table, and the United Nations System-wide Action Plan, there has been an increase in gender representations. The following remarks from UN Women (2017) shows the general progress in this respect:

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Table 1. Candidates that contested for election in 2015 Total Seats

No. of Men

No. of Women

% Women

Success Rate

Quota

Comoros

203

198

5

2.5

20.0

No

Ethiopia (lower house)

1828

1527

301

16.5

70.4

Yes**

Haiti (lower house)

1621

1492

129

8.0

0.0

Yes*

Haiti (upper house

232

209

23

9.9

0.0

Yes*

Marshall Islands

98

93

5

5.1

60.0

No

Nigeria (lower house)

1730

1504

226

13.1

8.8

No

Nigeria (upper house)

747

619

128

17.1

5.5

No

Majority Electoral System

Poland (upper house)

423

305

58

13.

22.4

No

Tuvalu

32

29

3

9.4

33.3

No

United Arab Emirates

330

262

78

23.6

1105

No

United Kingdom (lower house)

3971

2938

1033

26.0

18.5

Yes**

United Republic of Tanzania

1260

1012

238

19.0

57.1

Yes*

Afghanistan (upper house)

73

58

15

20.5

20.0

Yes*

Saint Kitts & the Nevis

23

22

1

4.3

100.0

No

Majority and Appointed

Saint Vincent & the Grenadines

43

37

6

14.0

0.0

No

Singapore

181

148

35

19.3

62.9

No

Andorra

106

72

34

32.1

29.4

No

Egypt

2573

2638

210

8.2

42.4

Yes*

Lesotho (lower house)

1136

799

337

29.7

8.9

Yes*

Mexico (lower house)

4436

2248

2248

50.0

9.4

Yes*

Micronesia (Fed. States of)

34

34

0

0.0

0.0

No

Switzerland (lower house)

3788

2480

1308

34.5

4.9

Yes**

Tajikistan (lower house)

285

255

30

10.5

40.0

No

Venezuela

1799

1128

671

37.3

3.6

No

Burkina Faso

6944

4870

2074

29.9

0.6

No

Croatia

2311

1354

957

41.1

2.4

Yes*

Denmark

799

549

250

31.3

26.8

No

Estonia

872

636

236

27.1

10.2

No

Finland

2146

1301

845

39.4

9.8

Yes**

Netherlands (upper house)

261

178

83

31.8

31.3

Yes**

Oman (lower house)

596

576

20

3.4

5.0

Yes**

Pakistan (upper house)

121

103

18

14.9

61.1

Yes*

Poland (lower house)

7858

4530

3328

42.4

3.8

Yes*

Portugal

4453

2553

1900

42.7

3.8

Yes*

Mixed Electoral System

Proportional Representation

continues on following page

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Table 1. Continued Majority Electoral System Sri Lanka

Total Seats

No. of Men

No. of Women

% Women

Success Rate

Quota

6151

5595

556

9.0

2.0

No

Surinam

387

259

128

33.1

10.2

No

Turkey

8426

6400

2026

24.0

4.0

Yes**

Source: IPU (2016) Legend: Yes* Statutory quota, Yes** voluntary party quota, Success rate~ Total number of women candidates divided by the total number of women elected (not shown in table).

“Progress towards achieving equal representation of women across the United Nation system remains slow. The lessons learned from reporting through UN-SWAP show that without intensified efforts and appropriate human and financial investments in this area, as well as in others such as organizational culture and resources allocation, the overall rate of progress in women’s participation will stagnate or decline” (p. 8). The situation at other international bodies is not different; Krsticevic (2015) details the under-representation of women in international bodies as of September 2015, noting that gender parity requires a 50 percent representation. The study analyzed women representation of 84 international bodies over the years, and show the involvement of women on these bodies to be very poor and worrying. The poor representation of women indicated by Krsticevic (2015) showsa glooming picture. For example, since the establishment of the International Court of Justice (ICJ) in 1945, only four women have served as members, though there have been 106 members for the ICJ. As noted, three of the four members are still members of the ICJ. This is not different from the International Tribunal of the Law of the Sea (ITLOS), it started to operate from 1994 and has seen 40 judges who have served on its bench. Sadly, only one woman has served and continue to serve on the ITLOS. International Criminal Tribunals have not been different. In Rwanda, the International Criminal Court compose of 10 permanent members with just two women as members. The Yugoslavian version has 17 permanent members with two women. Perhaps, the only exception is the International Criminal Courts with 17 permanent members; the three current serving women hold the top positions of that court namely the President, Vice President and the prosecutor. The table below shows the poor representation of women on some international courts.

Table 2. Normative framework Year

Expected Framework

1979

Convention on the Elimination of all forms of Discrimination against women

1980

Resolution 1990/15 of the Economic and Social Council

1985

Beijing Declaration and Platform for Action

2003

Resolution 58/142 of the General Assembly

2006

50th Session Commission on the Status of Women

2015

2030 Agenda for Sustainable Development

Source: Culled from UN Women (2017).

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Table 3. Percentage of women as members since establishment of some international courts Court

Percentage of Women Representation

International Court of Justice

3.8

European Court of Justice

8,4

International Tribunal of the Law of the Sea

2.5

Source: Krsticevic (2015).

Women representation of major international tribunals is just 17 percent on the average (Krsticevic, 2015). Not only did she looked at the under representation of women, but also presented areas where women have both high and low representations at the United Nations treaty bodies as indicated below in Table 4. Table 4. UN treaty bodies with high and low representation of women Treaty Body Committee of the Elimination of Description against Women

Percentage of Women

Remarks

96

High

Committee on the Rights of the Child

61

High

Committee on the Prevention of Torture

52

High

Committee on the Rights of Persons with Disabilities

33

Low

Committee against Torture

30

Low

Human Rights Committee

27

Low

Committee on the Elimination of Racial Discrimination

22

Low

Committee on the Protection of the Rights of Migrants Workers and their Families

21

Low

Committee on Economic, Social and Cultural Rights

17

Low

Committee on Enforced Disappearances

10

Low

Source: Krsticevic (2015).

The next area of another interest is at the national level, both government bodies and parliamentary seats. The U. N. Women (2017) states that representation of women at the highest level of governments, public and private organizations are insignificant. The table below shows the view of the UN Women. Further analysis shows a very disappointing situation, for example, the five countries where 50 percent of the ministers were women are Finland, France, Liechtenstein, and Sweden all in Europe, and only Cabo Verde in Africa. The American countries, Pacific countries and the Asians “could not pass the test”. As shown above, the percentage of women representation is insignificant. Per the analysis of UN Women (2017), it is only 30 countries in the world that have more than 30 percent of their ministers to be women, 46 countries have women ministers below 10 percent. The other dynamics is that most of the women ministers have social related portfolios namely family, education and culture. Such powerful portfolios as defense, finance, and economic sectors mostly do not have women as ministers. In terms of parliamentary seats across countries and regional level, admittedly there have been increases over the years. The table below portrays averages in the world and regional parliamentary seats occupied by women for 1995 and 2016. 1623

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Table 5. Women in national governments globally as at January 2017 Area

Women Representation

Number, Head of State

10

Number, Heads of Governments

19

National Parliamentarians

23%

Countries with 50% + Women ministers

5

Countries with 50% + Women Parliamentarians

2

Source: UN Women (2017).

Table 6. World and regional averages of women parliamentarians in july 1995 and january 2016 [regional ranking in the order of the percentage point change] Region

Average

1995

2016

World Average

11.3

11.3

22.6

Americans

+14.5

12.7

27.2

Sub-Saharan Africa

+13.4

9.8

23.2

Arab States

+13.2

13.2

17.5

Europe (Nordic Countries included)

+ 12.2

13.2

25.4

Pacific

+ 9.5

6.3

15.8

Asia

+5.6

13.2

18.8

Source: IPU (2015).

The percentages do not take into account parliaments for which data is not available As shown, there has been an improvement of the parliamentary seats of women for the past two decades. The good news is that for each region, there have been improvement in the parliamentary seats between 1995 and 2016. Apart from the Arab States and the Asian regions, the rest all saw an improvement by at least 100 percent increase in the number of parliament seats occupied by women. However, the percentages still fall below the 50 percent equality or parity. Country specific data from 1998 to 2017 is available from World Bank (2018). The above presentation shows that participation of women at the decision-making level at international bodies and government bodies are very poor, and this does not encourage economic development. This is not the best because Bradshaw et al. (2013) indicated that available evidence show that when there is gender equality on various bodies, there is rapid economic development. UN Women affirmed this in the following quote: “Established evidence reveals that women’s participation improves political decision-making and can contribute to the formulation and implementation of policies and strategies that better respond to the rights and interests of women and girls. Women demonstrate leadership by working across party lines through women’s caucuses and by taken on issues of gender equality, including the elimination of gender based violence, parental leave and childcare, as well as gender-equality laws and electoral reforms. Women’s participation in United Nations-led peace processes correlates with a greater likelihood of agreements reached and implemented. Similarly, peace agreements brokered with the involvement of women are

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more likely to be gender-responsive, sustainable, enhance civil society participation and ensure broader community acceptance” (UN Women 2017:8). The religious bodies’ recognition of women is not different from the international and government bodies as discussed above. The fact is that to date, no woman has been ordained as a Pope, the highest office in the Roman Catholic Church; moreover, all the Cardinals that meet to appoint a new Pope when the seat becomes vacant are men. Whereas some churches ordain women as priests, others do not. For example, the Church of Pentecost in Ghana (one of the fastest growing Pentecostal Church in Africa if not the world) has no ordained woman as a minister. By December 2018, the church has 1,476, ordained men ministers, 36, 412 ordained elders (men), 25,936 ordained deacons (men), but only 49, 103 ordained deaconesses (women) (see The Church of Pentecost 2019). However, women form over 70% of the overall adult membership. Apea (2019) provides a case for the ordination of women from the Pentecostal perspective in Ghana. Another example of unrecognition of women is that in the Moslem world, a woman Islamic cleric is unknown.

EMPIRICAL EVIDENCE OF WOMEN’S CONTRIBUTION IN SOCIETY Below is an important point from Bradshaw et al. (2013) on the effectiveness of women on development approach: “World Bank research has highlighted how the poor are less likely to engage in higher risk return activities and the result is that the return on their assets is 25-50% lower than for wealthier households, …... While not a gendered analysis, women’s relative poverty, lack of assets, and lack of experience might mean they are particularly risk averse keeping them from higher return economic initiatives. However, women have been shown to use micro-finance effectively to develop small enterprises and are recognised as good at paying back loans” (p. 9) emphasis added. This statement confirms the following empirical microfinance finding from different studies as shown below. Many studies such as Pitt and Khandker (1998), Pitt, Khandker, McKernan, and Latif (1999), Pitt, Khandker, Choudhury, and Millimet (2003), Pitt (2001) have established that the impacts of programme membership vary significantly by the gender of those involved in the programme. For instance, in Bangladesh, Pitt and Khandker (1998) establish that the stream of expenses on consumption rises 18 taka2 when 100 taka is provided as credit to women; in contrast, 100 taka credit to men was found to increase consumption expenditure by only 11 taka. Pitt et al. (2003) investigated how credit programs affect the poor and health status of children in rural Bangladesh. Among others the study found that an increase in loan to women by 10% bring about 6.3% increase in the arm circumference of their female children, this was two times the increase on comparable credit granted to men. Again, loans granted to women were found to positively affect, though little outcome on, the arm circumference of their male children (noting that the actual factor was not determined). On the average, an increase of credit to women by 10% brought an increase in the arm circumference of girls by 0.45 cm and boys by 0.39 cm. At the same time, the same increase in credit to men increase the arm circumference in girls and boys by 0.21 cm and 0.14 cm respectively. The study found that loans granted to women when estimated bring a large, positive, and statistically significant outcomes on the heights based on age for boys and girls. The result listed specific elasticities as 1.53 for male children and 1.14 for the female. The increase in the height of girls was 0.36 and for boys was 0.56 centimeters every year on the average, when credit to women increases by 10%. However, same credit to male had 1625

 Human Capital Formation for the Fourth Industrial Revolution

negative point estimates though not significant. Thus, when credit to men increases by 10%, analysis found that it reduced the height of girls by 0.16 cm and boys by 0.11 cm yearly. Pitt, Khandker and Cartwright (2006) investigated the outcome when male and female participate in group-based loan programmes. The outcome indicated that when women take part in credit programmes, they become empowered, are more involved in decision-making process in the household, have access to both pecuniary and economic resources. The study also found that women become more enhanced in terms of social network activities, are more active with better bargaining power compared with their spouses. Credit to women allow them to discuss important issues such as how to ensure good parental care and family planning very well. On the other hand, credit provided to men tend to have negative impact on numerous areas of women empowerment. The areas are physic all mobility access to services and financial possessions, influence to achieve domestic transactions. In India, Garikipati (2008) found that the households of women that joined self-help groups (SHG) that provided credit were less vulnerable and better placed than their counterparts who were not members of the SHG. More importantly, the results suggested that the longer a woman participate in the credit programme, the possibility of her household coping with drought and income diversification rises. D’Espallier, Guerin, and Mersland (2013) gave an empirical evidence when female gender becomes the focus of microfinance lending programme using dataset across the world. Among others, the study found targeting women for microfinance ensure repayment, but steep cost does not allow complete financial performance. In addition, in Ghana, Onyina (2014) found female household heads 2.2 times higher placed to make purchases of food items for the entire household when they are involved in microfinance credit programme than men household heads. Sivagandhi and Dash (2017) investigate how women are empowered through Self Help Groups and women employment prospects in India. They found that the two complement one another. Additionally, they stated that a change in the per capita income and poverty rate offer the opportunity for female employment and outreach of women SHGs throughout the Indian states. The study provided a list of reasons such as women having access to bank loans, credit facilities and women being educated as added advantage to increase the rate at which women empowerment initiative. Again, in Ghana, Addai (2017) found that there was a statistically significant positive correlation between microfinance and women empowerment, in two areas namely economic and social, though the relationship depends on two things, a married person or otherwise and level of education, marital status and educational level of the women with age having no controlling effect.

SOLUTIONS AND RECOMMENDATIONS From the above discussions, it is clear that women play important role in economic development. However, marginalized at all levels in life including representations at both domestic and international levels, as well leaders in religious organizations. To actually benefit from the fantastic role women play that can help bring the development of human capital for the Fourth Industrial Revolution into reality, the following have been outlined for all and sundry to help ensure that women come out of the doldrums to the forefront of various activities. First, there must be a strong political will to ensure that women are not marginalized, all political appointments and or representations are gender based. To be able to arrive at a balance in gender to participate in the decision-making processes at all levels, the UN Women (2017) put up the following recommendations to for consideration. 1626

 Human Capital Formation for the Fourth Industrial Revolution

An expectation is that all agree to introduce ad hoc measures in the forms of quota for all delegations to the various governing bodies so that women will compose of at least 30 percent with the aim of increasing it up to 50 percent. UN Women (2017) echoed IPU (2015) recommendation on how to treat women on electoral activities. Among others, the IPU (2015) stated that a lot of the existing electoral system in most countries have closed their doors to women, for women find it very difficult to break through when it comes to electoral system. More resources committed to women issues will go a long way to minimize the difficulties. Even coverage of women candidates relegated to the background. Hence, the IPU recommends political leaders to come out with polices that will allow women to participate in the electoral processes without hindrances. Placing them at better areas or at constituencies where they can easily win their contested seats. To the IPU (2015), in 2015, there was an improvement in this; however, there is a need for more action. It will go a long way to increase the number of women at the decision-making process, and ensure that women representation at international levels improves. Thus, more commitment is needed are all levels. At the national levels, delegates should be composed of equal gender, starting from 30 percent to be at par with men at the 50 percent with time. To achieve this requires explicit policies and mandated targets put in place and followed. The use of enforcement measures so that expected targets become reality in going forward. Ensuring that leadership positions in conferences and committees will also have Chairs and Co-Chairs for both male and female members, the headship may rotate from time to on gender bases. More education, campaign, training, and capacity building for women will help bring women to the forefront in the development of human capital for the Fourth Industrial revolution. Along the same line, Bradshaw et al. (2013) have called for the need to support women’s access to official or unofficial justice systems, and to task them to be responsible for the promotion of all women’s equal privileges, prospect, and involvement. With the implementation of these, women will become leaders at the forefront of the Fourth Industrial Revolution. In addition, there must be a focus on capacity building programmes for women representatives to help increase women’s involvement in leadership and other technical positions. Increase awareness so that both men and women will be trained on gender related issues so that there will be understanding by all for capacity building in favour of women involvement. In this case, there is a need for more financial support for such a gender related training for men and women. Designate a fund for women in terms of travelling to international bodies to ensure they fully participate in all activities. Here there is a need to re tool provision of basic services as a whole. For example, according to Bradshaw et al. (2013), if we consider financial, environmental, and health predicaments, women are the most affected. Consequently, there is a need to intensify the provision of such services to women and girls. Again, it is important to come out with policies to deliver inexpensive, excellence childcare and satisfactory healthcare delivery; this in the end would help women’s accessibility to paid work, to ensure that they are not vulnerable. Meantime, it is required that full time employment avenues are equally accessible to men and women. For women and access to finance, as well as continue to provide social protection, and many more areas in favour of women. Establish strategies so that it will build on the awareness for equal gender representation and ensuring that nominations and appointment to attend conferences and meetings and announcements of notices of polls are gender balanced. Promote opportunities to brainstorm with robust advocacy to ensure that there is strong network and collaboration among female representatives and between the delegates that represent women organizations. It cannot be otherwise but agree with Bradshaw et al. (2013) that “Key for economic growth is the promotion of women’s economic rights which entails promoting a range of 1627

 Human Capital Formation for the Fourth Industrial Revolution

women’s rights: their sexual and reproductive rights and rights to education, to mobility, to voice, to ownership, and to live free from violence” (p. 13). At the same time, it requires robust communication strategies put in place to educate people on the need for gender equality, for the human capital development as expected in the Fourth Industrial Revolution.

FUTURE AREAS OF RESEARCH From the above analysis, a need arises for future research direction. Such research direction on the acceptability of women in the society in general, as to what monitor women contribute in the economic development. Future research to depict the increasing number of women representation on various international and governmental bodies to portray the involvement of women in decision-making will be a step in the right direction. This will go a way not only to know the progress of under representation of women discussed in this chapter, but also the direction of much needed further campaign and education.

CONCLUSION This chapter has presented the need for women, the mostly marginalized in society projected very high in all areas in achieving their role in the development of human capital for Fourth Industrial Revolution. It argued that women have poor recognition in society and relegated to the background, such that what society expect from them become a reality. It listed some women and the role they played over the years. The chapter portrayed that, the role of women cut across all spheres of life. Nonetheless, the important roles that these women played at various times in history show an avoidable vulnerability of women, in expectation of recognized role in economic development. That’s, women resisted all decisions and barriers to ensure that they contribute their part in economic development. In conclusion, the argument is that women must be involved in other decision-making in economic development to be part of the development of the human capital for the Fourth Industrial Revolution process. In addition, encouraging women in all areas in the society will be an important activity in this respect.

REFERENCES Addai, B. (2017). Women Empowerment through Microfinance: Empirical Evidence from Ghana. Journal of Finance and Accounting, 5, 1-11. . doi:10.11648/j.jfa.20170501.11 Apea, E. (2019). Are the Churches flying with One Wing? A New Look at Ordination of Women. Emmanuel Apea. Bradshaw, S; Castellino, J., & Diop, B (2013). Women’s Role in Economic Development: Overcoming the Constraints. Sustainable Development Solution Network. A Global Imitative for the United Nations. Cartwright, M. (2013). Sappho of Lesbos, Palazzo Massimo. Ancient History Encyclopedia. Retrieved June 10, 2019, from https://www.ancient.eu/image/2123/

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D’Espallier, B., Guérin, I., & Mersland, R. (2013). Focus on Women in Microfinance Institutions. Journal of Development Studies, 49. doi:10.1080/00220388.2012.720364 Foundations of Western Culture (FWC). (2018). Industrial Revolution: Women’s Roles in the Industrial Revolution. Retrieved November, 19, 2018 from http://foundations.uwgb.org/womensroles/ Franck, R., & Galor, O. (2015). The Complementarity between Technology and Human Capital in the Early Phase of Industrialization. Retrieved August 27, 2018 from http://d.repec.org/ n?u=RePEc:bro:econwp:2015-3&r=his Garikipati, S. (2008). The Impact of Lending to Women on Households Vulnerability and Women’s Empowerment: Evidence from India. World Development, 36(12), 2620–2642. doi:10.1016/j.worlddev.2007.11.008 IPU. (2016). Women in Parliament in 2015 The year in review. Geneva: IPU. Krsticevic, V. (2015). Gender Equality in International Tribunals and Bodies: A global campaign for gender parity in international representation an achievable step with global impact. Academic Press. Mensah, N. P. W. (2010). Nana Yaa Asantewaa, The Queen Mother of Ejisu: The Unsung Heroine of Feminism in Ghana (An unpublished Master of Arts thesis). University of Toronto. Onyina, P. A. (2014). The Impact of a Microfinance Programme on Clients: Evidence from Ghana. Scholar’s Press. Pettinger, T. (2014). Women who changed the world. Retrieved on April 21, 2018 from www.biographyonline.net Pitt, M., & Khandker, S. R. (1998). The Impact of Group-Based Credit Programs on Poor Households in Bangladesh: Does the Gender of the Participant Matter? Journal of Political Economy, 106(5), 958–996. doi:10.1086/250037 Pitt, M. M. (2001). The Effect of Nonagricultural Self-Employment Credit on Contractual Relations and Employment in Agriculture: The Case of Microcredit Programs in Bangladesh. Bangladesh Development Studies. Pitt, M. M., Khandker, S. R., & Cartwright, J. (2006). Empowering Women with Micro Finance: Evidence from Bangladesh. Economic Development and Cultural Change, 54(4), 791–831. Pitt, M. M., Khandker, S. R., Chowdhury, O. H., & Millimet, D. L. (2003). Credit Programs for the Poor and the Health Status of Children in Rural Bangladesh. International Economic Review, 44(1), 87–118. doi:10.1111/1468-2354.t01-1-00063 Pitt, M. M., Khandker, S. R., McKeman, S.-M., & Latif, M. A. (1999). Credit Programs for the Poor and Reproductive Behavior in Low Income Countries: Are the Reported Causal Relationships the Result of Heterogeneity Bias? Demography, 36(February), 1–21. doi:10.2307/2648131 PMID:10036590 Rahman, S. (2007). The Impact of Microcredit on Poverty and Women’s Empowerment: A Case of Bangladesh (Unpublished PhD Thesis). University of Western Sydney.

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Sentryo. (2019). The 4 Industrial Revolutions. Retrieved on February 23, 2019 from https://www.sentro_net/the 4 Sivagandhi, S., & Dash, D. P. (2017). Microfinance and women Empowerment-Empirical evidence from the Indian states. Regional and Sectoral Economic Studies, 17(2), 61–74. The Church of Pentecost. (2019). General Headquarters 16th Extraordinary Council Meeting. Accra: The Church of Pentecost. Women, U. N. (2017). Shaping the International Agenda: Raising Women’s Voices in International in Intergovernmental Forums. Geneva: International Gender Champions. World Bank. (2018). World Development Indicators. Available at https://data.worldbank.org/indicator/ SG.GEN.PARL.ZS World Economic Forum. (2016). The Future of Jobs: Employment, Skills and Workforce Strategy for the Fourth Industrial Revolution. Retrieved August 27, 2018 from https://www.weforum.org/reports/ the-future-of-jobs World Economic Forum. (2017). System Innovative on Shaping the Future of Education Gender and Work. Retrieved August 27, 2018 from https://www.weforum.org/systeminitiatives/shaping-the-futureof-education-gender-and-work

KEY TERMS AND DEFINITIONS Activist: A person who believes strongly in political or social change and works hard to try and make this happen. Approbation: Approval or agreement, often given by an official group or praise. Carmelite Reformer: Someone who made an attempt to reform a friar or nun of a contemplative Catholic order at Mount Carmel during the Crusades and dedicated to Our Lady. Feminist: A person who believes in feminism, often being involved in activities that are intended to achieve change. Development: When someone or something grows or changes and becomes more advanced. Gender: The physical and/or social condition of being male or female. Human Rights: The basic rights which it is generally considered all people should have, such as justice and the freedom to say what you think. Mystic: Someone who attempts to be united with God through prayer. Nobel Prize: A set of annual international awards bestowed in several categories by Swedish and Norwegian institutions in recognition of academic, cultural, or scientific advances. Oscar: One of a set of American prizes given each year to the best film, the best male and female actor in any film and to other people involved in the production of films. Ptolemaic Ruler: The Roya family which ruled the Ptolemaic Kingdom in Egypt in the Hellenistic period between 305 to 30 BC. Spanish Inquisition: An organization within the Roman Catholic Church that existed from 1542 to 1834 and which was established to punish people whose religious beliefs were considered wrong.

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St. Bartholomew’s Day Massacre: Was in 1572, a targeted group of assassinations and a wave of Catholic mob, directed against the Huguenots (French Calvinist Protestants) during the French Wars of Religion. Modern estimates for the number of dead across France vary widely, from 5,000 to 30,000. Suffrage: Refers to the right to vote in an election, especially for representatives in a parliament or similar organization. Suffragette: A woman in Britain, Australia and the United States in the early 20th century who was a member of a group that demanded the right of women to vote and that increased knowledge of the subject with a series of public protests.

ENDNOTES 1 2



Women are not allowed to enter the Golden Temple in India. Taka is the currency used in Bangladesh the country of the study.

This research was previously published in Human Capital Formation for the Fourth Industrial Revolution; pages 205-228, copyright year 2020 by Business Science Reference (an imprint of IGI Global).

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Chapter 81

New Generation of Productive Workers:

How Millennials’ Personal Values Impact Employee Productivity in Industry 4.0 Rok Cresnar https://orcid.org/0000-0003-0056-8985 University of Maribor, Slovenia

ABSTRACT The main purpose of this chapter is to consider how can the millennials’ personal values impact employee productivity in the future organizational environment of Industry 4.0. In the modern business environment, major changes are happening in many fronts. On one hand, we have the phenomenon of digitalization and Industry 4.0, and on another hand, we see that the millennials are rapidly taking over important roles and positions in those organizations that are impacted by digitalization. If we consider the notion that the new industrial revolution behind Industry 4.0 will be based on major improvements in productivity due to the mediating effect of a technological revolution, then the role of employee productivity or better say the millennials’ productivity will be paramount. This chapter shows that based on deep analysis of millennials’ personal values worldwide, the millennials hold prominent personal values, which correspond well with Industry 4.0 readiness and competency models, meaning that they can significantly impact the productivity of an organization.

INTRODUCTION It has been well established through the decades of research endeavors, that personal values of an individual lay in the base of his or her attitudes, beliefs and behavior (Ajzen, 1991; Schwartz, 1992; Schwartz, 1994; Weber, 2017). But however nowadays, with the ever more apparent changing of the society and its constituent subsystems (Scholz et al., 2018), such as the economic system, personal values are of significant practical importance in trying to understand peoples’ behavioral inclinations DOI: 10.4018/978-1-7998-8548-1.ch081

Copyright © 2021, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

 New Generation of Productive Workers

(Črešnar & Jevšenak, 2019; Weber, 2017). Due to the rapid and increased digitalization, many jobs will be automated out of existence and personal values can be seen as a behavioral indicator of the inclined proactive behavior toward tackling these challenges. Proactive attitudes toward work challenges can indicate the level of achievable productivity (Syverson, 2011). The issue of productivity is also at the frontline of the generational changes in the workplace and here the millennial generation will be the most important to consider (Twenge et al., 2012; Weber, 2017; Akers, 2018). The millennial generation is often described as the largest generation ever to exist, reflecting trends of worlds’ population growth and counts as much as 80 million individuals. This generation was born between 1985 and 2005 and they hold, due to their upbringing and with it connected different life experiences, very different personal values (Gibson et al., 2009; Twenge, 2010; Ng et al., 2010; Twenge et al., 2012; Weber, 2017). Understanding the millennials’ personal values can help us to predict their broader impact on the economic system and within it their impact on organizations. Furthermore, it can help us to assess their potential for being productive and successful in the new organizational environment of Industry 4.0 (Črešnar & Jevšenak, 2019). Industry 4.0 is a current European trend in the economic and business practice, reflecting philosophies of the improvement of organizational workings and behaviour with the integration of digital technologies, automation, cybernetics, and artificial intelligence into the business processes (Bressanelli et al., 2018; Piccarozzi, Aquilani & Gatti, 2018; Müller et al., 2018). These integrations are supposed to fundamentally improve the levels of productivity to such extent that the next industrial revolution could become a reality (Wang et. al., 2016; Klausing, 2017; Yazdi et al., 2018). Here lies the fundamental problem of the conceptualization of Industry 4.0. The focus that the academic literature and business practice have on this issue is in the large part oriented on technologies and their impact on the business transformation (see for example, Qin & Cheng, 2017; Zhong, 2017). This focus is understandable, as technological areas are the ones that enable these advances. But however, there are a few significant issues regarding the role of employees in the processes of digital transformation. First, not only technological parts of organizational workings will be changing in Industry 4.0, but the entire organizational philosophies will as well (Scholz et al., 2018; Müller et al., 2018). For instance, the organizational environment will become more focused on teamwork, collaboration, become more multicultural and multidisciplinary, etc. (Erol et a., 2016) Second, the work processes and work organization will also change (Prifti et al., 2017; Enke et al., 2018). Some job profiles will no longer be needed, therefore, the question of how to assure the productivity of those employees is a separate issue altogether. But however, the productivity of those remaining employees and also the newcomers’ will be of paramount importance. Millennials are nowadays continuously taking over important roles in organizations (Ng et al., 2010; Twenge, 2012) and the question of how productive they can be in the new business environment and whether they have the right personal values to help them be productive remains open. To comprehensively address this issue, we utilize a distinct theoretical notion of employee productivity paradigm. Due to the still not comprehensively defined meaning of the concept, it is necessary to apply it only in the sense of the research problem. Therefore in this paper, we explore the relationship between personal values of millennials and the relevant factors of employee productivity. This study innovatively considers personal values as one of the main indicators of productivity in the changing business environment. In contrast, previous studies have applied the concept of personal values to try and predict certain outcomes in the economic and business practice, such as leadership behavior (Grojean et al., 2004; Bruno & Lay, 2008; Graf et al., 2011), organizational ethics (Fritzsche, 1995; Nedelko, 2015), innovativeness (Dabic, Potocan, & Nedelko, 2016), etc. 1633

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This paper offers an assessment of millennials’ personal values from previous studies and on these bases argues on the possible predictions of their impact on employee productivity in the digitalized environment of Industry 4.0. First, we present the concept of personal values and argue on their practical and predictionary behavioral importance. Next, we present the nature of millennials’ personal values and explore how they are different from previous generations. Here we draw cognitions from the results of the millennials’ personal value study (Črešnar & Jevšenak, 2019). Second, we present the concept of employee productivity, its complexity, and within it, which factors and aspects of the concept will likely be major areas of change in Industry 4.0. Lastly, we offer the assessment of the changes to the business environment, how they can impact employee productivity and what impact millennials’ personal values will likely have on employee productivity.

THEORETICAL BACKGROUND Personal Values Through the decades, a growing body of literature has established that personal values lay at the fundamental level of person’s perceptions and by extension have an impact on behavior (Kluckhohn, 1951; Rokeach, 1973; Schwartz, 1992, Schwartz, 1994; Schwartz, 2012). Considering their broad implication potential (Sagiv et al., 2017), this concept is also applicable in the business and economic sciences, such as considered in this paper. Most comprehensively, personal values can be defined as a plethora goals, beliefs, perceptions, etc. that dwell in the realm of abstraction and determine individual’s view of the world (Rokeach, 1973; Schwartz, 1992). But as recently proposed, personal values act as a cognitive filter that influences an individual’s judgment of specific real-world circumstances (Črešnar & Jevšenak, 2019). This means that this concept has enabled the scientists to have a base on which they are in part able to predict individuals’ future behavior (see Grojean et al., 2004; Twenge, 2010; Nedelko, 2015; Weber, 2017, etc.). This notion is most obviously seen in examples of how the scientists have applied them to try and predict a plethora of situations in the business economy. For example, personal values with connection to leadership or management (Grojean et al., 2004; Nedelko, 2015; Akers, 2018; Nedelko & Potočan, 2019), with connection to organizational ethics or social responsibility (Potočan et al., 2016; Nedelko et al., 2017), with connection to management innovativeness (Nedelko & Potočan, 2019), or for instance with connection to supporting innovativeness (Dabic, Potocan, & Nedelko, 2017), etc. At the beginning of the personal values research, the concept was considered very broadly (Rokeach, 1973). But, with increasingly more empirical studies conducted and with a more comprehensive understanding of what they are, the universal structure of personal values has emerged, which is still in use today (Rokeach, 1973; Schwartz, 1992). Currently, the most popular theory that is used in trying to determine personal values of millennials is Schwartz’s (1992; 1994; 2012) theory, which has universal structure and implications. Schwartz (1992) has through the rigorous empirical testing and factor analyses determined that personal values have the following structure. But however, it should be stated that the theory is now more refined (see Schwartz, 2012). The theory used in this paper’s practical implications consists of 10 dimensions, which vary and differ according to a person’s motivation. These are namely:

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1. Power: Is connected to a person’s perceptions of authority, wealth, domination, and control over people. 2. Achievement: Is connected to a person’s perceptions of capabilities, competencies, and personal success. 3. Hedonism: Is connected to a person’s perceptions of self-gratification, pleasure, and enjoyment of life tendencies. 4. Stimulation: Is connected to a person’s perceptions of exciting, varied, and challenging life experiences. 5. Self-Direction: Is connected to a person’s perceptions of the importance of freedom, creativity, and curiosity in life. 6. Universalism: Is connected to a person’s perceptions of tolerance, understanding others, and the need to protect nature and all that lives on earth. 7. Benevolence: Is connected to a person’s perceptions of honesty, helpfulness, and forgiveness towards others. 8. Tradition: Is connected to a person’s perceptions of fit and acceptance of the culture, and religion. As well as the perceptions of the importance of customs and ideas of the society. 9. Conformity: Is connected to a person’s perceptions of the importance of obedience, politeness, and restrain from actions that may cause harm to others. 10. Security: Is connected to a person’s perceptions of the importance of stability, safety, and harmony of the nation, relationships, and of the individual. Still further, 10 main types of personal values can be collapsed in 4 higher dimensions of values, where first two reflect the personal interests of an individual and the second two reflect the social interests of an individual (Schwartz, 1992): 1. 2. 3. 4.

Self-enhancement, encompassing power, hedonism, and achievement; Openness to change, encompassing stimulation, self-direction, and hedonism; Self-transcendence, explaining universalism and benevolence; and lastly Conservation, encompassing conformity, security, and tradition.

How a dominant personal value triggers behavior is yet to be shown, however, a simple and proven idea is that personal values drive attitudes, which trigger behavioral inclination or intention that results in the certain behavioral outcome. The process is presented in figure 1 (Ajzen, 1991; Schwartz, 1992; Verplanken, 2004; Roccas & Sagiv, 2010; Weber, 2017). Figure 1. Theoretical model of how values influence behavior

Source: (adapted from Ajzen, 1991; Schwartz, 1992; Schwartz, 1994) applied in Črešnar and Jevšenak (2019).

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Personal Values of Millennials Several studies from the field of social psychology and business have shown that millennials stand out, with regards to their personal values (Twenge, 2010; Ng et al., 2010; Twenge et al., 2012; Weber, 2017; Akers, 2018). This is the largest generation ever born on earth and has grown up under the influences of constant advances in digital technology computation, and digital communication, that gave them unique life experiences, which were not experienced before by previous generations. This had a significant effect on the way millennials personal values have developed (Weber, 2017). General findings indicate, that millennials are to the degree more open, and hedonistically leaned in values, and also more flexible and adaptive compared to the previous generations. Previous generations, are more conservative and value more the value spectrum of tradition encompassing security, stability, and hard work (Lester, 2011; Ahn & Ettner, 2014). Some findings also indicate that millennials are more oriented toward values of self-enhancement than self-transcendence (Twenge, 2012; Weber, 2017). This is especially true in the Western developed economies, where most prevalent studies were made. But however, the opposite categories of values can be found prevalent in other European countries such as Slovenia (Črešnar & Jevšenak, 2019). Cognitions from the literature indicate that values connected to competencies, achievement, and power may prevail in some countries. Still, highly ranked values of benevolence, universalism, hedonism, and openness, and understated values of conservation are common for millennials across the world. (Schwartz et al., 2012; Gibson et al., 2009; Ng et al., 2010; Twenege et al., 2012; Weber, 2017; Črešnar & Jevšenak, 2019). Looking more broadly on current transitional changes in the society and by extension in the business environment, which are predicated on the phenomenon of digitalization (Scholz et al., 2018), one fundamental result of the transformation can be outlined. Implementation of the practices of digitalization is supposed to raise the levels of productivity across the board (Wang et. al., 2016; Klausing, 2017; Yazdi et al., 2018). But the arguments are in the literature mostly centered around the business process productivity, which is directly impacted by the changes. However, the role of employee productivity is here less known and less understood (Črešnar & Nedelko, 2017). Fundamentally, the idea put forward in this paper is that personal values can show the orientation of behavioral fitness with the determinants of employee productivity and can insinuate, whether there can be an expected influence and impact between them. The results of the analysis of prevalent personal values of the millennials in Slovenia are presented in table 1 and can coo borate the above cognitions (Črešnar & Jevšenak, 2019).

RESEARCH METHODOLOGY The sample and data for the analyses of mean values were obtained in Slovenia in 2018. Analyzed were N=371 cases with no missing data, which represents a large enough base to draw meaningful conclusions from the results (Couper, 2000). Sample respondents ranged between 16 and 35 years of age, with an average of 22.62 years and the standard deviation of 2.85 years. There were 58 percent of female and 42 percent of male respondents. Additionally, 58.5 percent of respondents live in the countryside or in a suburban town and 41.5 percent of them live in a large city. 1636

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To devise a measurement instrument, Schwartz’s et al. (2012) refined theory of personal values was used. To analyze the data, 10 main sub-dimensions of personal values were constructed, where 26 items in the questioner were collapsed into the subdimensions using theoretical representations of which items measure certain aspects of the value spectrum (Schwartz, 1992; Schwartz, 1994; Schwartz, 2012). Respondents were able to select an answer on the Likert type scale from 1 = this is nothing like me to 6 = this is very much like me. Table 1. Prevalent personal values of the Millennials in Slovenia Personal Value

Mean

Standard Deviation

Variance

Rank

Benevolence

5.11

.79

.62

1.

Self-direction

5.00

.77

.60

2.

Hedonism

4.87

.88

.78

3.

Universalism

4.82

.72

.52

4.

Achievement

4.69

1.02

1.04

5.

Security

4.46

1.10

1.22

6.

Stimulation

4.15

1.04

1.08

7.

Tradition

3.42

1.56

2.44

8.

Conformity

3.40

1.11

1.24

9.

Power

3.05

1.06

1.12

10.

Notes: Sample size is 371. Source: Črešnar & Jevšenak (2019)

Acknowledging research results form Črešnar & Jevšenak (2019), it is apparent that certain groups of personal values are ranked higher than others. Specifically, at the top half of the distribution are values of self-transcendence and openness to change, which fall under the sector of personal growth and freedom from anxiety. At the bottom half of the distribution are values of self-enhancement and conservation that are connected to self-protection and anxiety avoidance (Schwartz et al., 2012). Another observation, which has implications in the prevalence of top-ranked values in the population is variability. Top-ranked personal values have lower variability than low ranked ones, which means that they are more significant and well rooted in society (Črešnar & Jevšenak, 2019). This makes it more likely that the Millennials will behave more consistently in accordance with their prevalent personal values. Prospective behavior based on the prevalent personal values of openness and self-transcendence has important implications in the future business environment, where such values are needed and can thus indicate better employee productivity.

THE CONCEPT OF EMPLOYEE PRODUCTIVITY For its broadness, it is difficult to find a universal definition of the concept, but more often than not this is a subject of confusion. We can find it for example, that it is synonymized with labor productivity

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or even more narrowly, with labor intensity. Another example of the usage for the term, with relation to human resource practices, is workforce productivity (see Schmidt, et al., 1982; Choobineh, 2017). To consider the concept of productivity as currently understood, it most often indicates the measure of the relationship between the number of inputs and the number of outputs produced in a given process (Bartelsman & Doms, 2000; Syverson, 2011). But with connection to the employee productivity spectrum, the term is not so easily defined. The definition that states that the concept refers to the added value per hour of work is one with the most consensus in the literature (Bartelsman & Doms, 2000; Lieberman & Kang, 2008). But, however, with regards to the prevalent managerial literature, employee productivity consists of two main determinants; namely; labor productivity and broader economic productivity (Walters, 2010; Črešnar & Nedelko, 2017). However, labor productivity will, under the influence of Industry 4.0, be less significant with connection to selected research problem, as some jobs that are in some way related to operational processes, will be automated out of existence. The real issue with regard to business processes automation is how to ensure the productivity of those employees, who will consequently become redundant? This question has real merit in addressing the subsequent aspects of a broader and more abstract set of economic factors that will be more prevalent to understand when determining employee productivity in the future (see Črešnar, 2017; Črešnar & Nedelko, 2017). For the apparent broadness and the variety of meanings, there is a need to consider the role of single factors, which may have implications in determining employee productivity. This is very evident in a large amount of literature, that considers individual or single aspects that influence employee productivity, for instance, work, labor, contributions, creativity, employee well-being, etc. (Črešnar & Nedelko, 2017). Therefore, it is obvious that in determining employee productivity, we ought to select those aspects that relate to the phenomenon of digital transformation in order to best see the effect. In most considerations, employee productivity is in some way connected to work processes. However, with the current paradigm shift of organizational workings through digitalization, the meaning of what constitutes work processes is rapidly changing. But nonetheless, most authors connect work processes to work design, work measurement, work organization and work performance (Krajewski & Ritzman, 1996; Ried & Sanders, 2002; Črešnar & Nedelko, 2017). As the focus and the problems here are connected to work processes, it is feasible to adopt the concept of micro-employee productivity in the problem consideration (Bartelsman & Doms, 2000; Črešnar, 2017). Micro-productivity focuses on employees at the level of an individual organization that encompasses smaller systems, which are most often referring to individual industries and individual organizations.

Prospective Changes of Employee Productivity due to the Mediating Effect of Digitalization On the one hand, we can see and hypothesize that phenomena of digitalization, industrial automation, and advances in artificial intelligence will impact operational processes more severely than the rest (Klausing, 2017; Yazdi et al., 2018). Here, the need for human labor will in some cases be entirely diminished (Erol et a.l, 2016; Fifeková & Nemcová, 2016) as the machines will be able to significantly improve productivity, maintain themselves, and work continually, which humans are unable to. Arguments can be made that a large part of the wave connected to the next industrial revolution, will be based around these increases in productivity, rather than on unknown effects of digital technology in the future.

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On another hand, a large portion of jobs and roles will be entirely transformed (Kagermann, et al., 2013). Meaning that here lies a major gap in the question of how to secure employee productivity. To address the issue of those individuals and other employees needed knowledge, skills, and abilities, Industry 4.0 competence models started to emerge (see Erol et al., 2016; Vukomanović et al., 2016; Prifti et al., 2017; Enke et al., 2018). They outline the most significant areas of change and propose, which competencies are the most important and can enable employees to be productive. Those competencies are connected on the personal level to the employees’ abilities to be able to self-manage, self-reflect, be team players, be able to collaborate, be able to lead teams and projects, etc. On the professional level, many competencies are connected more to the abilities of employees to use digital and automation technologies, have good statistical and research abilities, be able to plan and predict situations, and have the understanding of big-picture business goals, etc. These sets of competencies will be of greater use because the work environment will also change. Work will become more flexible and more efficient, due to the connectivity of cyber-physical systems with employees (Kotýnková, 2016). In some cases, work can become more complex (Prifti et al., 2018), because repetitive tasks will be less needed. Work-life balance will also be a major factor, as it is shown, that millennials value their time outside of work (Ng et al., 2010). This is suspected to be the case, as with better organizational flexibility allowed, employees can be better able to balance the activities of professional and personal development, and time (Kagermann, et al., 2013).

DISCUSSION How is Digitalization Changing the Business Environment in Terms of Work? In the most scientific and practical debates nowadays, the phenomenon of digitalization seems to be among the most important in the current business setting (Scholz et al., 2018; Bonilla et al., 2018). Digitalization has an incredible ability to store real-world data using digital symbols and then representing these data to the user through a plethora of digital technologies (Scholz et al., 2018). This has a variety of practical implication potential, wherein business with the use of digital technologies, the removal of significant bottlenecks in communication and control is promising the next industrial revolution, camouflaged in Europe under the phenomenon of Industry 4.0 (Bressanelli et al., 2018). This is achievable on the bases of integration of a few main technological philosophies into organizational workings, namely; automation, internet of things, cyber-physical systems, artificial intelligence, and smart manufacturing (Wang et al., 2016; Roblek et al., 2016). Because the impact is systemic, along with technologies, the philosophical and people-related aspects of organizational workings will also change. This is most clearly seen with the emergence of aforementioned Industry 4.0 competence models and here the perceived dynamics between employee productivity and individuals’ personal values are of strictly practical importance. Modern business environment is in large part becoming more focused on personal interactions, collaborations between multicultural and multidisciplinary teams, adaptivity, agility and flexibility of an individual, ethical responsibility and personal integrity stands, etc. (Erol et al., 2016; Prifti et al., 2017, Enke et al., 2018), which is with regards to needed personal values requiring benevolence and universal tendencies. Addressing professional aspects, employees will have to be highly creative and subsequently innovative, open to learning, change and new opportunities, emotionally stable and mature, be self-aware 1639

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and able to self-manage (Erol et al., 2016; Herold et al., 2016; Prifti et al., 2017), which in terms of personal values calls for high self-direction and stimulation values connected to the dimension of openness. Because the aforementioned directions of where the business environment is going, some of the more individually focused personal values will not be of such importance. Meaning that personal values of achievement, power, conservation, security, etc. may not be the best for securing millennials’ productivity and fitness, but as mentioned, some studies have found these values prevalent (Gibson et al., 2009; Ng et al., 2010; Twenege et al., 2012; Weber, 2017). This may in turn indicate a misfit and a potential problem of securing higher employee productivity. On a different note, these values are essential for future leaders (Bowels, 2015; Herold, 2016; Akers, 2018) and they can enable them to more efficiently achieve results and to be productive in this way. In consequence of these changes, it is important to assess how will these values connect with the changing work environment of Industry 4.0.

How Millennials Personal Values Impact Employee Productivity in Industry 4.0? High tendencies of millennials toward values of self-transcendence and openness to change suggest that they are a good fit for the new business environment of Industry 4.0 (Črešnar & Jevšenak, 2019) and subsequently indicate that higher levels of productivity may be possible to achieve in certain areas of business. Because they will be more open toward various cultures in the workplace, more benevolent in their attitudes toward others, more understanding of others and, have generally progressive attitudes, they will be more productive in areas of cooperation and teamwork. These are essential parts of the business transition from the standpoint of broader organizational workings (Erol et al., 2016; Herold et al., 2016; Prifti et al., 2017). Due to their attitudes, cooperation, and teamwork, it will be easier for them to be productive and it is thereby expected that different cultures and disciplines will not present obstacles in the path toward achieving organizational goals, which will in term be achieved more efficiently and with higher levels of productivity. Regarding personal aspects of their values, it is indicated that high proclivity toward creativity, benevolence, hedonism, stimulation, and self-direction may also positively impact areas of work connected to creative and innovative problem solving, outstanding ethical responsibility, better social interactions, and the need for agility, flexibility, and adaptivity. Millennials will, therefore, be able to be very productive in areas of making decisions based on creative and innovative problem-solving abilities, and in areas of working on dynamic projects that require agility and openness, because they will be better able to adapt to changes, which are most often outlined as the core of business transformation. High hedonism values also indicate better employee productivity. Which may sound strange, because fundamentally hedonism reflects leisure and enjoyment of life (Schwartz, 1992; 1994). But because work will be more flexible, millennials’ will be better able to rest and peruse activities outside work. Thus, they will be better able to satisfy their higher needs connected to their needs for freedom and varied life, which can have a beneficial productive impact on their innovative and creative work performance at times when they entirely focus on it. On a contrary note, self-enhancement or proclivity toward values of power and achievement can hinder the required need cooperation and teamwork. But with regards to the needed multicultural and multidisciplinary integration, better productivity can be expected as all studies found that values of conservation are consistently not prominent in millennials.

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RECOMMENDATIONS Tackling Organizational Challenges to Secure the Millenials’ Productivity In any setting of organization’s transformation, there are many changes in the way that organization work and behaves that present certain discomforts and challenges for its effective and successful future workings (Bareli et al., 2007). Since Industry 4.0 promises a paradigm shift in organizational workings and behavior on a systemic level, this subject is important to consider. As argued throughout this paper, major areas of change in organizational workings and behavior will be centered around the business processes and people related aspects. For organizations to improve employee productivity related to the business processes, they should consider centering their activities and goals around the people and less on technologies and processes. Meaning that organizations should include the millennial workforce in the broader organizational strategy in the sense that they enable for them proper communication channels, manage their expectations and prepare an adequate teamwork environment (Myers & Sadaghiani, 2010). In the case of the millennial population, traditional and strictly hierarchical organizational structures may not be the best fit (Newman, 2000). Millennials are more inclined toward open and flat organizational structures (Weber, 2017), which enable them more freedom and responsibility and also reflect their values. For Millenials to be productive organizations should enable them to work outside strict hierarchical bonds by preparing their roles accordingly. Another very interesting approach, that could help organizations to secure employee productivity is the proper utilization of key management tools. Management tools as a plethora of concepts and ideas have the ability to help solve highly complex organizational problems (Nedelko et al., 2015), which are in the sense of digital transformation inevitable. Some key management tools of digital nature, such as corporate blogs, social media programs, etc. and more traditional tools for supporting knowledge and innovation processes i.e knowledge management and open innovation can be useful (Črešnar et al., 2018). These are not highly utilized at the moment (Črešnar et al., 2018), but Millenials would significantly benefit from their use because they have been, through their upbringing, constantly in contact with the same concepts that these tools represent (Weber, 2017). To solve organizational problems faster and more efficiently, the usage of key management tools should be beneficial and it will also reflect in the levels of employe productivity.

CONCLUSION In this paper, we find that in more are than one area, millennials’ personal values indeed indicate better employee productivity in the business environment created by Industry 4.0. Because the business environment is transitioning in a way that reflects millennials personal values, they will be more productive in areas of collaboration, teamwork, self-management, creativity, innovation, ethical responsibility, and other practical and professional areas. Some gaps remain that are connected to high self-enhancement values of power and achievement in millennials, that are not as much needed and emphasized looking through the lenses of in what way the business environment will transform. However, because millennials grew up with technologies that are currently being integrated into organizational workings, they may be able to better understand what it takes to be productive, how to use them and what their capabilities are. 1641

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They can also better take into account the changes in the broader philosophical orientations and balance their personal life and their work life so that they can be productive in both.

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Weber, J. (2017). Discovering the Millennials’ Personal Values Orientation: A Comparison to Two Managerial Populations. Journal of Business Ethics, 143(3), 517–529. doi:10.100710551-015-2803-1 Yazdi, P. G., Azizi, A., & Hashemipour, M. (2018). An Empirical Investigation of the Relationship between Overall Equipment Efficiency (OEE) and Manufacturing Sustainability in Industry 4.0 with Time Study Approach. Sustainability, 10(9), 3031. doi:10.3390u10093031 Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2017). Intelligent Manufacturing in the Context of Industry 4.0: A Review. Engineering, 3(5), 616–630. doi:10.1016/J.ENG.2017.05.015

This research was previously published in Recent Advances in the Roles of Cultural and Personal Values in Organizational Behavior; pages 261-275, copyright year 2020 by Business Science Reference (an imprint of IGI Global).

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Education in the Era of Industry 4.0:

Qualifications, Challenges, and Opportunities Dharmendra Trikamlal Patel https://orcid.org/0000-0002-4769-1289 Charotar University of Science and Technology, India

ABSTRACT Industry 4.0 has changed the thinking of industry owners in terms of technological usage. With the help of modern digital technology, industry can fulfill the requirements of customers easily and compete strongly against their competitors. In order to achieve good quality of products at an affordable price, industry needs skilled people who are aware of autonomous and intelligent components. To prepare skilled people compatible with Industry 4.0, education plays a very important role. The chapter starts with which kind of qualifications are needed to fit in the smart factory era. In next section, the chapter deals with challenges that emerge in education in order to implement skills suitable for Industry 4.0. Lastly, the chapter describes opportunities for the education sector as far as the smart factory is concerned.

THE WAY OF SMART FACTORY: AN EDUCATIONAL PERSPECTIVE In 1780, the first revolution of industrial manufacturing (Nic Von,1996) had started. No technology was used in that era and manufacturer heavily depended on laborers for any kind of productions. Laborers had to do the mechanical kind of work so not a specific kind of qualifications were expected from them. The second industrial revolution (Joel Mokyr,1998) had started in 1870 which is considered as the technical revolution as the manufacturer had started using numerous technologies. This revolution comprised of heavy usage of manufacturing machineries, communication via telegraph, electrification, use of petroleum and transportation by means of railroads. This revolution had changed the thinking DOI: 10.4018/978-1-7998-8548-1.ch082

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 Education in the Era of Industry 4.0

of the education sector and they started more emphasizing on atomization, telecommunications and electrification concepts. The era of 1970’s is considered as the third revolution of industry (Xiaowen, 2016). The ways of communication in the form of Internet and mobile devices have changed the entire thought process of industry personnel. Artificial Intelligence has replaced laborers with automatic entities like robots. The use of 3D printing is unbelievable. The industry has started by using renewable energy in their production. Agriculture sector became dominant as genetically modified crops, farming started. Due to nanotechnology, new materials became lighter and more durable. The third revolution is the most rapid revolution due to information and electronics technologies. The education sector has boosted up the speed of the third revolution by producing right level of skills in diversifying areas like information technology, electronics technology, nanotechnology and synthetic biology. The third revolution has changed choices of Indian students and educational institutions and universities as well. Cyber-physical systems (Antsaklis, 2014, Shi J, et al., 2011) and interoperability among machines have given the birth of the next revolution, i.e. Industry 4.0(Aehnelt et al., 2014, Bauernhansl et al.,2014, Brettel, et.al,2014, Kolberg et al.,2015, Weyer et al.,2015) or Smart Factory(Aehnelt et al.,2014). Smart factory revolution facilitates any organization to digitally manage the entire life cycle of the project starting from planning to the testing phase. Smart Factory(Groover,2007, Kane et al.,2015) emphasizes on two things: (a) Information Technology that is responsible for business process automation and (b) Operational Technology that is responsible for industrial process and factory automation. The Machine to Machine communications and Human Machines Interface permits machines with intelligent sensors to converse as human language to ERP system(Lazovic et al.,2014, Scheifele et al.,2014). Internet of Things (IoTs) based technology (Kovatsch et al., 2012) plays a crucial role in the integration of IT and OT. The main challenge for educational institutions is to produce skills that fulfill demand of IT and OT. The People having fusion knowledge of the domain and Information technology will survive in today’s era. The knowledge in the field of cyber-physical systems, hand held robotics, RFID (Priego et al.2014), NFC, Intelligent networking, etc. is expected from people to survive in this highly digital era. The following Figure 1 describes educational evolution in terms of the industrial revolution. From the figure it is determined that Industry 3.0 and 4.0 has completely changed the scenario of educational systems. A person having only domain knowledge will not survive in a smart factory era due to extensive use of digitization. The Cyber-Physical system has changed the direction of an educational organization. Educational Cyber Physical System is the need of the modern education. Educational Cyber Physical System needs many components to work it efficiently. 1. Collaborative Learning Tools: It provides the communication and interaction between teacher and learner. 2. Learning Management Tools: Administrative functionalities to manage learning processes and data. 3. Assessment Tools: It is needed to assess the learning progress of the students. 4. Educational Guidelines: It takes care of the student development from different angles. 5. Intelligence Tools: Covers some intelligent aspects for better teaching-learning. 6. Engagement Tools: It provides high level participation experience to the students. 7. Integration Tools: It provides integration of sound, video,3D animations, text to the system for better experience of teaching-learning. 1648

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Figure 1.

Expected Qualifications For Smart Factory Smart factory is a fusion of real world manufacturing with the extensive use of Information and Communication Technology (ICT). Conventional expertise of employee must be tailored to meet up the requirements of this fusion approach(Brauner, P et.al.2015) to improve the quality of the production. Following listed qualifications are expected other than domain knowledge from people to fit in the Industry 4.0 standard: • • • • • • • • • • • •

Industrial ICT Specialist: Fusion expertise of electronics and ICT based hardware and software. Intelligent Network Analyst: Expertise in distributed sensor/actuator networks. Robotics: Expertise in autonomous robots. 3D Printing: Expertise in planning of business and factory process. Cloud Computing: Expertise in store, manage and process the data from remote servers. Big Data Analytics: Expertise to uncover hidden, valid and useful patterns from large data sets. Augmented Reality Expert: Expertise to give support to workers by means of real like software. Information Security Expert: Expertise to solve security issues. ERP Expert: Expertise in facilitating integration of ERP system and generate desired output. Quality Assurance Expert: Expertise in assuring quality of product. Fog Computing Specialist: Expertise in computation among devices. RFID Expert: Expertise in tracking smart devices. 1649

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Interdisciplinary Education For Industry 4.0 Industry 4.0 builds from four main dimensions such as automation, cyber physical system, Internet of things and cloud computing paradigms (Schlechtend et al.,2014). Substantial outcomes could be achieved from this new paradigm of manufacturing system if an organization have a right mix of skills from above mentioned dimensions. A person is considered as an expert in a specific dimension if he has knowledge of all sub dimensions and educational aspects mentioned in Table 1. Table 1. Educational aspects of industry 4.0 Dimension

Automation

Cyber Physical System

Sub Dimension

Educational Aspects

Control System

• Methodology to improve the accessibility of the control system • Behavior of System • Sensors working knowledge • Controller knowledge • Feedback System • Error Signals • Tracking of devices • Stability Concepts • Linear and Non Linear Models • Transmission delay • Soft and Hard Automation • Computer Aided Automation • Robots • Adaptive Control • Tool Interpolation

Architecture

• Seamless Integration of Control, Communication and Computation • Rapid Design and Deployment • Modular Approach of designing • Standards

Computations

• Distributed Algorithms • Software tools and techniques • Computational Challenges

Network Control

• Time Delay • Failures • Wireless Networks Concepts • Robustness • Heterogeneous Cooperation • Reliability, Availability and Security issues

Verification

• Trustworthiness Issues • Interoperability

Connection

• Sensor Network • Plug and play mechanism • Security based communication

Conversation

• Analytics for Performance Prediction • Analytics for Data Correlation

Cyber

• Discrete Mathematics • Data Mining Techniques

Cognition

• Visualization Techniques • Simulation Techniques • Decision Making

continues on following page

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Table 1. Continued Dimension

Internet of Things

Cloud Computing

Sub Dimension

Educational Aspects

RFID

• RFID in comparison of Barcode • RFID Tags • Passive and Active Transponders • Middleware • RFID Enabled Applications • RFID Standards • Technical issues of RFID • Implementation of RFID

Sensors

• Working Principle • Wireless Sensor Network • Sensing Devices and types • Characteristics of Sensors • Digital Sensor Processing • Architecture and adoption of Smart Sensors • Challenges of Sensors

Actuators

• Working Principle • Types of Actuators • Characteristics of Actuators

Virtual Objects

• Structure of Virtual Objects • Interactions of Virtual Objects • Integration and Communications issues • Layers of Virtual Objects • Interpretation of Virtual Objects • Behavior of Virtual Objects

Analytics

• IoT ecosystem and role of analytics • Predictive Modeling • IoTs data flow • Machine Learning Techniques

Fog Computing

• Fog Computing Characteristics • Fog Computing Components • Fog Computing Software platform • Fog Computing Applications • Modeling and Simulations

CHALLENGES OF EDUCATION SECTOR FOR INDUSTRY 4.0 The success of Industry 4.0 is profoundly depending on the skill sets of employees of an organization. Employees can achieve right skill sets, compatible with Industry 4.0, through proper education. Industry 4.0 requires interdisciplinary skill sets and that creates plenty of challenges for the education sector.

Educational Challenges of Smart Robot Based Manufacturing Traditional Manufacturing was required only core engineering knowledge to absorb in any industry. Historical manufacturing role were: Machine Operator, Mechanist, Technician, Coating Workers, etc. The fourth revolution of industry has changed the entire manufacturing (Schuh et al.,2014) thought process and robots become an integral part of manufacturing. Involvement of robots in manufacturing has created many challenges for education sector to produce right skills of employment. The challenges are:

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To design the curriculum that comprises of concepts of smart robot based automation (Dawande et al.,2009): Robots are one of the key components in automation. If the curriculum of university education lacks of the concepts and methodologies of robotics, it is quite impossible to prepare the students for the future Industrial based education. Now a day’s robotics is used in every aspect in industry so inclusion of robotics in effective way is very vital in the curriculum of university education. The organization has to set up appropriate robotics labs to map the theoretical content with the practical aspects. To prepare teachers who can give real industry based training to students or employees of an organization: The knowledge is transforming from teacher to students in any education system. If teachers do not have appropriate knowledge that cannot be transformed to students so teacher training is very essential in education. Universities have to prepare the teacher in such a way that they can give effective knowledge to their students. Training identification in reputed organization and to adjust the timing of teachers is very challenging for any educational organization. To design simulation that includes proper skill sets of robot: As discussed in first topic, robotics is an essential in any industry and concepts of robotics can be used in designing of the training module. Industry based simulation needs appropriate skill sets of robotics. For any educational organization, to establish such simulation needs huge funding. To determine how robot ought to work and how changes in circumstances and environment will affect the final outcome: Testing of all the aspects of robot’s behavior is very essential to determine the final outcome of the process. The challenge is to understand all kinds of behavior of robots with different environmental situation. To determine causes of operating errors: When we deal with robots, identification of causes of operating errors is very crucial. Some proactive mechanisms should be implemented to determine the causes of operational errors. To determine which kind of tests are needed for effective quality control analysis: Prepare effective test cases to evaluate the quality of the process is very essential. To adopt active learning effectively: Using robots, students should be able to do analysis, amalgamation and an assessment of the curriculum content. To identify complex problem solving to develop desired solutions: Collaborative robots should be designed to solve complex problem of industry and it should be the part of the university curriculum. In university curriculum case studies or projects should be designed that meets the current requirements of an industry. To define appropriate learning strategies to select appropriate training methods and procedures: Training of robotics is very vital and for that teachers should decide their learning strategies. To use technical rules and routines to resolve any problem: Technical knowledge regarding robotics is very important to resolve any technical problem. To decide the appropriate programming language to write code for various purposes: Programming language selection is very vital to take the work from robots. Industrial robot languages are very important in this context. Few examples are: RAPID programming language, Kuka Robot Lanaguage,PDL2,INFORM, AS,Karel,VAL3, UR Scripts,ROS Industrial To install programs, machines, etc. to meet desired specifications: Prepare appropriate technical infrastructure is very important to achieve the desired specifications of the robot.

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• • • • •

To gain practical exposure of circuit boards, processors, chips, computer hardware and software: There are a variety of equipments are available to design a robot but selection of an appropriate one is very vital. To gain knowledge of mathematics such as algebra, geometry, calculus, statistics, etc.: Robot controlling is based on mathematical concepts so deep knowledge of mathematics is very important. To understand knowledge of physics, such as physical principles, laws, their interrelationship etc.: Physics knowledge is very essential in robot designing such as physical principles, laws, applications, material, mechanical structures etc. To be familiar with knowledge of telecommunications: Communications through robot is carried out using telecommunications principles so knowledge of it is very essential. To establish e-learning modules to learn from anywhere: Designing effective e-learning modules about robotics that comprises of basic and advanced knowledge of robotics.

Educational Challenges for Production Line Simulation Industry 4.0 emphasizes on digital manufacturing so production line simulation is extensively used by industry to simulate diverse facets of their production operations. Use of simulation in industry creates abundant challenges; here challenges in education perspective are listed. •

•

• • • • • •

To design education and training syllabi after clear understanding of industry needs: Syllabi should cover current requirements of industry. Before designing the syllabi certain trips to well known industry is required to identify their needs. It is also important to take the feedback of industry experts while designing curriculum. For the educational organization, to identify such experts and take their views is the most challenging task. To identify an effective simulator that gives real exposure of industrial processes: Practical insights of any process of an industry are very important and that can be available to students in form of simulator. Identification of an effective simulation that exhibits current needs of an industry is the most important challenge for the education community. To afford the cost of the simulator: If an effective simulator is identified, then another challenge is the cost of it. To prepare effective manuals of simulator: If the simulator is affordable then how to use it is the main challenge. To train the trainer about the simulator To determine the causes of operational errors: When we deal with the simulator, identification of causes of operating errors is very crucial. Some proactive mechanisms should be implemented to determine the causes of operational errors. To optimize code to achieve good performance: To achieve good performance of the simulator, the code should be optimized. Remove unnecessary code and apply logic with few lines is a very challenging task. To take effective decision based on the simulator’s result or errors: Sometimes simulator generates very inefficient results due to lack of data availability in that case decision based on a simulator is most challenging tasks.

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• •

To identify measures or indicators to measure system performance: Identification of important measures and indicators is most challenging tasks to generate efficient results of the simulator. To establish an e-learning module for quick and anytime-anywhere learning: Designing effective e-learning modules about production line simulation that comprises of the overall concepts of the modern requirements of an industry is most challenging tasks.

Educational Challenges for Big Data Driven Quality Control Industry 4.0 based digital technologies generates an enormous amount of data that is termed as Big Data. Analysis of Big Data (Lee et al.,2014) generates very efficient insights in manufacturing, but many challenges are associated with that. In this section, only challenges related to educational perspectives in terms of Big Data Quality Control will be discussed. •

•

• • • • •

•

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To select set of appropriate big data analytics tools that gives insights to all manufacturing organizations. Smart industry generates a gigantic amount of data from sensor connections, on demand computing and cyber-physical devices. Analysis of such data is a very challenging task and it needs appropriate analytical tool. There are various categories of analytics tools are available in the market so selection of an appropriate one is very challenging in context to university education. To train the trainer about set of big data analytics tools: Big Data Analytics needs knowledge of programming, quantitative skill, knowledge of spreadsheets, structured query languages, Linux, Hadoop, statistical packages etc. To train faculty members in all skill is quite an impossible task for education universities. To identify useful metrics and key performance indicators for better quality control: To achieve good results of analytics, it is important to identify quality measures and indicators. To establish technological architecture to handle exponential data growth: To establish an infrastructure that seems to real industry is a major challenge for any education university. It is very costly, so the question is about affordability of it for education institutions. To decide acceptance sampling for better judge the quality of product: For better analytics, adequate data are very important. The main challenge for educational institutions is to generate such amount of sample data that is needed for better and effective results. To gain knowledge about statistics to decide quality measures: The main base of analytics is the adequate knowledge of statistics. To achieve statistical knowledge in the context of application is a very difficult thing for faculty members and students of different streams. To awareness about new tools and techniques of big data in terms of quality control: Regular updating knowledge about the tools and techniques of big data is very essential to provide better results from available data. It is very challenging in perspective of teachers of different streams to gain such knowledge and apply new techniques on big data. To gain knowledge about quality standards and quality assessment methods for big data: Big Data is different than usual data in many perspectives such as volume, variety, velocity, veracity and value. The traditional methods of quality standards and assessment are not enough for big data and to identify an appropriate one is a very challenging task.

 Education in the Era of Industry 4.0

• • • •

To adopt suitable and efficient data integration techniques as diversity of data sources: Data is of a variety of big data. Integration of a variety of data from heterogeneous systems is most difficult exercises in context to big data. To judge data quality in a specified time frame: Quality of big data depends from multiple dimensionalities so judging it in a specific term is the most difficult task. To gain knowledge about predictive quality analytics: The main objective of analytics is to predict about situation of an industrial operation. Predictive algorithms are very difficult to understand and implement. Predictive analysis is effective on accurate data. To modularize material of big data driven quality control for easy and efficient access: Big data involve multiple interdisciplinary fields. In order to gain appropriate knowledge about big data, the entire coverage should be modularized among different modules. To divide the content of big data in different modules exhibits great challenge as one module needs the knowledge about other module.

Educational Challenges For Smart Supply Network Industry 4.0 leads to a smart supply network that improves order fulfillment, profit, transparency and return of investment. The Smart Supply network depends on a number of key technologies: autonomous and integrated planning, smart execution systems, autonomous logistics, smart procurement, intelligent warehousing and advanced analytics. The main objectives of such smart supply network are to design entire system that is both flexible and openness. The main challenges of such system in educational perspectives are: •

• • •

• •

To make available software and technologies related to smart network: Network connection is important element in context to smart factory as it needs for communication among smart devices. The network of smart factory is far different than usual computer network as communication is done among devices. The new way of software and technologies are needed to make efficient communication among devices and it creates a great challenge for education community to establish such smart network. To train human resources in a way that they adopt these technologies easily: To find appropriate trainer in such a new technology is a great hurdle for education community. To design and integrate multi-organizational service network for achieving real exposure: Smart network involves integration of operations from multiple organizations and to achieve such kind of infrastructure in education institutions create lots of managerial and technical hurdles. To collaborate with benchmark organizations to prepare right content for smart supply network: To identify and collaborate with benchmark organizations for smart network is very difficult task for education organization as benchmark industries do not have such time and sometimes they do not disclose confidential data and processes for public. To design proper cases of future network to teach employee in right paths: Without proper basic knowledge of smart network, it is very difficult to design cases of future network for any educational institution. To measure the effectiveness of processes: Appropriate data and indicators are needed for education organization to measure effectiveness of any smart network based process in premise of institution. 1655

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•

To apply proper tools and techniques for data analytics: There are number of data analytics tools are available in market so selection of an appropriate is very difficult task for education institution due to lack of knowledge about it.

Educational Challenges For Predictive Maintenance In manufacturing organization, maintenance is a critical and main challenge associated with it to ensure the maximum availability of machines with minimum cost. Industry 4.0 brings new concept of maintenance that is predictive maintenance (Lee et al.,2013)that covers all maintenance related process steps ranges from automated detection to final recovery of the machine. Education sector plays very important role in the implementation of this new way of maintenance, however, there are a number of challenges associated with that. • • • • • • •

• • •

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To understand an appropriate predictive maintenance tool before using it in training purpose: Predictive maintenance tools involves complex prediction based algorithms that are very difficult to understand for stakeholders for education organization. To provide the correct type of training encompasses of technology and managerial skills: To find appropriate trainer in this modern technology is very difficult for education organization. To ensure that employee using the tool correctly: In order to use tool correctly, efficient training is required for teachers. The tool should have appropriate manuals to operate and efficient and affordable trainer should be available in market. To use proper methodology for efficient predictive maintenance: There are many prediction algorithms are available and use any one in context to current situation is very difficult task for education To determine technique that deals with large amounts of data with heterogeneous properties: Use of Big Data Analytics for education community is very difficult task. To train the people regarding error awareness and derives methods to deal with them: Exhaustive training is required to solve error in smart network and there is a scarcity of knowledgeable and affordable trainers. To identify meaningful features to indicate upcoming failure: Prediction about future error depends upon the use of right predictive algorithm. To identify right predictive algorithm needs extensive knowledge about all predictive algorithms that creates huge hurdle for education institutions. To setup experimental environment that continuously monitor the machine and suggest corrective actions: To establish real industry exposure in educational institution creates great challenge in terms of affordability and technicality. To determine the evaluation techniques for optimum prediction: Optimum prediction needs exhaustive knowledge about smart network but it is quite difficult for education institutions to get it without qualified trainer. To design training modules effectively: Designing a training module of smart network needs basic and advanced knowledge of tools and methodologies. These kind of knowledge cannot achieved without proper trainer and to get trainer in this modern topic is quite impossible for education organization.

 Education in the Era of Industry 4.0

Educational Challenges in Implementation of Industry 4.0 Based Digital Technologies Industry 4.0 is all about digital technologies. The following figure 2 describes the major digital technologies that smart factory needs for effective implementation. •

•

Cloud Computing: According to RightScale2016 (RightScale,2016) state of the cloud report, the main challenge in implementation of cloud computing is the lack of expertise in that field. Universities or education sector is not capable to produce skilled people in this area. The figure 3 describes of main cloud based challenges in percentage. Lack of Expertise is highest among them with 32%. Other challenges such as security, compliance, managing multiple cloud services, governance, and performance exist in higher proportion due to lack of expertise. Internet of Things(IoTS): International Data Cooperation(IDC) did one survey of 2350 large and medium size industries worldwide about the Internet of Things. Near about 50% companies either deployed IoTs or plan to implement in one year. Most survey participants are interested in generating computation at the “edge” rather than at the data center. The main challenge with IoT is security. Security challenges are the main due to lack of expertise in mitigates risks of “edge” computing. Educational universities do not have the right skills to train students in this area. Another issue with the educational sector is that they have a scarcity of professionals who have cross-industry insights that is very essential in this booming technology of the Internet of Things (IoTs).

Figure 2.

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Figure 3.

•

•

•

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Location Detection Techniques: Accurate indoor localization is more vital in the era of Industry 4.0. Newly invented ubiquitous indoor positioning system is an ideal for smart factory as it works with heterogeneous devices. People do not have much knowledge about security, applications, spatial algorithms, performance issues etc. about this new technology. The challenge for educational universities is to gain knowledge about all above discussed topics to train people effectively. Human Machine Interface: Smart human machine interface based requirement is drastically increased due to Industry 4.0 based technologies. Smart HMI enhances the productivity of operators and able to control or maintain machine effectively. The Manufacturer wants to operate machines like smart phone. It must have intuitive visualizations (Chen,2004) options that can integrate with other components of industry. The main challenge for the education sector is to devise the requirements of the interface based on specific industry and generate standards that can be applied to any industry. Fraud Detection: In the digital revolution of industry, fraud detection is an important aspect to achieve high reliability and availability of any information system. According to ThreatMetrix study of 2015, fraudulent attempts reached 25 million per month. The main challenge for educational universities is to prepare efficient cyber security experts who can devise proper techniques for fraud detections. 3D Printing: With the advancement of commercial manufacturing, the role of 3D printing (Bernd Bickel et al.,2013) becomes vital. According to the report of the Consumer Technology Association (CTA) and United Parcel Service (UPS) 98% of hearing aids worldwide is manufactured using 3D

 Education in the Era of Industry 4.0

•

•

•

•

•

printing. The challenge for the education sector in this context is to prepare software professionals who can design the software for this advanced technology. Big Data Analytics: In the modern industrial scenario, huge data are generated by machines and devices. Analysis of this data effectively gives valuable insights to industry. The main challenge for education is to select appropriate analytics tool that fits in major domains. To gain knowledge about modern techniques of analytics with very less references also a main hurdle for the education sector. Mobile Computing: Mobile or ubiquitous devices are integral part of Industry 4.0 based manufacturing. The software designing of mobile applications is totally different than computer based traditional software. The role of educational institutions is to prepared software designers in the context of mobile devices. Another issue is the security of mobile based transactions. The challenge for the education sector is to gain knowledge about all aspects of threats and train employees in such a way that they are in a position to mitigate or monitor all kinds of probable risks. Augmented Reality: Augmented reality becomes an essential part of manufacturing due to the quick expansion in mobile devices, telecommunications technologies, data storage, and wireless data transfer. Augmented reality can be used for many purposes in industries, but training is the greatest purpose among all. Designing a training module for specific section using concepts of augmented reality is the main challenge for the industry. Educational sectors play a very important role in this matter, but the main hurdle for them to prepared, skilled people of this technology and to gain knowledge about this. A report from Juniper Research shows that the use of Augmented reality apps in the enterprise will grow to $2.4 billion in 2019, up from $247 million in 2014 means tenfold increase over five years. The main challenge for educational sector is how to create skilled manpower to cope up with this situation. Wearable Devices: Wearable devices can be used in manufacturing for several tasks such as employee and plant monitoring, problem diagnosis, improving employee safety, etc. Gartner research director predicts that wearable could help employees diagnose and revamp problems more quickly, saving up to $1 billion annually in three to five years. The main challenges of wearable technologies in manufacturing industries are design of device and data privacy. The success of any wearable device is how it is able to replace the traditional device. The biggest challenge of wearables is to ensure the privacy and security of client data. The challenge for educational sector is to understand a scenario of wearable devices, in context to manufacturing industries and design effective curriculum according to it so skilled peopled can be produced. Smart Sensors: Smart factory entrench smart sensors for various tasks such as monitoring of machine performance. Understand and implementation of intelligent, smart sensor standards is crucial for any educational institution to train industry personnel in the right direction. To establish a proper research environment and funding is also a crucial for the education sector.

EDUCATIONAL OPPORTUNITIES FOR INDUSTRY 4.0 Smart factory has created enormous opportunities for the education sector to provide an efficient environment for these new technological stuff. Educational sector plays an important role in the effective implementation of Industry 4.0 based technologies. Following are the main educational opportunities due to Industry 4.0 based technologies. 1659

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(1) An Industry coalition for curriculum designing: Educational sector has a huge opportunity in designing industry specific curriculum with the coalition with Industry people. Opportunities for educational sector in curriculum designing context are: (a) Industry based Educational Criteria: Curriculum designing can consist of action oriented, problem solving, and task and issued based curriculum criteria rather than traditional criteria. (b) Integration of more Industry based Internships: Curriculum can enhance by integrating more internship opportunities with coalition with industry people. (c) Learning outcome based Curriculum: Curriculum can emphasize learning outcomes based on workforce needs of an industry. Employability can be the main outcome of curriculum designing. (d) Fusion Courses: Curriculum can consist of fusion courses of domain area and emerging technologies related to Industry 4.0 based on the current needs of industry. (e) Project Oriented Teamwork: Curriculum can involve project oriented pedagogy so tacit knowledge of the person can be accumulated. The Project’s definition can be decided by consultation of industry experts. (2) Digital Chain of Manufacturing: The opportunity for educational organization is to design entire educational program in digital form rather than the classical way. Educational institutes can strengthen the way of teaching-learning by simulations, virtual figures of machine and online e-contents. (3) Set up Labs with Intelligent Devices: It is a great opportunity for educational institutions to set up a laboratory with intelligent devices and attract students to work with those devices. Educational Institutions can tie up with industry for setting up laboratories and get exposure of technical knowledge of industry experts. (4) Develop right skills for Industry 4.0: Educational institutions have immense opportunities to develop ready-to-absorb people by setting up the right environment for industry 4.0. It consists of right curriculum, trained faculty members, effective learning environment and suitable infrastructure for industry 4.0. (5) Establish Research cell for Industry 4.0: Educational institutions can establish research cell for technologies which involved in Industry 4.0. This is a vital opportunity for educational sector to look differ than other institutions by establishing research initiatives in this modern area. (6) Increased Job Opportunities: Educational sector can increase job opportunities for students by effective implementation of smart factory based technologies in the curriculum. (7) Solution to Industrial Problems: Educational universities can solve problems of Industry by preparing students and faculty members successfully for Industry 4.0 based technologies. The education sector can give right practical exposure to students and faculty members to solve any critical problem of industry.

FUTURE RESEARCH DIRECTIONS OF INDUSTRY 4.0 IN CONTEXT TO EDUCATION Industry 4.0 merges the virtual world with real productions. It covers several areas such as information technology, logistics, electronics and mechanical. There is a huge scope of the research in this area, particularly in context for education.

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There is a huge scope of the research in designing an automatic tool that exhibits, digital chain of manufacturing; based on Industry 4.0. This research involves implementation of Industry 4.0 concepts in educational simulation. The research challenge is to provide an efficient virtual world of production that feels like a real world in simulation software. It is one kind of collaborative research where knowledge of information technology, electronics and mechanical engineering are essential. Designing a virtual reality based training module apps for students and teachers exhibits good research scope. The research involves the optimization of handheld devices resources without compromising the look and feel of training module. The challenge is to implement concepts of virtual reality in devices with very limited resources. Designing an analytical module that covers problems of industry, based on appropriate visualization techniques have enormous research scope. This kind of module is very helpful for students to understand industry problems. The module requires a gigantic amount of data that are very difficult to analyze. Deciding appropriate visualization technique in context of an industry problem creates a huge challenge. The research directions in Industry 4.0 in context to education are to be designed automatic software tools that give exposure about Industry 4.0 to all stakeholders. Designing software is required in any kind of devices such as personal computer, mobile, wearable devices, etc. and extensively use the methodologies of augmented reality. The research direction of Industry 4.0 in the context of education is very limited and constrained to only designing of automated tools. However, there is huge research scope in Industry 4.0 based technologies. Following are several glimpses of it: •

• • • • • • • • •

Automation of industrial machines and plants using distributed embedded systems. This kind of research work needs expertise of information technology, electronics, robotics and ERP. A special focus is required in designing of the system that deals with transparency and handles the complexity of entire system workflow. Supporting industrial operations by designing and evaluation of Human-Machine-Interfaces. This research extensively involves visualization techniques and augmented reality concepts. Innovative usage scenarios inside the industrial plan. This kind of research uses big data analytics along with visualization techniques. This research enhances diagnosis during plant operation. Automated methods and tools for predictive maintenance. This kind of research involves analytics and visualization techniques. The system is capable of predicting maintenance schedule automatically based on data received from the sensors. Demand oriented adaption of man power. This kind of research involves analytics based on demand-supply philosophy of production. Modeling for an integrated and comprehensive security in devices. This research emphasizes on security aspects of cyber physical devices. It involves vulnerabilities and industrial attacks. Quality assurance Analytics. For industry perspective, quality is the main concern. This research involves analytics from multiple dimensions. Cloud based manufacturing control. This research involves the cloud computing concept with big data. Sensor based software tools for quality assessment. This research deals with sensor data for analysis of quality assessment in context to product or process. Human robot collaboration. The research heavily uses of electronics concepts along with big data analytics.

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• •

Mobile assisted systems for Industry operations. This involves mobile computing with visualization techniques. Manufacturing Data Analytics. This involves predicting operations of manufacturing using data analytics.

CONCLUSION Industry 4.0 revolution has changed the thinking of the industry as well as educational sector. The success of Industry 4.0 is based on right skilled people. It is the responsibility of the educational sector to prepare human resources in such a way that they can easily absorb in the modern manufacturing paradigm of the industry. For preparing right skills among people, educational sector must know which kinds of skills are vital for them. This chapter had started with educational evolution based on the industrial revolution, so education sector can aware about their objectives of right skills. The second part of chapter dealt with the expected qualifications for the smart factory so educational sector can incorporate the right mix of educational aspects in the teaching-learning process. The chapter also described interdisciplinary educational aspects of students’ fraternity. The chapter discussed technological challenges for the educational sector in the implementation of smart factory based curriculum. The chapter briefly described the educational opportunities for industry 4.0. At last chapter gave certain research direction of Industry 4.0 in context for education. Research direction of Industry 4.0 in the context of education is very limited and it is constrained to only designing an automated tool to create a virtual world of real production that is benefitted in the teaching-learning process.

REFERENCES Aehnelt, M., & Bader, S. (2014). Tracking assembly processes and providing assistance in smart factories. Proceedings of the 6th International Conference on Agents and Artificial Intelligence (ICAART). Antsaklis, P. (2014). Goals and Challenges in Cyber-Physical Systems Research Editorial of the Editor in Chief. IEEE Transactions on Automatic Control, 59(12), 3117–3119. doi:10.1109/TAC.2014.2363897 Bauernhansl, T., ten Hompel, M., & Vogel-Heuser, B. (2014). Industrie 4.0 in Produktion, Automatisierung undLogistik — Anwendung, Technologien, Migration. Wiesbaden: Springer. doi:10.1007/9783-658-04682-8 Bickel, V. B., & Alexa, M. (2013). Computational Aspects of Fabrication: Modeling, Design, and 3D Printing. IEEE Computer Graphics and Applications, 33(6), 24–25. doi:10.1109/MCG.2013.89 PMID:24921096 Brauner, P., & Ziefle, M. (2015). Human Factors in Production Systems. Springer International Publishing. doi:10.1007/978-3-319-12304-2_14 Brettel, M., Friederichsen, N., Keller, M., & Rosenberg, M. (2014). How virtualization, decentralization and network building change the manufacturing landscape: An Industry 4.0 Perspective, International Journal of Science. Engineering and Technology, 8(1), 37–44.

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Chen, C. (2004). Searching for intellectual turning points: Progressive knowledge domain visualization. Proceedings of the National Academy of Sciences of the USA, 101(1), 5303-53. Dawande, M., Pinedo, M., & Sriskandarajah, C. (2009). Multiple Part-Type Production in Robotic Cells: Equivalence of Two Real-World Models. Manufacturing & Service Operations Management: M & SOM, 11(2), 210–228. doi:10.1287/msom.1070.0208 Groover, M. P. (2007). Automation, Production Systems, and Computer-Integrated Manufacturing. Prentice Hall Press. Kane, G. C., Palmer, D., Phillips, A. N., & Kiron, D. (2015). Is your business ready for a digital future? MIT Sloan Management Review, 56(4), 37–44. Kolberg, D., & Zühlke, D. (2015). Lean automation enabled by industry 4.0 technologies. IFAC-PapersOnLine, 48(3), 1870–1875. doi:10.1016/j.ifacol.2015.06.359 Kovatsch, M., Mayer, S., & Ostermaier, B. (2012). Moving application logic from the firmware to the cloud: Towards the thin server architecture for the internet of things. Proc. Innovative Mobile and Internet Services and Ubiquitous Computing (IMIS), 2012 Sixth International Conference, IEEE, 751-756. Lazovic, V., Montenegro, J., & Durickovic, T. (2014). The digital economy in developing Countries -challenges and opportunities. 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 580-1585. 10.1109/MIPRO.2014.6859817 Lee, J., Kao, H.-A., & Yang, S. (2014). Service innovation and smart analytics for industry 4.0 and big data environment. Procedia CIRP, 16, 3–8. doi:10.1016/j.procir.2014.02.001 Lee, J., Lapira, E., Yang, S., & Kao, H. A. (2013). Predictive manufacturing system trends of next generation production systems. Proceedings of the 11th IFAC workshop on intelligent manufacturing systems, 150–6. 10.3182/20130522-3-BR-4036.00107 Mokyr. (1998). The Second Industrial Revolution, 1870-1914, The Lever of Riches. Academic Press. Posada, J., Toro, C., Barandiaran, I., Oyarzun, D., Stricker, D., De Amicis, R., ... Vallarino, I. (2015). Visual computing as a key enabling technology for industrie 4.0 and industrial internet. Computer Graphics and Applications, IEEE, 35(2), 26–40. doi:10.1109/MCG.2015.45 PMID:25807506 Priego, R., Orive, D., & Marcos, M. (2014). Maintaining the availability of the Control System in Industrial Automation. In Agenten im Umfeld von Industrie 4.0 (pp. 15-19). Sierke Verlag. RightScale. (2016). State of the cloud report, Hybrid Cloud Adoption Ramps as cloud users and cloud providers mature. Retrieved from www.rightscale.com/lp/2016-state-of-the-cloud-report Scheifele, S., Friedrich, J., Lechler, A., & Verl, A. (2014). Flexible, Self -configuring Control System for a Modular Production System. 2nd International Conference on System -Integrated Intelligence, 15, 398–405. Schlechtendahl, J., Kretschmer, F., Lechler, A., & Verl, A. (2014). Communication Mechanisms for Cloud based Machine Controls. CIRP Conference on Manufacturing Systems, 17, 830–834

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Schuh, G., Potente, T., Wesch-Potente, C., Weber, A.R., & Prote, J.-P. (2014). Collaboration Mechanism s to Increase Productivity in the Context of Industrie 4.0. CIRP Robust Manufacturing Conference, 19, 51–56. Shi, J., Wan, J., Yan, H., & Suo, H. (2011), A survey of cyber-physical systems. International conference on wireless communications and signal processing (WCP), 1–6. von Tunzelmann. (1996). Engineering and Innovation in the Industrial Revolutions. STEEP Discussion Paper No 30, Science Policy Research Unit. Weyer, S., Schmitt, M., Ohmer, M., & Gorecky, D. (2015). Towards Industry 4.0-Standardization as the crucial challenge for highly modular, multi-vendor production systems. IFAC-PapersOnLine, 48(3), 579–584. doi:10.1016/j.ifacol.2015.06.143 Zeng. (2016). Study on the Third Industrial Revolution and Paradigm Transformation of China’s Manufacturing Industry—Based on Theoretical Analysis of Scale Economy and Scope Economy. American Journal of Industrial and Business Management, 73-82.

KEY TERMS AND DEFINITIONS 3D Printing: It is a process used to produce a three-dimensional object of manufacturing industry to view it in a better manner. Actuator: It is a component of a device that is used to control or move the device. Adaptive Control: It is a methodology of designing a control system that deals with uncertainties. Augmented Reality: It is a technology that creates an artificial environment of the real situation by integrating digital information. Big Data: Data set that is extremely large in size and mostly unstructured in nature. Big Data Analytics: It is a process to analyze big data to uncover hidden, valid, and useful patterns and insights. Cloud Computing: Technology that delivers computing services over the Internet. It includes computing services like storage, networking, databases, servers, etc. Cyber-Physical System: It is a system that is supervised by computer-based algorithms and heavily integrated with the Internet. Data Mining: It is a process of analyzing large data to discover hidden and useful patterns. ERP System: It is software that integrates all aspects of manufacturing operations such as production planning, designing, manufacturing, marketing, sales, etc. Fog Computing: It is a decentralize computing paradigm that performs on the edge of the device. Industry4.0/Smart Factory: This is the fourth revolution of manufacturing industries. This revolution emphasizes on cyber-physical systems, internet of things, cloud computing, big data analytics, and 3D printing. Internet of Things (IoTs): It is a networking of physical devices embedded with internet, electronics, sensors, software, actuators that enable devices to store and send the data. Location Detection Technique: It’s a technique that locates objects or people inside the industry.

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Machine Learning Techniques: This technique provides an ability to computer to learn without being explicit human intervention. Predictive Modeling: It is a process that forecasts certain events in manufacturing using data mining and probability. RFID: It is a technology that automatically tracks tags attached to any object. Second Industrial Revolution: It considers as a technological revolution of manufacturing industries. In this revolution, industries have started using existing technologies such as telegraph, rail network, gas, water supply, and sewage system. Sensor: It is an electronic component that is used to detect the behavior of an object in the environment. Simulation: It is software that imitates the operation of a real-world manufacturing process. Smart Supply Network: It is a methodology in which things work together. It collects the data in real time and does automatically alteration in the flow of products as demand changes. Third Industrial Revolution: It considers as the digital revolution of manufacturing industries. Industries have started using computers for their many processes. Velocity: It deals with the pace at which data flows from heterogeneous sources. Veracity: It means abnormalities in available data. Wearable Device: This is the technology that puts on a human body in form of a gadget. This technology is helpful in industry for plant monitoring.

This research was previously published in Methodologies and Outcomes of Engineering and Technological Pedagogy; pages 88-111, copyright year 2020 by Engineering Science Reference (an imprint of IGI Global).

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The Future of Product Design Education Industry 4.0 Jennifer Loy https://orcid.org/0000-0001-7153-0699 University of Technology Sydney, Australia James I. Novak University of Technology Sydney, Australia

ABSTRACT When a society is undergoing transformational change, it is a challenge for all involved to step outside their immediate context sufficiently to evaluate its implications. In the current digital revolution driving Industry 4.0, the pace of change is rapid, and its scale and complexity can inhibit a proactive, rather than reactive, response. Yet if it were possible to return to the first industrial revolution, armed with twentyfirst century knowledge and historical perspective, planning for a healthy society and the future of work could have been very different. This chapter aims to support educational leadership in the development of proactive strategies to respond to the challenges and opportunities of Industry 4.0 to inform the future of work, industry, and society. This is framed through the lens of product design, with its unique position at the nexus of engineering and the humanities, and directly tied to changes affecting manufacturing in the fourth industrial revolution.

INTRODUCTION From disruptive technology to disruptive ideas, the early decades of the twenty-first century can be characterized as a period of non-conformity and new direction. Whilst globalization and amalgamation dominated economic strategy at the turn of the century, the predicted homogenization into a world without borders has been fractured by a backlash of nationalism and separatism, burgeoning entrepreneurship and new business practices based on a sharing economy. In this setting, the expectation could be that product design education would be working through a corresponding period of radical change, yet in many universities, with higher numbers and reduced funding, there are increasing pressures to conform to a DOI: 10.4018/978-1-7998-8548-1.ch083

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 The Future of Product Design Education Industry 4.0

modularized education system; design has become a linear process, and limited approaches to creativity are prescribed with diluted outcomes defined by business school drivers and design thinking approaches to interdisciplinary practice. This chapter questions the dominant influences in prevailing product design education, challenging existing thinking in the disciplines to call for educational reform in the face of outdated conventions and thinking. If the discipline is to remain relevant in this era of digital disruption and rapid technological advancements, then pedagogy must be subject to critical scrutiny in order to ensure that the pathways aligned with this approach are not restricted by existing practices and closed thinking. This chapter argues for academics to learn from the past and present in designing pedagogy and curriculum, informing the need for change to ensure authentic learning for product designers in the twenty-first century.

BACKGROUND Product design emerged as a profession during the eighteenth century in response to the drive towards mass production and the design challenges it created. According to design historian Adrian Forty, in his seminal book, Objects of Desire (1992), the earliest product designers in the UK employed by innovators, such as Josiah Wedgewood, were educated on European trends in art and architecture, as well as the reduction of a design into repeatable components. Production uniformity and aesthetic conformity became megatrends of the time and were central to design development, allowing manufacturers to produce standardized products within an orderly system of centralized manufacturing. The impact on the organization of labor, and subsequently lifestyle and the urbanization of the population, was relatively gradual. During the late nineteenth and early twentieth century, however, as Ford established the moving assembly line, the pace of urbanization changed with large-scale factories drawing in workers from large distances (Sparke, 2013). For designers, their role became increasingly constrained by mass-manufacturing processes and practices, with these being the driving technologies of the times. Design had to conform to assembly rules, and generally the lower the cost of components the higher the margins for business. For workers, the transition to working within a system where labor was divided to its most basic action became common place. The impact on the organization of society was immense. The transition can be characterized as a shift from traditional hand production to massive industrial machinery and factory production. To a large extent it was stimulated by the invention of large-scale manufacturing processes fuelled by the discovery of new methodologies for exploiting the energy stored in huge iron and coal deposits. The subsequent access to apparently unlimited energy and human resources engendered by the rise of capitalism and individual and corporate entrepreneurship and innovation, marked a major transition in human affairs. The Industrial Revolution was the socioeconomic equivalent of the Big Bang. (West, 2017, p.211) Following the Second World War, building the economy was seen as paramount, with marketing and mass production scaled up. This drive meant that designers during the twentieth century were frequently locked into the contradictory practice of trying to design the best possible outcomes yet encourage obsolescence for repeat sales. Dissenters, such as Victor Papanek, argued for a sense of moral responsibility in design. He was shunned during his early career following the publication of his seminal work, Design for the Real World in 1971 (revised edition 2005), but as the social and environmental impacts 1667

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of mass production began to be understood towards the end of the century, attitudes to his work within the design community began to alter. By the nineteen-nineties, Papanek’s book was required reading in design schools as environmentally-conscious, socially aware design gained momentum. The straightforward design – or redesign – of commercial objects for mainstream retailers was replaced in design schools by the responsible design of products for specific challenges facing society, with the lifecycle analysis of the product integral to the work. A century after the role was introduced as a taught profession (attributed to the Deutscher Werkbund established in 1907, precursor to the Bauhaus (Droste, 2015)), the environmental and social impact of the work of the profession and the manufacturing and marketing parameters that it had been established under, were, to a large extent, discredited (Hawken, 2010). Sustainable design became central to the teaching of product design in universities with lifecycle assessment, extended producer responsibility and designing for the circular economy core to industrial design education for the twenty-first century (Walker, 2006). Whilst the terms ‘product design’ and ‘industrial design’ are often used interchangeably, the latter is arguably more systems oriented than the first, but both are closely linked to the emergence of mass production and consumer culture in the early 20th century (Fiell & Fiell, 2006; Walker, 2006). Over the last forty years, the perception of the role of the product designer in higher education has changed significantly (Loy, 2012) as the social and environmental impacts of design for production have become better understood. As a discipline synonymous with large-scale manufacturing and distribution, product design has shaped the physical world through the creation of objects constrained by economic manufacturing drivers, such as standardization and repeatability. Based on this, Walker (2006) argues that the very terms product design and industrial design therefore carry with them an associated baggage that may no longer be suitable in the twenty-first century as new technologies disrupt traditional modes of design and production. This is part of broader digital transformation impacting manufacturing today, termed Industry 4.0. Much like the earlier Industrial Revolution’s embrace of the latest technologies of the times, Industry 4.0 encompasses today’s latest technologies including artificial intelligence (AI), machine learning, the Internet of Things (IoT), and ubiquitous computing, and will radically alter the world of work, education and consumer relationships with products. Designers no longer focus on purely human-centered design and the functioning of the product and its stylistic appeal, but rather on the product service system it operates within. The agency of the product is recognized as impacting behavior and sustainability, and is as core to the design as its economic viability. As Tatum discussed in his Design Issues (2004) article on design responsibility, every design decision, however small, has consequences because of the cumulative effects of incremental change. From an environmental perspective, the next twenty to fifty years will be critical, and design education needs to anticipate the challenges coming. Founder and Executive Chairman of the World Economic Forum, Schwab (2017) explains that the fourth industrial revolution: began at the turn of this century and builds on the digital revolution. It is characterized by a much more ubiquitous and mobile internet, by smaller and more powerful sensors that have become cheaper, and by artificial intelligence and machine learning… By enabling “smart factories,” the fourth industrial revolution creates a world in which virtual and physical systems of manufacturing globally cooperate with each other in a flexible way. This enables the absolute customization of products and the creation of new operating models. (2017, p. 7)

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Product design must necessarily evolve from its roots in the first industrial revolution to maintain relevancy in this new era of digital technology. With boundaries between the physical and digital worlds blurring through digital fabrication technologies, such as additive manufacturing, the demand for product designers to standardize manufacturing and develop one-size-fits-all products is being removed. Additive manufacturing (AM), also known as 3D printing (3DP), is comparatively new; it refers to a range of technologies fed directly by three-dimensional computer-aided design (CAD) files. Described by Gibson, Rosen and Stucker (2015, p. 2), the object is “fabricated directly without the need for process planning... the key to how additive manufacturing works is that parts are made by adding material in layers; each layer is a thin cross-section of the part derived from the original CAD data.” This contrasts traditional plastics manufacturing technologies, such as injection molding, which requires significant investment in machining molds before a single product can be produced, with manufacturers needing to sell high volumes of parts from each mold in order to recoup their initial investment and make a profit. The significance of additive manufacturing is that the creation of molds and tooling, and therefore the need to design and sell standardized products, is removed. This has led to a realization of mass-customization, also referred to as mass-personalization, (Hu, 2013; Tseng, Jiao, & Wang, 2010) whereby the 3D CAD file describing a product is altered to suit the unique functional or aesthetic needs of an individual. Objects of similar dimension yet different detail cost approximately the same to 3D print. This is resulting in the 3D printing of high-value customized products such as hearing aids, medical implants and orthotics. In the twenty-first century, a confluence of innovation based on digital technologies is creating a paradigm shift in industrial practice. Whilst AM is just one example of a technology contributing to the fourth industrial revolution, it is the catalyst for a shift in thinking from mass production to printon-demand and representative of the changes facing designers. In Industry 4.0, factory automation is increased, along with the monitoring of different facets of production. This includes machines, the progress of the part and the quality of the part at each stage of manufacture. The supply of materials and additional components could be integrated into an automated extension of the ‘just in time’ (JIT) approach. However, when the practice extends beyond the factory and the collection of data, the analysis of data, and automated responses to changes in the system, the approach moves beyond incremental manufacturing efficiencies. Where distribution and product lifecycle are monitored as part of product service systems, this data is fed back to the organization, providing insights on product performance, adaptation and interaction as part of a connected workflow. This approach is enabled by the drop in cost of monitoring systems, increased sophistication of algorithms and communication within systems (termed the Internet of Things), due to developments in ‘Cloud’ storage (Seldon & Abidoye, 2018, p. 137) and wireless connectivity. On a practical level, this shift requires serious up-skilling of academics and upgrading of facilities in higher education to prepare students for their new role within a cyberphysical manufacturing system. However, more fundamentally, academia needs to lead an understanding of the implications of transformational change in this sector to develop strategies and policy for the future of work and industry. The changes currently occurring have the potential to impact societies, the environment and economies. Product design programs and research in higher education are central to these changes and therefore academics need to be providing leadership in addressing the long-term implications of decisions made now, informed by history and a reasoned mapping of the changes about to come.

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EDUCATIONAL FRAMEWORK Learning From History With the benefit of experience gained by looking back at the long-term consequences of earlier industrial revolutions, both positive and negative, the recognition of a coming paradigm shift should theoretically allow societies to be better prepared for the widespread change that accompanies an industrial revolution. The impact of technological change on the organization of work and the subsequent impact on societies should be central to discussion on the implementation of technological interventions to production systems, such as automation and machine learning, characteristic of Industry 4.0. However, in his book Are Robots Taking Our Jobs (2017), Cameron suggests that when considering the changes brought about by digital transformation, people - and therefore organizations - have an inability to comprehend the scale, scope and speed of change. This is because its impact and complexity will be on a different scale because of global connectivity, and it is therefore challenging to view in its entirety. It is also difficult to view different aspects through a narrow lens to try to build a larger perspective, because of the lack of conventional organizational borders and hierarchy in thinking it engenders, and its challenge to existing heuristics. New ways of thinking about the world will be required to provide education with accessible strategies to assess the implications of the paradigm shift, and to attempt to mitigate problems it could create, as well as frame and address problems it could solve. Higher education has a responsibility to prepare future citizens for their role in society and as the future workforce. With Industry 4.0, this will involve training to ensure the workforce are equipped to cope with the new technology, but more fundamentally, it should involve developing values and aspirations for society informed by the opportunities and challenges digital industrialization will bring. It will also involve attempting to anticipate the changes to people’s lives on a daily basis, such as the need to change infrastructure and ways of working. In general, as the concept of Industry 4.0 emerged from the German car industry, it is seen as increased automation and maximized throughput. However, the term more broadly refers to an increased ubiquity of computing capability across manufacturing and product service systems. Increased monitoring systems, generating data, analyzed by algorithms as the basis for operations within the factory changes the way manufacturing operates. However, when monitoring is expanded out to the distribution of the product and its interactions during its lifetime, this informs the factory on future demand and working as part of a system to feedback information on its operation and performance, and changes the way people and products interact. In disciplines based on the design of manufactured products and product service systems, such as product design, a simple analogy would be moving from 2D paper-based drawings to 3D computer models, as has happened over the last thirty years. For designers, and design educators, it has required a different way of understanding and manipulating form. Expertise in traditional 2D working drawings was no guarantee of expertise in computer 3D modeling – it could even be a hindrance as it predetermines the expectations of the designer. In a similar way, Industry 4.0 heralds a shift from 3D to 4D product modeling, where the entire life of a product is considered during design, with the product possibly having the capacity to update or adapt through time as needs and circumstances change (Novak & Loy, 2017). This is because the digital technologies that inform developments in Industry 4.0 allow for connected products to also be responsive, and therefore develop into complex systems. For industry and academia to respond, thinking needs to be disruptive and informed by new knowledge. Furthermore, Schwab describes that:

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While the profound uncertainty surrounding the development and adoption of emerging technologies means that we do not yet know how the transformations driven by this industrial revolution will unfold, their complexity and interconnectedness across sectors imply that all stakeholders of global society governments, business, academia, and civil society - have a responsibility to work together to better understand the emerging trends. (2017, p. 2) The challenge to educational strategists is to step outside traditional discipline boundaries, as suggested by Schwab, and build objective, new perspectives that respond to its transformative potential. Design education is by definition forward looking and requires strategies for facing the unknown, engaging in initial research, and creating new, objective views of the situation being faced that allow realistic problem-framing and solution development. Product design academics should therefore be ideally placed to envisage new perspectives for Industry 4.0 education to respond not only to the technological developments in isolation, but also their broader societal, environmental and economic implications. In learning from the past, Walker (2006, p. 9) talks about previous design movements that have “challenged prevailing stereotypes and stimulated and influenced subsequent designers.” Examples include the De Stijl group in The Netherlands, Bauhaus and the Italian Memphis designers. However, he also points out that: the issues and agendas to which they were responding are not our issues and agendas. Today, we are facing new challenges associated with the globalization of industrial capitalism, the environment, national and transnational socio-economic disparities, and rapidly evolving scientific and technological developments. (Walker, 2006, p. 9) Educators today need to learn from the achievements of the past, particularly in creating social change, but whilst the ideas need to be viewed in context, equally, they cannot be embraced by the current generation without critical review. This is demonstrated by Fuad-Luke (2009), where he considers movements in design history through the lens of sustainability in his book Design Activism: Beautiful Strangeness for a Sustainable World. This approach provides a critical examination of the past, in order to inform current thinking. The necessity of a revised approach to higher education responding directly to the potential future of work enabled by Industry 4.0 needs to be recognized. Product design provides the basis for this discussion because of its position at the nexus of engineering and the humanities, but the challenge to build a radically different educational response to the paradigm shift this industrial revolution effects extends across all disciplines – in whatever future form they emerge as traditional academic boundaries merge. There is a need for strategies to address educational change to keep pace with developments in digital technology but, more fundamentally, for higher education to prepare future citizens to recognize and respond to the issue of human development in a digital era.

Learning From the Present The digital revolution at the turn of the century has transformed communication and interaction across the globe. Consequently, digital technology is changing learning and teaching for all disciplines. For industrial and product design, the digital revolution is exactly that – a revolution in ideas, aspirations, technological opportunity and direction. It challenges conventions in product design, manufacturing, development, the designer-producer-user relationship, and has the potential to allow for major restructuring 1671

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in manufacturing systems around the world. This is particularly driven by the sustainability imperative, illustrated by the discovery of plastic waste in remote regions, including from arctic ice-core samples. The highest density of plastic ever to have been documented was on an uninhabited coral atoll called Henderson Island in the South Pacific. Scientists studying the island estimated there were over 38 million pieces of plastic, with items from Germany, Canada and other distant places recorded. (McCallum, 2018, p.27) Rethinking the future based on an in-depth, researched understanding of complex situations, and research into the opportunities and challenges provided by new technologies and their relationship to existing ones, whilst keeping the human experience, social and environmental responsibilities to the fore is surely fundamental to the product design discipline. But is this actually the case? Taking a critical view, is it the reality that in working to become an established academic field of research and education over the last thirty years in a competitive tertiary education environment, the discipline has been compromised? Is creativity education being reduced to predictable, manageable, linear exercises due to practical reasons or for accountability? Has a drive to employ academics with a decent H-index pushed industrial and product design educators out or under the auspices of an engineer? In a study into the relationship between research and teaching activities in tertiary institutions, Gunn (2018) found that the need to meet the requirements for producing research outputs and bring in research funding was distorting the priorities of the universities. Gunn (2018, p.153) argued that it was “an anomaly” in terms of a business practice, as the dominance of research focus directed resources away from serving the “customers” in the “core business.” He explains how the environment in which UK universities have operated over the last twenty-five years has been transformed and there is a need to refocus on teaching to meet the requirements of a new educational era. The extent of the disruption of digital technologies to society and the future of work and industry has only begun to emerge. However, based on examples such as the online communication platforms supporting Uber and Airbnb, disruption is likely to have far reaching consequences. Academics will need to rethink the curriculum and pedagogy in response to the complex nature of the changes and prepare students for the unknown and the unexpected. However, has the very ability to respond positively and imaginatively to the unknown (a critical need for society in the face of disruptive change and surely characteristic of design), been rendered insignificant and even obsolete over that time because it is hard for institutions to quantify - or for those from other disciplines to properly replicate? Industrial and product design disciplines will need to step outside the confines of dominant, established academic practices and attitudes and take an objective look at the role of the designer in the twenty-first century, and an unfettered look at educational development that supports the discipline ideal irrespective of external influences and current ideas of what tertiary education and research should be. This is a time of change, and change is what design should be all about. Over the last twenty years, elements of a design approach to problem framing and problem solving have been hived off by business, health, engineering and transdisciplinary programs. There have been two major problems with this: Firstly, those teaching within these programs are frequently not fullyfledged designers and therefore not adequately equipped to teach the subject effectively. Imagine if designers started to teach engineering without qualifications or professional experience; there would be considerable objections raised. Yet it appears that it is acceptable within tertiary education for this practice, which speaks to the current understanding of what design is, and the status of design programs in universities based on the current priorities of those tertiary institutions. Secondly, the elements of the 1672

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design process that have been adopted by other programs are limited, resulting in a narrow interpretation of design and its application. In particular the systemization of a design process, as frequently presented in engineering programs and publications, is reminiscent of presenting a health care plan that does not consider the human element. The complications and subtleties of the situation are lost. Design is reduced to incremental and predictable actions. Many design philosophers, such as Hugh-Aldersey Williams (2011), have drawn parallels between life after the digital revolution and life before the industrial revolution. The seminal work by economic philosopher Adam Smith, An Inquiry into the Nature and Causes of the Wealth of Nations, proposed that efficiency would be improved by the division of labor into the smallest possible increments within manufacturing (Forty, 1992). This approach has dominated mass production over the last hundred years, supported by a stock market driven economy where individual, short term gain has taken precedence over a holistic view of the human experience, contraction and convergence equity issues and long-term environmental impacts. Yet, as West (2017, p.211) points out, this approach is unsustainable: “As presently constituted, our continued success requires the supply of coal, gas, oil, fresh water, iron, copper, molybdenum, titanium, ruthenium, platinum, phosphorous, nitrogen, and much, much more to be available at an exponentially increasing rate.” Digital disruptions include algorithms for stock market trading, the displacement of conventional banking with blockchain, and the highlighting of the inadequacies of existing regulatory systems to address changes in business practice, healthcare and manufacturing. These changes are happening quickly. As Seldon and Abidoye (2018, p.169) point out, academia’s understanding of the changes in educational access with developments in AI can be outdated and its reaction slow: “Google is the greatest pedagogical success and it is a piece of AI.” In universities, even the introduction of programs can take a number of years, irrespective of public demand, because of the protective systems in place. When a program is added, it often must be justified by evidence of similar, successful programs elsewhere. It also needs to fit within the established program offerings and the organization of faculties, schools and departments. These processes make agile changes difficult. The reduction of risk in the introduction of programs also reduces the ability of a university to change direction quickly or to offer diversity in the programs that operate across traditional organizational boundaries. Given the rapid pace of technological change, this inability to adapt at a similar pace is cause for concern. To date, research universities have been secure in their position as educational providers. Yet complacency has a history of preventing sufficient preparation in the face of unanticipated or under estimated change. This is a dangerous attitude for all disciplines to have, including, as discussed, long-established and regulated disciplines such as engineering, but for design, there is really no excuse. Digital technologies have the potential to cause far more disruption than appears to be currently being predicted within educational institutions. “A growing national and institutional policy focus on teaching, and anxiety over the student experience, presents the chance for new narratives of investment in learning and teaching to develop beyond the hegemonic reach of research excellence and research-intensive universities” (Charles, 2018, p.28). Genuine, informed, engaged design education is not hampered by the weight of formulaic restrictions and discipline legacy. It has not only the freedom, but the imperative, to keep a watching brief for change and respond energetically to researching the possible futures on the horizon. However, in order to maintain integrity in this aim, industrial and product design disciplines need to disentangle themselves from the reflected status of disciplines that are currently the longest established, and most recognized as authoritarian in universities at this time. Design education needs to be founded on working with the unknown, and the constant development of new practices and approaches to explore, understand and address forever new problems and opportunities. Students and 1673

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academics need to re-embrace that spirit of improvisation, and tertiary education needs to understand its value. Ways of working founded on this approach need to be revisited and formally developed, with an alignment perhaps closer to acting studies than current design education programs that are aligned to business or engineering.

Learning for the Future According to Seldon and Abidoye (2018, p.169) “AI is impacting everyone, every day, but schools and universities are not consciously using it to exploit teaching and learning or adequately to prepare their learners for the AI-driven workforce they will subsequently encounter.” They continue: Nothing but nothing is more important than education in ensuring that AI works in the interests of all humanity. We need to re-imagine our schools from the ground up to teach our young to be more fully human and not to be content any longer with giving them just ‘factory era’ skills.” (Seldon & Abidoye, 2018, p.312) Seldon and Abidoye point out that in the UK, the Prime Minister’s Industrial Strategy, announced in November 2017, has AI as the leading component, and start-ups in London have grown from 16 in 2008 to 6000 in 2018. There have been fairly radical shifts for product design education over the last forty years – a relatively short time compared to other, more traditional disciplines, such as engineering and architecture. Yet the changes facing all of higher education in the rapidly emerging digital context are likely to disrupt product design education even more significantly because of their intrinsic connection to manufacturing and therefore AI. Seldon and Abidoye argue: There is no more important issue facing education, or humanity at large, than the fast approaching revolution of Artificial Intelligence, or AI. This book is a call to educators everywhere, in primary, secondary, further and higher education (HE), and in all countries, to open our eyes to what is coming towards us. If we do so, then the future will be shaped by us in the interests of all. If not, others, the large tech companies, governments and even the bad guys will decide, and we will have only ourselves to blame. (2018, p.1) The suggestion is that educational models will change and that governments must lead changes to modernize education. (Seldon & Abidoye, 2018) Artificial Intelligence in this context does not refer solely – or even predominantly – to humanoid robots, but to the automation of labor and its impact on the future of work and industry. For higher education, the challenges are both to prepare graduates for a digitally connected future, but also to radically evolve education, to create relevant pedagogy and curriculum in a digital age. The context and intent of a curriculum for the coming digital era needs to be informed by the dichotomy that is emerging in response to the rapid changes digital technologies are bringing. This dichotomy is exemplified by the contrasting opinions of Greenfield (2017) and Kelly (2016) on technology and society issues such as the ‘Quantified Self’ (Kelly, 2016, p.238, Greenfield, 2017, p.33). It is also illustrated for the general public in exhibitions such as ‘The Future Starts Here’ at the Victoria and Albert museum in London (Hyde & Pestana, 2018). Instead of a science-fiction, all white, technology-worshipping ideology, the exhibition presented a curiously dystopian view of developments in a vaguely post-apocalyptic setting. In a darkened room, with exhibition stands that look like the 1674

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relics of a Bladerunner 2049 film set, cutting-edge research, such as the MIT 4D pneumatics prototypes, were shown alongside examples of digital-based interference in politics (both Trump and Brexit) and fake news. An example of 3D printing being used by designer Sarah Hendren and anthropologist Caitrin Lynch to support the individual needs of an amputee in the design of low-cost, bespoke assistive technology (Galloway, 2018) was positioned alongside a stand devoted to those who are working to extend life through strategies such as cryogenics and downloading the human brain. For product design education, Industry 4.0-specific education is critical for future graduates. As products increasingly become connected to the digital world, and are created through entirely digital means, the questions for product designers have become bigger, shifting from how digital technologies such as sensors, the Internet of Things and additive manufacturing will affect products, to how it will affect product systems, industries, their organization and the future workforce. Education with regards to Industry 4.0 will need to comprise of two things: The first is the means to critically research and interrogate the opportunities and implications of technological change. The second is the ability to constantly update skills and understandings fluidly in a continuous state of change. Both are difficult to achieve in the face of the educational heritage that has built up over the last fifty years as education itself became more of a business, with changes to funding and the focus of universities generally leading to larger class sizes and more standardized learning. Traditionally a designer has gone through an education system where the content generally changes only incrementally and has worked in a career where the tools of designing, the processes and manufacturing technologies, change slowly, if at all. Whilst an occasional updating of skills might have been necessary – lifelong learning has not. However, design is currently in flux, linked closely with the burgeoning developments in digital technology: “Technology is humanity’s accelerant. Because of technology everything we make is always in the process of becoming. Every kind of thing is becoming something else, while it churns from “might” to “is.” All is flux. Nothing is finished. Nothing is done” (Kelly, 2016, p. 6). The education of designers must also now be in flux – subjects taught the same way as they were only a few years ago would arguably be setting students up for failure, as the knowledge they would graduate with would be outdated before they began their working career. Brown and Alder (2008) built on this idea describing that: In the twentieth century, the dominant approach to education focused on helping students to build stocks of knowledge and cognitive skills that could be deployed later in appropriate situations. This approach to education worked well in a relatively stable, slowly changing world in which careers typically lasted a lifetime. But the twenty-first century is quite different. The world is evolving at an increasing pace. When jobs change, as they are likely to do, we can no longer expect to send someone back to school to be retrained. By the time that happens, the domain of inquiry is likely to have morphed yet again. (Brown & Adler, 2008, p. 30) Therefore, lifelong learning must be embedded in design – it must be synonymous with the act of designing. Gore (2013) makes a similar point, arguing there is still too much emphasis in education on the memorization of facts: “Yet in a world where all facts are constantly at our fingertips, we can afford to spend more time teaching the skills necessary to not only learn facts but also learn the connections among them” (Gore, 2013, p. 67). The challenge is less about reshaping education for the students, as each new influx of students knows little about what has been taught in previous years, but rather how to transition design staff to embrace change, particularly if they have past industry experience in product design based in traditional mas-manufacturing technologies. Many academics (not just design) have 1675

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been experts in their field, but as change brought on by digital technologies such as AI accelerates, many established practitioners and academics simply do not want to adapt and remain relevant. At what point does the need to prepare students for the future outweigh this experience of the past? Petko, Egger, Cantieni and Wespi (2015, p.49) observe that “research has shown that successful technology adoption does not so much rely on hardware and software but, more importantly, on teachers’ skills and beliefs.” Leadership in academia tends to be older because of the traditional hierarchical promotion system, and industry professors are still rare, though not unheard of (for example, journalist Professor Peter Fray at the University of Technology Sydney in 2018). Whilst age is not a barrier to the adoption of new technology, it is arguably more difficult to adapt to new ways of working when expertise is built on traditional working practice. Large scale operations, such as universities, are difficult to redirect. Examples of disruptive change brought about by management decisions can be seen at the University of Melbourne though, for example, where first year studies were integrated, and trimesters introduced. Brown and Alder (2008) introduced “Learning 2.0,” linked to the Open Educational Resources (OER) movement: In a traditional Cartesian educational system, students may spend years learning about a subject; only after amassing sufficient (explicit) knowledge are they expected to start acquiring the (tacit) knowledge or practice of how to be an active practitioner/professional in a field. But viewing learning as the process of joining a community of practice reverses this pattern and allows new students to engage in “learning to be” even as they are mastering the content of a field. (Brown & Adler, 2008, p. 20) This prepares students for lifelong learning. In many ways this idea is more closely linked with an apprenticeship for design whereby learning is practice-led and conducted in the process of designing – thus learning and designing become intertwined (Novak, 2018). However, the rapid pace of change and the radically different ways of working that are emerging with Industry 4.0 require more proactive strategies than relying on current practitioners to lead the generation. Arguably it will be the other way around, as young people elect entrepreneurial practice over conventional employment, influenced by the success of companies such as Google, which started in a garage in 1998 and has 4.2 billion search requests daily in 2018, and YouTube, which started in a room above a pizzeria in 2005 and today has 8.8 billion videos online (Seldon & Abidoye, 2018). According to Seldon and Abidoye (2018, p. 137) “the IoT facilitates the collection of big data on a scale we are still unable fully to assess or exploit, as the sheer volume can militate against sifting the quality from the unreliable evidence and forming solid conclusions,” and China is working to establish itself as the world’s primary AI innovation center by 2030 (p. 314). Relying on conventional innovation practices within established industries responding to the challenges and opportunities of Industry 4.0 seems a dangerous strategy for the rest of the world. However, the rise in interest in entrepreneurship (for example 40% of students at the University of Technology Sydney expressed a preference for being an entrepreneur over being conventionally employed (Conroy, 2016)), and the opportunities to foster it that digital connectivity provides, may provide new direction. In order to maximize this trend, the university system needs to become nimble in its offerings, dynamic, connected and practice-led. This can only be achieved with a framework of practice to support academics in moving to this new way of thinking. Responding to these changes, a proposed model of education that blends elements of the flipped classroom model (Altemueller & Lindquist, 2017; Gavriel, 2015) with a ‘Build-Measure-Learn’ lean startup approach may offer an appropriate strategy whereby students take responsibility for their own learning, guided by a facilitator. This is diagrammatically shown in Figure 1. The traditional roles of 1676

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teacher and learner are reversed, with students bringing new knowledge to classes, which is disseminated to fellow students and facilitator alike. This is significant in a time where technology is evolving so rapidly that it is impossible for a single lecturer or facilitator to know everything. Combined with a lean startup approach, students are encouraged through projects to consider their products as part of larger business models, analyzing investment strategies, testing prototypes on the market, and even developing Kickstarter campaigns (for example a course called Innovation and Entrepreneurship run by the authors at Griffith University). This approach is agile and sets students up with lifelong learning skills as they participate in the practice of learning alongside peers and mentors. Figure 1. Proposed framework of learning based on the flipped classroom and lean startup ‘buildmeasure-learn’ models (Novak 2018)

The industrial design discipline by definition is about imagining, planning and realizing the future. Even when engaged with rationalizing the details of a specific manufacturing requirement for an individual company, the designer is part of a discipline focused on innovation, understanding and effecting progress. As designers will constantly face new challenges, new technologies and changing aspirations for society that impact design decisions, design education necessarily needs to prepare graduates to be lifelong learners and proactive, critical thinkers. Lynam and Cachia (2018) argue that in an increasingly digital, interconnected world, information is readily accessible and becomes rapidly outdated, and therefore future employers will value graduates who are critical thinkers rather than just knowledgeable. They highlight the ability to source and evaluate credible information as necessary for effective problem-solving and decision-making, and these skills are an important part of the framework described by Figure 1 as “out of class” activities. Sterling (2015) also argues that in preparation for real-world, complex problem solving, students need to be educated not about change, but rather for change and this requires a pedagogical shift from transmissive education to transformative education. In the early decades of the twenty-first century, digital technologies are disrupting traditional business and industrial practices. Online communication platforms are fostering more realistic entrepreneurial

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ambitions than were previously possible, ubiquitous computing and the Internet of Things are forcing a complete rethink of product service systems, and additive manufacturing and the digitalization of inventory are enabling global distributed manufacturing. Whereas the development of industrial and product design practice over the last century could be characterized as incremental, building a body of knowledge on production practices and materials that informed design education, recent developments are creating a paradigm shift in the place of design in society and the role of the designer that contrasts with accepted practice. As digital disruption increasingly challenges established hierarchies and conventions, educational conformity may itself be under threat and traditional disciplines may benefit from a rethink on the role of product design education. Only, however, if product design education itself recognizes the emerging opportunities for change and breaks away from the constraints it has accepted will it gain acceptance in the current academic value system.

AUTHENTIC LEARNING IN THE TWENTY-FIRST CENTURY Schools and universities in 2018 are little changed from 1600, for all the ubiquitous (non-AI) technologies. The teacher or lecturer address rows of students from the front of the class, who advances at the same pace. Learning is not personalized and the teacher is weighed down by the burden of preparing material, setting and marking assignments, and reporting on student performance. (Seldon & Abidoye, 2018 p. 140) As university funding has changed, so have university student numbers increased. The organization of university education, its monitoring and regulation, have become driven by administrative realities that are created to work across large organizations. There are advantages in this approach, for example the ability for students to work more easily across modular programs to differentiate themselves through specializations, and the ability for students to retake single subjects or study part time in order to support themselves financially or negotiate other external commitments. However, the overall result is the modularization of the learning experience. Academics have to break learning into distinct events. The student is expected to put the experience together in their own minds to become well rounded graduates. It is clear from the rise in importance of graduate attributes from a university perspective that there are concerns already with this approach, and considering the large body of educational research arguing for authentic learning in recent years (e.g. Dee Fink (2013), Weimer (2013)), it is important to question whether organizational constraints are in fact distilling the educational experience. Before modularization, design was predominantly taught as a series of projects. If research shows that this approach is more effective for design teaching in the twenty-first century, will universities support the change? If the modularization of learning for administrative reasons was set to one side, and an objective, fresh view of design education in the current context, post digital revolution, with the growing sustainability imperative and the need for contraction and convergence and extended producer responsibility, what would it look like? Is the future of product design compromised by the conventions of past learning at a time when a paradigm shift in human patterns of consumption is needed and new thinking is required? As Gore discussed in his book The Future (2013, p. xv)): “There is a clear consensus that the future now emerging will be extremely different from anything we have ever known in the past. It is a difference not of degree but of kind.”

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Design academics need to engage more intensively with research into learning to build a new body of knowledge specifically on design teaching for the twenty-first century. It may be that teaching intensively in short bursts over the year best prepares students for the stresses and pressures involved in their role as designers in the future, or it may be that providing no factual knowledge to students during their degree is a strategy, where they have to gain first-hand knowledge of factors, such as materials and processes, at every level. Students need to prepare for individualized learning programs. Schools and universities have to catch up with their students quickly. Equally, they must prepare for life-long learning, which will spread rapidly. Credit transfer will become the norm rather than the exception. Students should all be taught computer, digital and AI literacy, and how to understand the difference between the human and the machine. The Welsh government is ahead of other UK nations on this. (Seldon & Abidoye, 2018. p. 313) Research into design studio assessments needs to challenge the underlying assumptions that tertiary educational accountability builds in. It may be that assessment should never be based on the individual, that a more genuine engagement in teaching group work and differentiating roles is essential for effective operation as a designer in the future. This may include the use of online hubs to bring together geographically separated individuals to work collaboratively, rather than relying on the traditional physical classroom, with Massive Open Online Courses (MOOC’s) and collaborative websites like GitHub, providing new ways for individuals to learn and engage in real-world projects. Design is a real-world activity, and assessments will have more meaning for the students if they are current and real-world relevant. According to research by Lynam and Cachia: Student-focused assessments were preferred by students and encouraged engagement. Learners appreciated assessments that built on their skill set; involved an element of choice and creativity; and were associated with a balanced workload. Students particularly valued the benefit of having assessments that were relevant to their career ambitions and developed their skill set. (Lynam & Cachia, 2018, p.231) The disruptions that technological developments are creating in the world need to be acknowledged in university industrial and product design education. The disciplines need to throw off the academic cultural cringe that has resulted from coming late to an established game, with rules that did not take them into account, and competing on a playing field that is far from level. This is not for the sake of the disciplines, but for future generations who need the benefit of design education that is not watered down but has genuine integrity that responds to the context it is constructed within. This century is crucial for the future of mankind and the health of the environment. Designers are needed, and design academics must prepare. There is a dominant link in education between the idea of interdisciplinarity in education and industry 4.0. Seldon and Abidoye (2018) argue for problem solving, open ended problems, collaboration and the reduction or delaying of specialization. In design, project-based learning is common-practice in higher education, and the concern Seldon and Abidoye express over the dominance of ‘right’ answers in education does not realistically apply in design as there are rarely ‘right’ answers. Projects are already commonly marked based on process as much as, or instead of, end products. Interestingly, interdisciplinarity is more contentious. Whilst it is frequently argued that projects break disciplinary boundaries and require an interdisciplinary team, it is more difficult to teach in this way across disciplines beyond merely bringing 1679

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students from different domains together. Transdisciplinary programs, such as the ones run at the University of Technology Sydney, appear to work well for service design problems. However, if the focus is on Industry 4.0 and preparing for the future role of product designers to work within the subsequent systems, there are some differences. The reality is that, just as earlier manufacturing processes had constraints and opportunities, so too do the processes within the systems of Industry 4.0. There are difficult technical challenges in designing for these production processes, for example for additive manufacturing; this is a challenging technology to design for and if it is not taught properly it will become solely the domain of engineers using topological optimization software, or growth software using algorithms capable of ‘designing’ products automatically without the need for designers at all. Further changes are affecting the communication of concepts, with renderings taken from CAD programs replacing concept drawings, and virtual reality experiences emerging to replace these renderings. Product designers need to change their thinking to looking at the industrial system, the product service system but also now the full life of the product which itself can change over time through embedded electronics or 4D materials. The idea of a static product has been debunked because of the agency attached to it. Culturally products have impact, and the future of work and industry is impacted by design. As Rosling, Rosling and Rosling Ronnlund (2018) point out, the current worldview is outdated and many people are working on incorrect facts on important issues, such as crime statistics, healthcare costs and employment statistics: “Would you be happy if your doctor was using cutting-edge research from 1965 to suggest your diagnosis and treatment?” (Rosling, Rosling, & Ronnlund, 2018, p. 26). According to Cameron (2017) and Bregman (2017), the worldview has to change in response to the digital revolution. Industry 4.0, encompassing AI and machine learning, will change not only the organization of labor but also its nature: “It’s time for a new labor movement. One that fights not only for more jobs and higher wages, but more importantly for work that has higher intrinsic value” (Bregman, 2017, p.261). Academics need to recognize this possibility and address it within higher education in all disciplines, and product design is no exception.

RECOMMENDATIONS Raising the next generation of designers requires academics to question current educational models, practice and content. The impact of Industry 4.0 on their educational experience should be radical, not incremental, yet this is difficult for academics immersed in current systems and ontology. In preparing students for a future in design, academics need to focus not solely on skills, but on preparation for lifelong learning, and, more essentially, for understanding their role in human development in a digital era. An educational framework for the emerging discipline is suggested, based on key points summarized here: This chapter addresses issues relating to disruptive change for higher education signified by Industry 4.0, using the product design discipline as an example, and outlines the paradigm shift needed for education to remain relevant. It highlights the importance of education that integrates the humanities with engineering for the future of society and draws on a historical perspective of the impact of societal and environmental change brought about by earlier industrial initiatives. Through this approach, the chapter calls for a greater emphasis on human development in a digital era and environmental, social and economic responsibilities embedded in the implementation of Industry

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Table 1. Proposed framework of learning based on the flipped classroom and lean startup ‘buildmeasure-learn’ models Learning From History

Learning From the Present

Learning for the Future

Reduction in poverty through industrialization

Widening educational access and changing educational models

Lifelong learning and agility in educational offerings

Social planning problems due to centralized manufacturing

Fragility of policy and political decisions

Distributed manufacturing

Impacts of pollution and the failure of current systems

Extended producer responsibility

Circular economy

Waste of natural resources

Product service systems

Entrepreneurship

CONCLUSION Over the last twenty years, the proportion of the global population living in extreme poverty has halved. This is absolutely revolutionary. I consider it to be the most important change that has happened in the world in my lifetime. It is also a pretty basic fact to know about life on earth. But people do not know it. On average only 7 percent – less than one in ten! - get it right. (Rosling et al., 2018 p.6) Product designers need to be non-conformists. They need to be able to consider the world and its systems with an informed objectivity, and not be held back by outdated ideas or knowledge that has been superseded. Based on the impact of mass production on the environment, and, in particular, the throw-away society of the twentieth century, it is interesting to consider how the benefit of hindsight could have altered production and disposal practices that characterized the era. Education on the cultural changes involved could have contributed to the development of different values and systems whilst change was in its infancy, and it was possible to influence its direction. Product designers need to be intrinsically motivated to keep updating their understanding of the world and their impact on it. It is the role of product design educators to foster that motivation by constantly questioning educational practice and engaging in pedagogical research. The future of work and industry will be very different in the way it operates to how it did last century. “Time is money. Economic growth can yield either more leisure or more consumption. From 1850 until 1980, we got both, but since then, it is mostly consumption that has increased” (Bregman, 2017 p. 139). The digital revolution is changing interaction, identity, community and economics. Universities as an institution are under threat with competition from open access education and alternative educational providers after the 2010 restrictions on for-profit private universities entering the sector were relaxed. Coming late to the academic community, industrial and product design educators have strived for acceptance and credibility within established hierarchies and systems. However, from disruptive technology to disruptive ideas, the early decades of the twenty-first century are creating a period of paradigm change and design education should not align itself with entrenched, complacent ill-prepared academia. An educational rebellion is needed for design learning to remain relevant, and to shoulder its responsibilities not only in preparing the designers of the future, but also in leading a rethink of education that is not based on the assumption that accepted practice is unassailable and should not be challenged. As

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West observes, “regardless of the diversity of our actions, material desires, and well-bring, we all want to have meaningful and fulfilling lives” (West, 2017, p. 211). Pedagogy must be subject to constant critical, unbiased scrutiny and product design academics must never forget they are designers at heart.

REFERENCES Aldersey-Williams, H. (2011). The new tin ear: Manufacturing, materials and the rise of the user-maker. London, UK: RSA Projects. Altemueller, L., & Lindquist, C. (2017). Flipped classroom instruction for inclusive learning. British Journal of Special Education, 44(3), 341–358. doi:10.1111/1467-8578.12177 Bregman, R. (2017). Utopia for realists and how we can get there. London, UK: Bloomsbury. Brown, J., & Adler, R. (2008). Minds on fire: Open education, the long tail, and learning 2.0. EDUCAUSE Review, 43(1), 16–32. Cameron, N. (2017). Will robots take your job? Cambridge, UK: Polity. Charles, M. (2018). Teaching, in spite of excellence: Recovering a practice of teaching-led research. Studies in Philosophy and Education, 37(1), 15–29. doi:10.100711217-017-9568-1 Conroy, G. (2016). Kick-starting student start-ups. Retrieved from http://sciencemeetsbusiness.com.au/ kick-starting-student-startups/ Dee Fink, L. (2013). Creating significant learning experiences: An integrated approach to designing college courses, revised and updated. San Francisco, CA: Jossey-Bass. Droste, M. (2015). Bauhaus. Cologne, Germany: Taschen. Fiell, C., & Fiell, P. (2006). Industrial design A-Z. Cologne, Germany: Taschen. Forty, A. (1992). Objects of desire: Design and society since 1750. London, UK: Thames & Hudson. Fuad-Luke, A. (2009). Design activism: Beautiful strangeness for a sustainable world. London, UK: Routledge. Galloway, A. (2018). Engineering at home. In The future starts here. London, UK: V&A Publishing. Gavriel, J. (2015). The flipped classroom. Education for Primary Care, 26(6), 424–425. doi:10.1080/1 4739879.2015.1109809 PMID:26808941 Gibson, I., Rosen, D., & Stucker, B. (2014). Additive manufacturing technologies: Rapid prototyping to direct digital manufacturing (2nd ed.). New York: Springer. Gore, A. (2013). The future. New York: Random House. Greenfield, A. (2017). Radical technologies: The design of everyday life. London, UK: Verso.

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Gunn, A. (2018). Metrics and methodologies for measuring teaching quality in higher education: Developing the Teaching Excellence Framework (TEF). Educational Review, 70(2), 129–148. doi:10.108 0/00131911.2017.1410106 Hawken, P. (2010). The ecology of commerce revised edition: A declaration of sustainability. New York: Harper Business. Hu, S. J. (2013). Evolving Paradigms of Manufacturing: From Mass Production to Mass Customization and Personalization. Procedia CIRP, 7, 3–8. doi:10.1016/j.procir.2013.05.002 Hyde, R., & Pestana, M. (Eds.). (2018). The Future Starts Here. London, UK: V&A Publishing. Kelly, K. (2016). The inevitable: Understanding the 12 technological forces that will shape our future. New York: Penguin Books. Loy, J. (2012). Creating confidence in an alienating educational environment. In Proceedings International conference on Engineering and Design education. Artesis University College. Lynam, S., & Cachia, M. (2018). Students’ perceptions of the role of assessments at higher education. Assessment & Evaluation in Higher Education, 43(2), 223–234. doi:10.1080/02602938.2017.1329928 McCallum, W. (2018). How to give up plastic: A guide to changing the world, one plastic bottle at a time. London, UK: Penguin Life. Novak, J. (2018). Self-directed learning in the age of open source, open hardware and 3D printing. In E. Ossiannilsson (Ed.), Ubiquitous inclusive learning in a digital era (pp. 154–178). Hershey, PA: IGI Global. Novak, J., & Loy, J. (2017). Digital technologies and 4D customized design: Challenging conventions with responsive design. In V. C. Bryan, A. T. Musgrove, & J. R. Powers (Eds.), Handbook of research on human development in the digital age (pp. 403–426). Hershey, PA: IGI Global. Papanek, V. (2005). Design for the real world: Human ecology and social change (revised ed.). Chicago: Chicago Review Press. Petko, D., Egger, N., Cantieni, A., & Wesoi, B. (2015). Digital media adoption in schools: Bottom-up, top-down, complementary or optional? Computers & Education, 84(May), 46–61. Rosling, H., Rosling, O., & Rosling Ronnlend, A. (2018). Factfulness: Ten reasons why we’re wrong about the world and why things are better than you think. London, UK: Sceptre. Schwab, K. (2017). The fourth industrial Revolution. New York: Crown Business. Seldon, A., & Abidoye, O. (2018). The Fourth education revolution: Will artificial intelligence liberate or infantilise humanity. Buckingham, UK: University of Buckingham Press. Sparke, P. (2013). An introduction to design and culture: 1900 to the present (3rd ed.). London, UK: Routledge. doi:10.4324/9780203129999 Sterling, S. (2015). Sustainable Education: Revisioning learning and change (Schumacher Briefings). Cambridge, UK: Green Books UIT.

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Tatum, J. (2004). The challenge of responsible design. Design Issues, 20(3), 66-80. Tseng, M. M., Jiao, R. J., & Wang, C. (2010). Design for mass personalization. CIRP Annals - Manufacturing Technology, 59(1), 175-178. doi:10.1016/j.cirp.2010.03.097 Walker, S. (2006). Sustainable by design: Explorations in theory and practice. London, UK: Routledge. Weimer, M. (2013). Learner-centered teaching: Five key changes to practice (2nd ed.). San Francisco, CA: Jossey Bass. West, G. (2017). Scale: The universal laws of life and death in organisms, cities and companies. London, UK: Weidenfeld & Nicholson.

ADDITIONAL READING Beetham, H., & Sharpe, R. (2013). Rethinking pedagogy for a digital age: Designing for 21st century learning (2nd ed.). New York, New York: Taylor and Francis. Fleischmann, K. (2015). The democratisation of design and design learning: How do we educate the next-generation designer. The International Journal of the Arts in Society, 8(6), 101–108. Global Trends 2030: Alternative Worlds. (2012). Retrieved from http://www.dni.gov/index.php/about/ organization/national-intelligence-council-global-trends Greenfield, A. (2006). Everyware: The dawning age of ubiquitous computing. Berkeley, California: New Riders. Novak, J. (2018). Re-educating the educators: Collaborative 3D printing education. In I. M. Santos, N. Ali, & S. Areepattamannil (Eds.), Interdisciplinary and international perspectives on 3D printing in education. Hershey, PA, USA: IGI Global. Petrova, M. (2014, 4-5/9/2014). Educating Designers from Generation Y – Challenges and Alternatives. Paper presented at the DS 78: Proceedings of the 16th International conference on Engineering and Product Design Education (E&PDE14), Design Education and Human Technology Relations, University of Twente, The Netherlands.

KEY TERMS AND DEFINITIONS 3D Printing (Additive Manufacturing): A digital fabrication technology that allows the production of an object by adding material layer-by-layer in three dimensions. Computer-Aided Design (CAD): The use of computer systems to assist in the creation, modification, analysis or optimization of a design in 2D or 3D.

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Flipped Classroom: This is a teaching methodology that encourages students to access lecture material outside of class, devoting class time to hands-on problem solving and the application of knowledge. The teacher’s role shifts to that of a facilitator, and collaborative learning and problem-based learning are important features of the flipped classroom. Industry 4.0: Also known as the “fourth industrial revolution,” this describes the current trend for increased automation in manufacturing, communication and machine-to-machine and human-to-machine relationships more broadly. Product and Industrial Design: Disciplines tightly linked to mass production and the design of goods to be manufactured for consumption.

This research was previously published in Redesigning Higher Education Initiatives for Industry 4.0; pages 164-182, copyright year 2019 by Information Science Reference (an imprint of IGI Global).

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The Development of the Management Competences at the Postgraduate Level in the Context of the Fourth Industrial Revolution Edgar Oliver Cardoso Espinosa https://orcid.org/0000-0001-7588-9439 Instituto Politécnico Nacional, Mexico

ABSTRACT The objective of the chapter is to describe the main managerial competences to be formed at the graduate level, according to the characteristics established in the context of the fourth industrial revolution. From this perspective, individual knowledge, experience, initiative, and creativity are recognized as the unlimited resource of organizations and countries, so that the talent of the people is the basis of the competitiveness and survival of the organizations of any type that require a manager. Five axes of training at the graduate level are identified: personal competences, strategic competences, intra-personal competencies, personal efficacy competencies, and investigative competences.

INTRODUCTION The Fourth Industrial Revolution is characterized by the establishment of a social, cultural and economic transformation from the generalization of the use of artificial intelligence and its application in robotics, which impacts the industrialization processes and the economy, society and politics, which implies a change in human and organizational relationships (Velásquez, 2019). In the World Economic Forum (WEF, 2017), several disruptive technological changes that are transforming social relationships between humans and objects were identified. Thus, there has been the widespread use of the Internet, the absolute use of mobile devices both at a personal and organizational DOI: 10.4018/978-1-7998-8548-1.ch084

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 The Development of the Management Competences at the Postgraduate Level in the Context of the 4IR

level, the population census through big data, the collection of taxes through blockchain, as well as incorporating artificial intelligence into the board meetings. This context has been named the Fourth Industrial Revolution, which is characterized by the use of new exponential technologies such as advanced robotics, autonomous transport, artificial intelligence, data collection sensors, the Internet of objects, 3D printing manufacturing, nanotechnology or quantum computing (Coleman, 2017). Given the previous challenges of the Fourth Industrial Revolution, Frey and Osborne (2017) established that the countries’ education system faces the following challenges: In the first place, the incorporation of the technological impact in the formative path of the professionals that are required for this new context, therefore the incorporation of new curricular designs and relevant training programs that respond to these new economic, social and cultural needs is required. Secondly, it is necessary to analyze the impact of this transformation on the academic management of organizations in order to formulate proposals that allow innovating both teaching and learning methodologies as well as the professional performance of graduates. And thirdly, to promote the generation of new knowledge in a framework of social innovation in order to exercise a prospective vision of this transformation based on both positive and negative results that are presented. In this way, Higher Education Institutions (HEIs) face the need to generate and implement educational programs that impact on the social and economic development of the countries, and which in turn meet the criteria of relevance, innovation and quality. As Marcovitch (2002) mentions: Faced with the technological revolution, HEIs behave like any other organization that cannot ignore and stop taking advantage of its benefits. Thus, HEIs continue to play a fundamental role in the training of professionals, high-level specialists, scientists and researchers that the country demands. In this context, a priority is to ensure that they are functioning at the forefront of intellectual and scientific development (Mercado, Cernas and Nava, 2016). Specifically, the postgraduate level is assuming a fundamental role in the formation of highly trained human talent that requires not only the productive field but also the scientific and technological one (Reyes, 2018). In this sense, a fundamental challenge of any country is to ensure that all its citizens have the right skills for an increasingly digitalized and globalized world that allows promoting both inclusive labor markets and stimulating innovation, productivity and growth. Particularly, skills are needed: technical and professional skills, use of ICT for workers who manage digital infrastructure and the functioning of the digital ecosystem; Soft skills such as leadership, communication and teamwork, required for the increasing number of opportunities for ICT-mediated collaborative work (OECD, 2015; OECD, 2016a; Grundke, Squicciarini, Jamet and Kalamova, 2017). To achieve this, educational systems are required to focus on an anticipating training of changing needs in skills to adapt the programs they offer to guide students towards options that lead to good results. In this way, it is not enough for workers to have the necessary skills for the digital economy, but employers must fully use these skills to propitiate their benefits in terms of higher productivity and greater competitiveness (OECD, 2016b). Also, it is necessary to encourage the use of high performance work practices such as teamwork, work autonomy; flexible training that allows promoting better organization and management of work within companies and throughout the economy, as well as fostering the necessary skills to consolidate the organization (OECD, 2016c).

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Since the twentieth century, countries, organizations and individuals have identified that the changes are continuous and that the context in which they live presents uncertainty and competitiveness as the main characteristics. The above implies a high investment in technology, and by extension it also implies the development of staff competences, since workers must acquire a new set of capabilities related to data management and analysis, computer-assisted production, online simulation, programming, predictive maintenance and the like (Ynzunza, 2017). In the same way, good managers are required who know how to exercise a leadership capable of communicating and guiding under this panorama to achieve the established objectives. Therefore, HEIs face the challenge of imparting training options that allow the development of competences that can cope with this reality (Martínez, Hernández and Gómra, 2016). This is complemented by the scenario that the manager in the 21st century faces challenges of various kinds and magnitude, so he has to be at the forefront to solve any situation he faces in the organization, so it is necessary to capitalize on his talent by developing a set of competences that allow him to respond to the challenges established by the environment (Ramírez, Cerón, Cerón and Maya, 2017). Faced with this situation, managers and future leaders are required to be trained to offer a quality service from the start-up of a set of competences to assume this essential function in any organization. Therefore, in an organization that promotes the development and improvement of managerial skills, it results in an implementation of effective strategies aimed at keeping the organization at a competitive level (Granada and Camisón, 2008). In this sense, any manager or one who aspires to be, it is necessary that he is constantly updating and improving them (Del Castillo, 2010). Thus, the objective of the chapter is to describe the main managerial competences to be formed at the postgraduate level, depending on the characteristics established in the context of the Fourth Industrial Revolution.

BACKGROUND At the beginning of the 21st century, it was recognized that education is a strategic and priority factor in the human, social and economic development of the countries, so that in order to achieve this, the coverage and quality which promote the competitiveness of organizations is necessary. In the same way, the relevance of postgraduate training was identified, which orientation was towards the generation of significant processes that would lead to the solution of local needs with the respective transfer of knowledge. Thus, there is a perspective focused on the postgraduate course leading to results of high social impact (Torres and Prieto, 2014). These changes not only involve an economic and cultural globalization, but also a redefinition of organizations towards the establishment of a new technological-productive model based on a reorganization of space and time, as well as new forms of management and realization of work. Also, it implies a modification of the rigid control system towards a free type system that refers to the fact that it is no longer necessary for the employee to be permanently in the organization due to the insertion of ICTs that now allow a discipline and rigor based in the use of knowledge (Araujo and Balduzzi, 2010). Given this panorama, a transformation of the pyramidal organization towards a normative-evaluative type based on the connection, collaborative work, autonomy and flexibility of the structure is required (Luci, 2009).

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In this sense, the training approach based on competences is relevant. Thus, a competence is defined as the acquisition of knowledge, attitudes, values and skills in order to successfully perform a series of activities in the work environment. So, this term is oriented towards the learning capacity of an individual that integrates the assimilation of new information to apply it effectively, relating it to its previous set (Alles, 2006). Therefore, a competence integrates three fundamental components: knowledge, skills (know-how) and attitudes (know how to be and want to do), which use in the performance of the person allow a certain achievement (Sagi, 2006). In this way, competences are acquired and expanded through learning, so they are acquired during academic training, they are activated in the labor market and they are used effectively for the economy and society, so all of the skills available for the economy at any given time make up the human capital of a country (OECD, 2017). On the other hand, Hellriegel, Jackson and Slocum (2010) define them as the set of knowledge, skills, behaviors and attitudes required by a person in order to be efficient in his managerial work in an organization. Likewise, they propose a holistic model integrated by the following competences: Communicative, self-administration, planning and management, strategic action, teamwork and multicultural. Meanwhile, Whetten and Cameron (2005) established that managerial competences allow linking the tools, techniques and attributes of personality with the strategy in an organization for the achievement of objectives, so that their model is structured by: motivation towards employees, communication, analytical and creative problem solving, empowerment and delegation, self-knowledge, stress management, team building and conflict resolution. Elseways, the inclusion of soft skills is important due to the high level of interrelation that is established through social networks and other technologies, which make the environment a constant exchange of data, information, communication and knowledge (Ortega, 2016). In this way, professionals who have a high integration of soft skills and technical competences will have a lower risk of being replaced by automatic processes that have interaction limitations (David, 2015; Frey and Osborne, 2017). On the other hand, Murti (2014) indicates that soft skills have been determined as necessary for effective work performance since they are transversal because they are related to the personality, attitude and behavior of each person. Thus, the profile of a graduate must be prepared for lifelong learning, have good communication and teamwork due to the fact that technical skills are not enough (Mafflioli and Giuliano, 2003). In the same way, Dahm, Farrell and Ramachandran (2015) determined that those professionals who have greater communication skills are perceived with greater technical capacity, which has an impact on the ease of finding employment. While the research carried out by Deming (2015) found that demand in the global soft skills labor market has grown by 24% because it has been recognized that technical skills are needed to be integrated with soft ones with a focus of multidisciplinary work. Likewise, Madrigal (2009) established that oral and written communication skills, together with teamwork have a high correlation to the professional success of a person and especially if he holds a managerial position. Therefore, training oriented towards the integral management of the organization is required, which is the process that interrelates the external environment with the internal one to generate the best management and governance, involving both the sustainable management of resources and social responsibility (Tejada and Peña, 2009).

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Thus, its relevance is to achieve the optimization of the processes, the fulfillment of goals and a structured planning that allows the organization to anticipate the environment where it is, based on the participation of its members, which contribute with systemic solutions since they have the skills to understand the various activities of the company in which they work (De Oliveira, 2013). To achieve this, a postgraduate program is required to consolidate the formation of human talent by articulating the various management processes that allow a profitability, positioning and competitiveness of the organization through the development of a set of competences (Barnett, 2002).

THE POSTGRADUATE AS AN ALTERNATIVE OF TRAINING OF MANAGERIAL COMPETENCES By recognizing that education is the fundamental factor that allows the formation of the human talent that is required for the various economic activities of a country that allow an integral development in a permanent way, so that in the 21st century new trends and different transformations have been established; therefore, the development of skills that allow individuals to adapt to different contexts, communicate effectively and use technologies to analyze and interpret the large amount of information that is generated is required (Lozano, Rosales & Giraldo, 2018). In the same way, Ynzunza, Izar, Bocarando, Aguilar and Larios (2017) establish that the implications of the Fourth Industrial Revolution in organizations are: First, the creation of new collaboration and social infrastructure schemes; other forms of humanmachine interaction; highly specialized job profiles; more complex work processes; assisted and dependent technology work environments coupled with requirements for the management of digital technologies, robots, programming and interpretation of data as transversal competences. Second, transformations oriented towards the digitalization of information and production systems for management activities; automation systems for data acquisition of machinery and production lines; with the exchange of information for monitoring and control of processes and decision making in real time, which means that the members of the organization also adapt to these fundamental changes. Thirdly, the speed at which data and knowledge are produced is growing rapidly, so that new requirements are imposed on the productive processes of organizations, so the formation of human talent is needed to direct and manage the appropriate cyber infrastructure (MacLeod, 2016). Given this panorama, HEIs need to be reinvented in the context of the Fourth Industrial Revolution because it has been identified that those organizations that are updated with scientific and technological advances are the best positioned because they are characterized by investing in research and development that allows them to generate innovations. From the perspective of competences, it is necessary to generate new training alternatives that allow their development in managers in order to define a profile that integrates a sustainable vision of the future, innovation and human development in the organization. Also, the relevant role of HEIs for the formation of human capital has been recognized because it is a competitive advantage that enables countries to promote their international competence, boost knowledge generation and therefore generate sustained growth (Leong & Kavanagh, 2013). On the other hand, Álvarez, Gómez and Rato (2004) established the need to form universal competences that are required by a future manager and which are the following:

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• • • • • • • • • • • • •

Analysis of organizational problems Delegation Sensitivity Decision making Opening Adaptability Analysis of events outside the organization Negotiation Creativity Initiative Risk taking Stress tolerance Motivation

While organizations in this scenario need to recognize as a main competitive advantage the formation of their human capital by implementing strategies that encourage their preparation at the postgraduate level, which will lead to effective and efficient management coupled with both professional and business growth (Lorusso, 2010). Therefore, the manager which requires the Fourth Industrial Revolution is a university graduate with a highly competitive postgraduate training that allows him to integrate different work teams, to perform diverse roles adapting quickly to changes, as well as to manage a set of social skills (Blanco and Latorre, 2012). In this sense, in the context of the Fourth Industrial Revolution it is necessary that the postgraduate course provides not only theoretical but also methodological foundations in a systemic way that covers the different management areas: management, administration, commercialization, marketing, human resources, environment, legal treatment, finance, information and communication technologies, mainly (Cardoso, 2018). Also, Delamare and Winterton (2007) established that higher education, especially postgraduate education, offered vocational training in line with the scientific and technological advances of the labor sector, which in the framework of economic globalization have changed not only in the productive field but also in the organizational, so new skills are required that allow not only the application of knowledge but also its generation and thus be able to conduct research and innovation in companies. Similarly, training in research at the postgraduate level is essential for future managers oriented towards the design, methodology and dissemination of projects that involve the development of competences related to the detection of problems, techniques and instruments for collecting and organizing information together with the analysis of results as well as the presentation of the solution proposal (Brunner and Uribe, 2007). That is, the interrelation of training in design, finance, strategies and sustainability, will mainly provide a set of tools for the management of the current processes that the organization requires in an interconnected market, with a sustainable, ethical, profitable and innovative business vision. Therefore, the formation of human talent with a systemic perspective of 360º is required, which is relevant for the development of innovation in products and services that provide value to the organization.

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Thus, based on the transformations established by the Fourth Industrial Revolution, it is required that the managerial capabilities of human capital also be modified and their training oriented at the postgraduate level. Specifically, the training of managerial competences at the postgraduate level is classified as follows (table 1): Table 1. Managerial competences MANAGERIAL COMPETENCES 4.0 Leadership capacity Decision making Conflict management Creativity Ability to work under pressure Personal resilience Personal

Communication Networking Teamwork and multidisciplinary collaboration Adaptability and flexibility to change Management and assignment of responsibilities Trust in new technologies Entrepreneurship Customer orientation Emotional intelligence

Strategic

Business vision Interfunctional orientation Resource management Negotiation Communication Leadership

Intra-strategies

Initiative Coaching Delegation Network of effective relationships Personal development Problem resolution

Personal effectiveness

Personal management Proactivity Integrity Self-government Study design

Investigative

Instrumental Management for disclosure

Source: Own elaboration

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Based on table 1, the managerial competences that are necessary to develop in future managers are classified into five categories: The first focused on the personal type that allows the interaction of the manager with the various members of the organization. The second oriented towards the strategic ones that allows the leader to make the decisions that lead to the fulfillment of objectives and goals. The third towards the intra-strategies that are necessary for every manager to handle because it implies the technical, practical and operational knowledge related to the processes of the organization. The fourth corresponds to those of personal effectiveness that are required because every manager needs to be recognized as a person who has to be in continuous professional development that allows him to face the different challenges he faces as the leader of an organization. The fifth focuses on research that allow training about knowledge and skills to undertake projects for the resolution of problems of social and economic relevance in the organization, so it implies analyzing and systematizing the actions of the manager to promote management of knowledge, as well as decision making in an argued form. It also implies carrying out the management of the results of the projects carried out. Therefore, this training is of a systemic nature because they are linked to the skills, personality and knowledge acquired so they integrate the competences, attitudes, motivation and commitment, which will allow management competences to manifest in habitual behaviors that will achieve the success of a person in his managerial function and therefore in the development of the organization.

FUTURE RESEARCH DIRECTIONS In the context of the Fourth Industrial Revolution, it is relevant to carry out systemic evaluations that allow the identification of long-term achievements by educational programs at the postgraduate level. Therefore, the impact assessment is a fundamental process for decision making in three moments: 1. Diagnostic or of entry, in order to shape the student’s income profiles, the characteristics of their work context and their training needs. 2. Formative or of process, oriented to the evaluation of the educational actions of the program during its implementation and, 3. Summative or of products, which purpose is to determine the level of achievement of the objectives and goals of the program that have been achieved, the changes in the training of students combined with their level of satisfaction. Likewise, it is relevant to include in the training of managers at the postgraduate level the impact of these educational programs in relation to sustainable development at both organizational and regional and national levels.

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CONCLUSION Given the growing complexity of today’s world, it is essential that management training at the postgraduate level be approached from a multidisciplinary approach based on the importance of research applied to the design and solution of organizational management. In the same way, it is necessary to integrate the development of soft skills that promote quality improvement to the organization in a sustainable environment. Therefore, a manager must develop certain competences that are manifested both in ways of thinking, feeling, acting and relating that allow him to successfully face managerial situations and experiences. It is precisely the success achieved in a given context that will constitute proof that the manager is a competent leader. Accordingly, it is relevant for organizations that the profile of their managers is oriented towards those people who have competences that serve as a basis for formulating strategies of competitive approach. Thus, the formation of managerial competences is required to focus on the knowledge of the organization, as well as the identification and resolution of problems in a dynamic context.

ACKNOWLEDGMENT This research was supported by the Instituto Politécnico Nacional, México [SIP number 20195564].

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Madrigal, B. (2009). Habilidades directivas. Mc Graw Hill. Mafflioli, F., & Giuliano, A. (2003). Tuning Engineering education into the European higher education orchestra. European Journal of Engineering Education, 28(3), 251–273. doi:10.1080/0304379031000098832 Marcovitch, J. (2002). La universidad (im)posible. Cambridge, UK: Cambridge University Press. Martínez, M., Hernández, M., & Gómora, J. (2016). Modelo de competencias directivas en escenarios globales para las instituciones de educación superior. Revista Iberoamericana para la Investigación y el Desarrollo Educativo, 6(12), 321-333. Retrieved from https://www.ride.org.mx/index.php/RIDE/ issue/view/12 Mercado, Cernas, & Nava. (2016). Interdisciplinariedad Económico-Administrativa en la Conformación de una Comunidad Científica y la Formación de Investigadores. Revista de la Educación Superior, 45(177), 43 – 65. Murti, A. (2014). Why soft skills matter. The IUP Journal of Soft Skills, 8(3), 32–36. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2636655 OECD. (2015). OECD Science, Technology and Industry Scoreboard 2015. Paris: OECD Publishing. doi:10.1787ti_scoreboard-2015OECD. (2016a). Skills for a Digital World: 2016 Ministerial Meeting on the Digital Economy Background Report. Paris: OECD Publishing. doi:10.1787/5jlwz83z3wnwOECD. (2016b). Getting Skills Right: Anticipating and Responding to Changing Skill Needs. Paris: OECD Publishing. doi:10.1787/9789264252073OECD. (2016c). New Markets and New Jobs. Paris: OECD Publishing; doi:10.1787/5jlwt496h37lOECD. (2017). Diagnóstico sobre las estrategias de competencias, destrezas y habilidades de México. Retrieved from https://www.oecd.org/mexico/Diagnostico-de-la-OCDEsobre-la-Estrategia-de-Competencias-Destrezas-y-Habilidades-de-MexicoResumen-Ejecutivo.pdf Ortega, S., Febles, R., & Estrada, S. (2016). Una estrategia para la formación de competencias blandas desde edades tempranas. Revista Cubana de Educación Superior, 35(2), 35–41. Retrieved from http:// scielo.sld.cu/scielo.php?script=sci_arttext&pid=S0257-43142016000200003&lng=es&tlng=es Ramírez, J., Cerón, H., Cerón, A., & Maya, N. (2017). Las competencias directivas base de la competitividad empresarial: Un estudio correlacional. Revista Administración y Finanzas, 4(12), 87-98. Retrieved from http://www.ecorfan.org/bolivia/researchjournals/Administracion_y_Finanzas/vol4num12/ Revista_de%20_Administraci%C3%B3n_y_Finanzas_V4_N12_7.pdf Reyes, G. (2018). Análisis comparativo de programas de Maestría en Tecnología Educativa, tendencias actuales en la formación de futuros profesionistas. International Journal of Information Systems and Software Engineering for Big Companies, 5(2), 29–40. Sagi, L. (2006). Gestión por competencias: el reto compartido del crecimiento personal y de la organización. Madrid: ESIC editorial.

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Tejada, F., & Peña, G. (2009). Reflexiones sobre las características constitutivas de la gestión integral. Revista Signos, 1(2), 87–91. Torres, D., & Prieto, J. (2014). Posgrados: Visión y percepción en la Universidad Pedagógica y Tecnológica de Colombia. Revista Historia de la Educación Latinoamericana, 16(22), 249–273. Velásquez, O. (2019). La 4RI. Como parte de la reflexión universitaria. Pensamiento Universitario, 29, 2. Retrieved from https://ascun.org.co/uploads/default/publications/5aeee20947a9b5676665ca2a7d4f2 86d.pdf Whetten, D., & Cameron, K. (2005). Desarrollo de habilidades directivas. Pearson. World Economic Forum (WEF). (2017). Project Plan Overview: 21st Century Curriculum Project. Sydney, Australia: World Economic Forum. Ynzunza, C., Izar, J., Bocarando, J., Aguilar, F., & Larios, M. (2017). El Entorno de la Industria 4.0: Implicaciones y Perspectivas Futuras. Conciencia Tecnológica, 54, 86-98. Retrieved from http://www. redalyc.org/jatsRepo/944/94454631006/94454631006.pdf

ADDITIONAL READING Camarillo, J. (2017). La dirección escolar, su concepción en México. Conexxión Journal, 6(16), 16–24. Iranzo, P., Camarero, M., Tierno, J., & Barrios, C. (2018). Formación para la función directiva en la escuela. Bordón, 70(2), 57–72. Ministerio de Educación (2017). Reporte para orientar el diseño de un sistema de desarrollo profesional directivo para Chile. Santiago de Chile: Ministerio de Educación. Muñoz, G. (2018). Estudio exploratorio sobre modelos internacionales de formación de directores y supervisores: un análisis en clave comparada. Buenos Aires: UNESCO. Poggi, M. (2001). La formación de directivos de instituciones educativas. Buenos Aires: UNESCO. Rivero, R., Hurtado, C., & Morandé, Á. (2018). ¿Cuán preparados llegan los directores escolares?: Un análisis sobre su formación y trayectorias laborales previas a ejercer su cargo. Quality in Education, 48, 17–49. UNESCO. (2015). Liderazgo escolar en América Latina y el Caribe: Experiencias innovadoras de formación de directivos escolares en la región. Santiago de Chile. UNESCO. Vázquez, S., Liesa, M., & Bernal, J. (2016). El camino hacia la profesionalización de la función directiva: El perfil competencial y la formación del director de centros educativos en España. Educational Profiles, 38(151), 158–174. Weinstein, J., Cuellar, C., Hernández, M., & Flessa, J. (2015). Experiencias innovadoras y renovación de la formación directiva latinoamericana. Iberoamerican Journal of Education, 69, 23–46.

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KEY TERMS AND DEFINITIONS Artificial Intelligence: Simulation of cognitive processes that are usually associated with human intelligence, corresponding to computer systems designed to interact with the world through functionalities (such as visual perception and voice recognition) and intelligent behaviors that we conceive as essentially human (for example, evaluate the available information and act in the most sensible way to achieve a certain objective). Augmented Reality: Use of technology that complements the perception and interaction with the real world and allows the student to superimpose a layer of information on reality, thus providing richer and more immersive learning experiences. Competence: Ability that the person has in being, knowing and knowing how to do in real situations, before a new task, to solve problems, make decisions and develop projects, from an entrepreneurial and managerial vision. Fourth Industrial Revolution: It is identified as the digital interconnection of people, machines and objects that offers immense possibilities of increasing production efficiency to individuals, companies and nations. Internet of Things: Interconnection of everyday objects with the Internet so it uses objects equipped with embedded processors or sensors that are capable of transmitting information through the network. These connections allow remote administration, status monitoring, tracking, and alerts.

This research was previously published in Management Training Programs in Higher Education for the Fourth Industrial Revolution; pages 95-111, copyright year 2020 by Information Science Reference (an imprint of IGI Global).

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Professional Training in Tourism for the Fourth Industrial Revolution Rosa María Rivas García https://orcid.org/0000-0002-8371-6068 Instituto Politécnico Nacional, Mexico Jésica Alhelí Cortés Ruiz https://orcid.org/0000-0002-5459-4874 Instituto Politécnico Nacional, Mexico

ABSTRACT At present, the World Tourism Organization indicates that, as a worldwide export category, tourism occupies the third position, behind chemical and fuel products and ahead of the automotive industry. In many developing countries, tourism is the main export sector. Therefore, the purpose of this chapter is to propose an approach to the training of tourism professionals for the fourth industrial revolution, so this chapter proposes an approach to educational competencies in the training of tourism professionals for Industry 4.0 with a focus in sustainable development; initially, the subject of educational competencies in higher education will be described, since derived from these, professional competencies are achieved. Next, the exploration of the concepts of intellectual capital, tourism, and the fourth industrial revolution will be shown; to conclude the authors show the relation of the thematic axes.

INTRODUCTION The evolution and diversification of tourism, as well as the advancement of science, information and communication technologies, the digitalization of industrial processes, the optimization of resources focused on the effectiveness of commercial methods; They have transformed the processes, methods, and in general the way of conducting business. Tourism as the third position of export category worldwide, requires the development of concepts and theories with an educational management approach for DOI: 10.4018/978-1-7998-8548-1.ch085

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the training of professionals in the tourism sector based on knowledge management, educational and professional skills, intellectual capital and the interaction of the tourist industrial activity in the context of the fourth industrial revolution. The assertive use of resources oriented to the formation, production, adaptation and evaluation of new knowledge are a factor of competitive advantage; Human capital based on its competencies and capabilities as an intangible resource of an organization has developed its importance until it becomes the main generator of value. At present, knowledge is a key element in the creation and generation of competitiveness and heritage in organizations. In this environment, the thematic axes of this chapter are established in the conceptual confluence of professional competences and intellectual capital within the framework of the fourth industrial revolution in the professional activity in tourism. In order to specify the themes of the chapter, it is necessary to base the perceptions of educational competences in higher education and intellectual capital; to frame them in the training of tourism professionals with a focus on sustainable development. As the tourism industry evolves, it tends to the effects of globalization and innovation of tourism products; In addition, these changes have an impact on the human capital of the industrial sector and the fourth fourth revolution. Therefore, the thematic axes of the section are introduced, with the contributions of educational competences, professional competences, intellectual capital, tourism; framed in industry 4.0.

EDUCATIONAL COMPETENCE IN HIGHER EDUCATION First, the concept of educational competencies will be addressed; Due to the polysemic nature of the word “competence”, this content describes the concept - Expertise, aptitude, suitability to do something or intervene in a specific matter. Because the Royal Spanish Academy (2018), considers different meanings for the word in question. competition1 From lat. competentia; cf. to compete. 1. f. Dispute or contest between two or more people about something. 2. f. Opposition or rivalry between two or more people who aspire to obtain the same thing. 3. f. Situation of companies that compete in a market offering or demanding the same product or service. 4. f. Rival person or group The competition has been passed. 5. f. Am. Sports competition. competition2 From lat. competentia; cf. competent. 1. f. incumbency. 2. f. Expertise, aptitude or suitability to do something or intervene in a specific matter. 3. f. Legal scope of powers that correspond to a public entity or to a judicial or administrative authority. Next, a review of the conceptual contributions of the topic educational competences; defined by various authors. 1700

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According to McClelland (1973). The competences are linked to a way of evaluating what “really causes a superior performance at work”, and not “to the evaluation of the factors that reliably describe all the characteristics of a person, in the hope that some are associated with work performance”. Chomsky (1985), based on language theories, establishes the concept and defines competencies such as capacity and disposition for performance and interpretation. De Ketele (1996) defines the educational competences to the ordered set of abilities (activities) that are exerted on the learning contents, and whose integration allows solving the problems that arise within a category of situations. Set of tasks more or less of the same type, within a family of situations. On the other hand, Perrenoud (2007) mentions that educational competence is the ability to mobilize a set of resources (knowledge, skills, information, etc.) to effectively solve a series of situations connected to cultural, professional and social conditions. The Organization for Economic Cooperation and Development (2013) defines an educational competence as a concept greater than knowledge and skills. It implies the ability to respond to complex demands, using and mobilizing psychosocial resources (including skills and attitudes) in a context. Under which each degree develops competencies; some of their own or specific to the corresponding degree, while others are transversal or shared with other degrees; the concepts refer to the breadth of the material that the various authors have developed to explain said matter; Therefore, some of the classifications adopted to raise the subject does not establish any specific aspect, terminology or views of any author; • •

Specific competences, which are specific to a field or degree and are aimed at achieving a specific profile of the graduate. They are close to certain formative aspects, areas of knowledge or groupings of subjects, and tend to have a longitudinal projection in the degree. Generic (or transversal) competences, are common to most of the degrees, although with a different incidence, and are contextualized in each of the degrees in question. Within this block we find personal skills such as time management and the responsibility of learning itself; interpersonal skills, work in teams, lead or negotiate; competences related to information management, languages, information technology, etc., the latter competences are under the name of instrumentals. They are focused on the type of knowledge, dispositions and attitudes that graduates of different formations must develop throughout their lives; These allow regulation as a professional aware of the social, scientific, technological and cultural opportunities.

According to Gairín Sallán (2009), the specific competences are specific to a field or degree, which are aimed at achieving a specific profile of the graduate. These are close to certain formative aspects, areas of knowledge or groupings of subjects, with longitudinal projection in the degree. On the other hand, generic (or transversal) competences are common to most degrees, although with a different impact, and are contextualized in each of the degrees in question. Within this block are personal skills, which are linked to time management and the responsibility of learning itself; as well as interpersonal skills, related to teamwork, leading an action or negotiating a conflict, or instrumental skills, associated with information management, language proficiency, technology, etc. In addition to the above concepts, then a synthesis of the subject competences in higher education as a frame of reference. The OECD Definition and Selection of Skills Project (DESECO) (2006) provides a framework that can guide a long-term extension of evaluations of new domains of competencies. The objective of the 1701

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PISA report, for example, is to monitor how students who are at the end of compulsory schooling have acquired the knowledge and skills necessary for their full participation in society; Therefore, the key competences are classified into three broad categories. First, individuals must be able to use a wide range of tools both physical (use of information technology) and sociocultural (use of language) to effectively interact with the environment. Second, people need to be able to communicate with others, as we are in an increasingly interdependent world. And, thirdly, individuals must take responsibility for managing their own lives to act autonomously. The World Conference on Higher Education (UNESCO, 2009) discussed the need to promote lifelong learning that allows the construction of appropriate skills to contribute to the cultural, social and economic development of society. It was also indicated that the main tasks of higher education have been and will continue to be linked to four of its main functions: a generation with new knowledge (research functions), training of highly qualified people (education function), provide services to society (social function) and social criticism (ethical function). According to Cano (2008), competency training at this academic level involves articulating conceptual, procedural and attitudinal knowledge, and relies on the personality traits of the subject to build learning; In addition, it requires reflexive action, is functional and moves away from standardized behavior. Derived from the previous concepts, the educational competences in higher education are developed from the knowledge and discipline of each degree and are the following: KNOWLEDGE Competencies: Cognitive, disciplinary, conceptual. Skills the KNOWLEDGE TO DO: Procedural and instrumental. TO BE competences: Attitudinal and value. Generic: Transversal Competencies

Intellectual Capital The sequence of the chapter exposes some of the main definitions of the Intellectual Capital concept; from a competitive advantage generation approach, since it is an intangible non-transferable asset that generates capital in an organization. As Steward (1998), defined as intellectual material, knowledge, information, intellectual property, experience, which can be used to create value, wealth is the product of knowledge. This and information have become the fundamental raw materials of the economy and its most important products. Well, Salmador and Merino (2008), mention that intellectual capital indicates the value of accumulated wealth derived from knowledge or a set of intangible assets. It combines intangible assets, which create new knowledge; This becomes business skills or the creation of competitive advantage. It generates value to the company and represents the new wealth of organizations and nations. It is not usually reflected in the financial statements of a company. Intangible asset and is represented by the competencies of the worker at any hierarchical level or position; that is, what knowledge do you have, what skills you have developed and what attitudes you reflect in your work performance for the benefit of the organization. (Sarur, 2013). Derived from the above concepts, it is necessary that intangible assets for an organization, determined in the knowledge as a capital producing agent outlines the concept of intellectual capital. In this way, 1702

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intellectual capital is the set of intangible assets based on the knowledge and skills of employees in a value creation system.

Tourism Tourism is a social, cultural and economic phenomenon related to the movement of people to places that are outside their usual place of residence for personal or business / professional reasons. These people are called visitors and tourism has to do with their activities, of which some involve an expense. (OMT, 2018b). With data from the OMT Panorama of international tourism. 2018 edition; The following points are outlined that outline tourism as a key to development, prosperity and well-being; • •

International tourism revenues increased by 4.9% in real terms (figure adjusted for exchange rate fluctuation and inflation), reaching 1.34 trillion (1.340 billion) of US dollars. UU. in 2017 A robust demand for travel, both in traditional and emerging markets, led to growth in income worldwide, in line with the positive trend in international tourist arrivals (+ 7%).

It is of vital importance the tourist activity; since it promotes cultural preservation, environmental protection, peace and security, jobs, economic growth, development. Likewise, the specific data of such importance are; • • • • •

1/10 jobs in the world derived from tourism. 1.6 billion US dollars in exports. 10% of world GDP. 7% of world exports. 30% of exports in services. Here are some definitions of the tourism concept;

Table 1. Brief exploration of the concept tourism Autores

Definición de Turismo

Glucksmann, Schwinck o Bormann (1919-1938)

Tourism, as a matter of university research, begins to interest in the period between the two great world wars of this century. During this period, European economists began to publish the first works, highlighting the so-called Berlin school.

OMT (1994)

Tourism includes the activities carried out by people during their trips and stays in places other than their usual environment, for a consecutive period of less than one year for leisure, business and other purposes.

OMT (2014)

Tourism is a social, cultural and economic phenomenon related to the movement of people to places that are outside their usual place of residence for personal or business / professional reasons. These people are called visitors (who can be tourists or hikers, residents or nonresidents) and tourism is related to their activities, which involve a tourist expense.

Source: Own elaboration with data of the cited authors

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The mentioned mentions mark common elements; there is a physical movement of tourists who are those who move outside their place of residence, the stay at the destination must be for a certain period, not permanent. Tourism includes both the trip to the destination and the activities carried out during the stay, the motivation for travel is diverse and tourism covers the services and products created to meet the needs of tourists.

Fourth Industrial Revolution The concept of the fourth industrial revolution, also referred to as industry 4.0, is discussed below with the following contributions; The concept of industry 4.0, emerged in Germany in 2011, to refer to a government economic policy based on high-tech strategies (Mosconi, 2015); characterized by automation, digitalization of processes and the use of electronics and information technologies in manufacturing. Likewise, for the personalization of production, the provision of services and the creation of value-added businesses. And, for the capabilities of interaction and the exchange of information between humans and machines. (Sommer, 2015). Capilla (2017), states that industry 4.0 is a new form of organization and operation of the industry, a new industrial revolution in which a connection from the user to the manufacture of a product is implemented. Despite its recent emergence, industry 4.0 can be considered as the fourth industrial revolution, in which the forms of production make use of cybernetic physical systems to create a more flexible and reconfigurable industry, that is, that the structure of a Factory can be modified to produce different products. Within this new industry there are nine types of recent technological advances: big data and data analysis, autonomous robots, computer process simulation, integration systems, internet of things applied to industry, cybersecurity, information storage in the cloud, 3D printing or additive manufacturing and augmented reality. Schwab (2016), describes that “industry 4.0” was a term coined at the Hannover fair in 2011 to describe how it will revolutionize the organization of global value chains. By creating “smart factories,” the fourth industrial revolution generates a world in which virtual and physical manufacturing systems cooperate with each other in a flexible way across the globe. This allows the absolute customization of the products and the creation of new operation models. The fourth industrial revolution, however, not only consists of connected intelligent machines and systems. Its scope is wider. At the same time, there are waves of more progress in areas ranging from genetic sequencing to nanotechnology and renewable energy to quantum computing. It is the fusion of these technologies and their interaction through physical, digital and biological domains that makes the fourth industrial revolution fundamentally different from previous industrial revolutions.

Training Of The Professional In Tourism For The Fourth Industrial Revolution The fourth industrial revolution generates millions of new jobs globally for people with appropriate skills and training. One of the biggest challenges for governments, educational institutions, companies and any entity interested in the development of intellectual capital; It is the training of the workforce of the future and simultaneously the transition of current workers to the new economic activity called Industry 4.0. with a focus on sustainable development. Because the term fourth industrial revolution or industry 4.0, is newly created (less than ten years), we do not have full understanding of this change. The unlimited possibility of having billions of people 1704

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connected through mobile devices in real time, the capacity for processing and storage, access to information, the confluence of technological advances that cover wide fields, such as artificial intelligence, robotics, the internet of things, autonomous vehicles, 3D printing, nanotechnology, biotechnology, materials science, energy storage, quantum computing, to name a few. New technologies are changing the way we live, work and interact with each other and the speed, breadth and depth of this revolution are forcing us to rethink how countries develop, how organizations generate value and even what What it means to be human. (Schwab, 2016). With a focus on this chapter, it is necessary to emphasize that tourism is an important economic activity at a global level, international tourism revenues mean that tourism is the third largest category of exports in the world. As a worldwide export category, tourism occupies the third position, behind chemical and fuel products and ahead of the automotive industry. In many developing countries, tourism is the main export sector. (OMT, 2018). The positive peculiarities of tourism highlight the importance of the activity, which is why it requires the formation of competent human capital to generate solutions to the needs posed by the activity, so that the training of the tourism professional in the field of educational skills and it’s the relationship with the formation of intellectual capital in the context of the fourth industrial revolution is transcendent. In addition to the relevance of sustainable development that is part of the generic professional competences; with the purpose of promoting prosperity, economic opportunities, social welfare and environmental protection. From the perspective of the Organization for Economic Cooperation and Development in relation to education and human resources strategies in the tourism sector, the OECD makes recommendations to member countries through the publication OECD Tourism Trends and Policies (2018) in which he points out; supply-side public policies to improve competitiveness include the promotion of investments and the simplification of trade regulations; However, it is recognized that it may be necessary to expand and clarify regulations in emerging areas such as informal tourism services promoted through electronic platforms. It is recognized that a key aspect for many countries is the need to resolve the shortage of personnel and skills in the sector, which requires working to improve the dissemination and attractiveness of careers in the tourism sector and the availability of relevant training. On the other hand, intellectual capital is established as an intangible asset and is represented by the competencies of the worker at any hierarchical level or position; that is, what knowledge do you have, what skills you have developed and what attitudes you reflect in your work performance for the benefit of the organization (Sarur, 2013). Subsequently, the exploration of information related to the theory of professional competences and their link with the intellectual capital of the tourism industry in the context of the fourth industrial revolution; It is necessary to establish the importance of this intangible asset, relevant in the achievement of organizational goals, since human resources are considered the most relevant when contributing to the fulfillment of strategic objectives. Therefore, it is important to point out the professional competencies necessary for the formation of the intellectual capital of tourism professionals for the adequate work within the framework of the fourth industrial revolution with alignment to sustainable development; based on information from the UN Mexico (2017, par. 4). Sustainable development; It is the shared, holistic and long-term vision that countries have agreed on as the best way to improve the lives of people around the world. Sustainable development promotes prosperity and economic opportunities, greater social well-being and environmental protection.

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Therefore, in relation to the review of previous concepts related to educational competences in higher education that result in professional competences, they develop from the knowledge and discipline of each degree; Therefore, for the tourism professional, the following are defined based on the research of an evaluative nature of the profile of the tourism professional, based on the research of the tourism industry, educational competencies and the multidisciplinary training necessary for the sector of Rivas (2107). KNOWLEDGE Competencies: Cognitive, disciplinary, conceptual. ◦◦ Understanding the tourism industry for the development of management in the various tourism organizations and companies, considering aspects of spatial, social, cultural, political, labor and economic dimension. ◦◦ Management knowledge. ◦◦ Business knowledge. ◦◦ Marketing knowledge for the effective commercialization of the different tourism products and implementation of commercial objectives, strategies and policies. ◦◦ Digital marketing. ◦◦ Knowledge of the legal framework and regulations of tourism activities. ◦◦ Analysis of the impacts generated by tourism. ◦◦ Management of the tourist territory in accordance with the principles of sustainability in the tourism field. ◦◦ Management of profitable, socially participatory and environmentally responsible tourism projects. ◦◦ Evaluation of tourism potentials and prospective analysis of their development. ◦◦ Financial resources management. ◦◦ Direction and management of different types of tourism entities. ◦◦ Knowledge of the objectives, strategy and public planning instruments. ◦◦ Knowledge and understanding of cultural heritage management. ◦◦ Knowledge and understanding of natural heritage management. ◦◦ Planning of infrastructure and tourist facilities based on public policies of the sector. ◦◦ Planning and management of human capital in tourism organizations. Skills the KNOWLEDGE TO DO: Procedural and instrumental. • • • • • • • • • • • 1706

Oral and written communication in own language (Spanish). Oral and written communication in a second language (preferably English and / or Chinese); based on bilingual training. Use of specialized tourism software. (Reservation systems) Administrative and operational procedures in the industrial housing sector. Administrative and operational procedures in the industrial field of food and beverage provision. Administrative and operational procedures in the industrial field of passenger transport by rail. Administrative and operational procedures in the industrial field of passenger transport by road. Administrative and operational procedures of the industrial field of passenger transport by water. Administrative and operational procedures in the industrial field of passenger air transport. Administrative and operational procedures in the industrial field of transport equipment rental. Administrative and operational procedures in the industrial field of passenger transport by rail.

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• • • • •

Travel agency activities and other reservation services. Administrative and operational procedures in the industrial field of travel agency activities and other reservation services. Administrative and operational procedures in the industrial field of cultural, sports and recreational activities. Administrative and operational procedures in the industrial field of other tourism-specific activities, specific in each country. Use of information and communication technologies. TO BE competences: Attitudinal and value.

• • • • • • • •

Motivation for quality. Initiative and entrepreneurial spirit. Ability to work in multidisciplinary and interdisciplinary teams. Appreciation of diversity and multiculturalism through knowledge of cultures and customs of other regions and countries. Ability to work in an international context. Creativity. Leadership Have a marked customer service orientation Generic - Transversal Competencies

• • • • • • • • • •

Research skills Problem solving Decision-making capacity Loyalty Ability to organize and plan Capacity for analysis and synthesis Skills on information management Ethical commitment Ability to learn Commitment towards sustainable development

FUTURE RESEARCH DIRECTIONS Coming from the recent concept of the fourth industrial revolution; It is necessary to develop exploratory and descriptive research with a qualitative approach to the topic to demonstrate the importance in the preparation, growth and development of intellectual capital in the tourism industry; coupled with the relationship of educational competences, by virtue of the fact that human capital is capable of developing a competitive advantage based on professional training. Therefore, future research is especially recommended in companies in the tourism industry, with their respective business characteristics, because according to the International Tourism Panorama of the UNWTO (2018), as a world export category, 1707

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tourism It ranks third, just behind chemicals and fuels. In many developing countries, tourism is the main category of exports. And it is a key sector for socioeconomic progress, through job creation.

CONCLUSION Although all the professional competences previously described are important for the adequate labor performance in the tourism industry, to achieve competitiveness in the era of the fourth industrial revolution with a focus on sustainable development, there must be emphasis on the competencies of KNOWLEDGE: Cognitive, disciplinary, conceptual ; such as the understanding of the tourism industry for the development of management in the various tourism organizations and companies, considering aspects of spatial, social, cultural, political, labor and economic dimension, the analysis of the impacts generated by tourism, management of the tourist territory in accordance with the principles of sustainability in the tourism field, the management of profitable, socially participatory and environmentally responsible tourism projects, the evaluation of tourism potentials and the prospective analysis of their development, knowledge and understanding of the management of the Cultural heritage, knowledge and understanding of natural heritage management, planning and management of human capital in tourism organizations. The powers of BE: Attitudinal and value. Specifically, in the appreciation of diversity and multiculturalism through the knowledge of cultures and customs of other regions and countries. And the powers of KNOWLEDGE TO DO: Procedural and instrumental; as well as in the ability to learn (competence derived from generic - transversal competences); as are the ethical commitment and the commitment towards sustainable development. In order that prosperity, social welfare, environmental protection, economic opportunities result in equitable and fair societies; without compromising the resources of the future. Derived from the above, the intellectual capital of tourism organizations “the human being” typified as the asset that incorporates the capabilities of the worker at any hierarchical level (and in any industry) based on professional skills; It develops knowledge, skills and attitudes that it reflects in its work activity in favor of organizations and generates competitive advantage. It is the workforce that has built, builds and will build economic activity, so it is unavoidable that it has and maintains the habit of learning daily for its insertion in the fourth industrial revolution that outlines flexible cooperation across the planet for the personalization of products and services through new operation models.

REFERENCES Bueno, E., Salmador, M., & Merino, C. (2008). Genesis, concept and development of intellectual capital in the knowledge economy: A reflection on the Intellectus Model and its applications. Applied Economics Studies, 26(2), 43-63. Retrieved from http://hdl.handle.net/10486/669095 Cano, G. (2008). The evaluation by competences in higher education. Retrieved from http://www.urg. es / local / recfpro / rev123COL1.pdf Chapel, R. (2017). What is industry 4.0? Recovered from http://www.conacytprensa.mx/index.php/ sociedad/politica-cientifica/18282-la-industria-4-0

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Chomsky, N. (1983). Language and unconscious knowledge. In Rules and representations. FCE. De Ketele, J. (1996). School Performance Evaluation: What? Why? For what? Tunisia Journal of Science Education, (23), 17-36. Gairín Sallán, J. (Ed.). (2009). Guide for the evaluation of competencies in the area of social sciences. Barcelona: Agency for the Qualitat of the University System of Catalonia. Glucksmann, Schwinck, & Bormann. (1919-1938). Tourism concept and approach. Berlin School. McClelland, D. C. (1973). Testing for Competencies rather than intelligence. USA. The American Psychologist, 28(1), 1–14. doi:10.1037/h0034092 PMID:4684069 Mosconi, F. (2015). The new European industrial policy: Global competitiveness and the manufacturing renaissance. London, UK: Routledge. doi:10.4324/9781315761756 OECD. (2013). Panorama of education 2013 Indicators of the OECD. Santillana. OECD. (2018). OECD Tourism Trends and Policies 2018. Paris: OECD Publishing; doi:10.1787/tour-2018OMT. (1994). Introduction to Tourism. Retrieved from https://pub.unwto.org/WebRoot/Store/ Shops/.../1128/9284402697.pdf OMT. (2014). Understanding tourism Basic Glossary. Recovered from http://media.unwto.org/en/content/ entender-el-turismo-glosario-basico OMT. (2018). OMT panorama of international tourism. Edition 2017. Madrid: World Tourism Organization. Retrieved from https://www.e-unwto.org/doi/pdf/10.18111/9789284419890 ONU Mexico. (2017). What is sustainable development and why is it important? Recovered from http:// www.onu.org.mx/que-es-el-desarrollo-sostenible-y-por-que-es-importante/ Organization for Economic Cooperation and Development (OECD). (2006). The definition and selection of key competences. Executive Summary. Retrieved from http://www.deseco.admin.ch/bfs/deseco/en/ index/03/02.parsys.78532.downloadList.9 4248.DownloadFile.tmp / 2005.dscexecutivesummary.sp.pdf Perrenoud, P. (2007). Ten new competences to teach (4th ed.). Barcelona: Graó. RAE. (2018). Spanish dictionary. Edition of the Tercentenary. Madrid: Royal Spanish Academy. Retrieved from http://dle.rae.es/?id=A0fanvT|A0gTnnL Rivas, R. (2017). Diagnostic study on the management of professional competences of the graduate in tourism based on a multidisciplinary training. Case Higher School of Tourism of the National Polytechnic Institute. Generations 2012 and 2013 (Doctoral thesis). National Polytechnic Institute, Mexico. Sarur, M. (2013). The importance of intellectual capital in organizations. Administrative Sciences, 1, 39–45. Schwab, K. (2016). The fourth industrial revolution. Ed. Debate. Sommer, L. (2015). Industrial revolution-Industry 4.0: Are German manufacturing SMEs the first victims of this revolution. Academic Press. Steward, T. (1998). The new wealth of organizations: intellectual capital. Granica.

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UNESCO. (2009). Communiqué of the World Conference on Higher Education 2009: the new dynamics of higher education and research for social change and development. Paris: UNESCO. Retrieved from www.unesco.org/education/WCHE2009 World Tourism Organization (UNWTO). (2018b). Understanding tourism: basic glossary. Retrieved from http://media.unwto.org/en/content/entender-el-turismo-glosario-basico

KEY TERMS AND DEFINITIONS Educational Competence: Complex development processes which are suitable in contexts, by integrating different knowledge (knowing how to be, knowing how to do, knowing to know, knowing to live with), in order to do activities and / or solve problems with a sense of challenge, motivation, flexibility, creativity, understanding and undertaking, within an approach of meta-cognitive thinking, ongoing improvement and ethical commitment, which are aimed at contributing to personal development, the construction and strengthening of social networking, the continuous search of a sustainable economic and business development, and the care and protection of the environment and the living organisms. Fourth Industrial Revolution or Industry 4.0: Emerged in Germany in 2011, to refer to a government economic policy based on high-tech strategies; characterized by automation, the digitalization of processes and the use of electronics and information technologies in manufacturing. Also, for the personalization of production, the provision of services and the creation of value-added businesses. And, for the capabilities of interaction and the exchange of information between humans and machines. Intellectual Capital: Set of intangible assets based on knowledge and access to the evolution of resources in a system of value creation, through the achievement of sustainable competitive advantages. Professional Competence: The degree of utilization of knowledge, skills and the good judgment related to the people’s profession, and in correspondence with all the situations that can be lived in the exercise of professional practice. Tourism: A social, cultural, and economic phenomenon related to the movement of people to the places that are outside their usual location of residence for personal or business/professional reasons. While these people are called visitors (which may be tourists or excursionists; residents or non-residents), tourism is associated with the people’s activities and, therefore, implies tourism expenditure. Tourism Industry: The term tourism industries include the factory complexes that regularly produce tourism products; and is one of the most colloquial terms used in the tourism sector. UNWTO: The World Tourism Organization (UNWTO) is the United Nations agency responsible for the promotion of responsible, sustainable, and universally accessible tourism.

This research was previously published in Management Training Programs in Higher Education for the Fourth Industrial Revolution; pages 62-78, copyright year 2020 by Information Science Reference (an imprint of IGI Global).

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The New Challenges in the Training of the Engineer for the Industry 4.0: A Case Study of a Brazilian University Center Sergio Ricardo Mazini University Center Toledo Araçatuba, Brazil Márcia Maria Teresa Baptistella University Center Toledo Araçatuba, Brazil

ABSTRACT The growing changes brought about by new concepts and new technologies, such as Industry 4.0, have demanded that educational institutions seek new teaching and learning methodologies, as well as new resources that can contribute to the training of the future engineer. This chapter demonstrates some practices adopted in the process of training the future engineer in a university center in the interior of the state of São Paulo through the use of the CDIO initiative. The results presented confirm the importance and necessity of changes in the teaching and learning process in higher education institutions.

INTRODUCTION Since the beginning of time, evolution has been a constant in all areas, there is always the need to adapt and innovate to follow the various changes that have been taking place in the world, both for companies, industries, services, as well as people. Industry 4.0 or the fourth industrial revolution, as many call it, reflects these changes and the various adaptations that are necessary for business development and continuity to be possible, where people need to innovate and adapt to new realities and technological and

DOI: 10.4018/978-1-7998-8548-1.ch086

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 The New Challenges in the Training of the Engineer for the Industry 4.0

market trends, as new professions emerge while others disappear, new business segments thrive while others decline, and so on. While executives are aware of the imperative need to embrace innovation as a business strategy, they recognize that they lack the knowledge of the tools and tools that make it possible to put theory into practice. “Companies do not adopt consistent practices of innovation, do not prepare their leaders, and do not have ways to measure the innovation process” (Scherer & Carlomagno, 2016, p. 4). Still according to Scherer & Carlomagno (2016), few companies are considered innovative because innovating demands time, needs to value people, means tolerating mistakes, taking risks and changing, but above all must lead to results. However, in the world where total quality and zero defect prevail, there is not much room for creativity and innovation, as it is cheaper to continue doing the usual and routine than to devote time and resources in the search for new and uncertain alternatives, where the development of a new idea, a new product or a new process can take months or even years, because to innovate it is necessary to incorporate the new knowledge obtained for the elaboration of new products and services that add value to the company. The process of innovation is based on the sharing of tacit knowledge, which is in people’s minds, and encouraging the sharing of this knowledge means valuing people. Knowledge is one of the greatest human assets because it is the result of a complex combination of skills, competencies and attitudes that allow a person to understand, critique, analyze and interpret facts and data, developing the ability to master techniques, processes, ideas, concepts and evolutions (PÁDUA FILHO, 2016). For Alvarenga Neto (2008), knowledge companies are organizations that have information and knowledge that make them differentiated, with capacities of perception and discernment, capable of generating value for themselves. However, “successful organizations will increasingly move from hierarchical structures to more collaborative and networked models” (SCHWAB, 2016, p.65). In this context the approaches in this work are made, with the objective of presenting the industry scenario 4.0, as well as the skills and competences for the production engineering professional that will act in this new world scenario.

THEORETICAL BACKGROUND Effectively started in the second decade of the 21st century, more precisely between the years 2013 and 2016, the fourth industrial revolution is characterized by the convergence of important levels of sensing, control and artificial intelligence driven by globally established mass communication and intercommunication requirements (STEVAN JUNIOR; LEME & SANTOS, 2018). However, according to Schwab (2016), the fourth industrial revolution is not just about connected systems and intelligent machines, because waves of new discoveries occur simultaneously in areas ranging from gene sequencing to nanotechnology, from renewable energies to quantum computing. “What makes the fourth industrial revolution fundamentally different from the previous ones is the merging of these technologies into the interaction between physical, digital, and biological domains” (SCHWAB, 2016, p.16).

Industrial Revolutions: Historical Evolution To arrive at the fourth industrial revolution, radical and abrupt changes occurred throughout history. According to Assad Neto et al. (2017) and Schwab (2016), the first industrial revolution occurred between 1712

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1760 and 1840, provoked by the construction of highways and the advent of the steam engine, beginning the mechanical production; the second industrial revolution occurred between the late nineteenth and early twentieth century, with the invention of electricity and the creation of the assembly line, enabling mass production; the third industrial revolution began in the 1960s, characterized by the use of electronics and information technology to obtain greater automation in manufacturing processes, it is also often called the digital revolution, because it is driven by the development of semiconductors, mainframe computing (1960s), personal computing (1970s and 1980s) and the internet (1990s), as shown in Table 1. Table 1. The 4th industrial revolutions Period

Revolution

Description

Late 18th century

1st Industrial Revolution (Industry 1.0)

The first Industrial Revolution began in Britain in the late eighteenth century and ended in the mid-nineteenth century. It represented the shift from an agrarian, craft-based economy to an industry-led and machine-manufacturing economy with the introduction of mechanical production methods and the application of steam energy.

Early 20th century

2nd Industrial Revolution (Industry 2.0)

The second period began in the early twentieth century. It was marked by the era of mass industrial production, in which principles of the assembly line were focused on the creation of mass consumer products. The introduction of electric power aided the set of changes.

From the 1960s

3rd Industrial Revolution (Industry 3.0)

The third revolution started in the 60’s is marked by the automation of production processes with the deployment of electronic products and IT in industrial processes.

4th Industrial Revolution (Industry 4.0)

It began at the turn of the century, and is based on the digital revolution, characterized by a more ubiquitous and mobile Internet, smaller and more powerful sensors, artificial intelligence and machine learning. Over the next decade, the fourth industrial revolution will usher in the era of decentralized production. The use of sensor technology, interconnectivity, and data analysis will allow the merging of real and virtual worlds into production.

Nowadays

Source: Prepared by the authors, adapted from Pinheiro & Gargaglione & Gonçalves (2017), Assad Neto et al. (2017) e Schwab (2016)

According to Schwab (2016: 114), “the fourth industrial revolution can robotize mankind and thus compromise our traditional sources of meaning - work, community, family and identity. Or, then, we can use the fourth industrial revolution to elevate humanity to a new collective and moral consciousness based on a common sense of destiny. It is up to all of us to ensure that this latter scenario occurs. “ For Assad Neto et al. (2017), the High-Tech Strategy 2020 technological development plan was launched in 2010, aimed at strengthening the partnership between industry and science, as well as improving the conditions for technological innovation in the country. Germany has been at the forefront of the development of Industry 4.0 (I4.0), being the first country to introduce it, transforming its manufacturing processes to increase efficiency, ensure resource conservation, flexibility and competitiveness (YANAI et al., 2017).

Industry 4.0: Fourth Industrial Revolution According to Stevan Junior, Leme & Santos (2018), an industry is considered to be extremely efficient when all processes occur without failure or waste of materials, focusing on the constant reduction of

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production costs, as well as when processes involve development of communication between sensors and equipment, making them more agile and reliable. For Schwab (2016), society is aboard a technological revolution that will fundamentally transform the way people are living, working, or relating, for to him, transformation will be different from anything that the human being has experienced before. According to Stevan Junior, Leme & Santos (2018, p.137): For many, Industry 4.0 is a new model of management and organization of the manufacturing industry, possible from the development of automation and data exchange technologies. It is considered the manufacturing system of the future.Connectivity between people and systems provides a more expressive and functional level of integration. Thus, Industry 4.0 facilitates the vision and possibility of an intelligent factory, with a high level of modularity, flexibility and decentralized decision-making. Integration and communication between devices promote better operation, virtualization, decentralization, service orientation, modularity, real-time monitoring, flexibility and optimization of the manufacturing system. Thus, Industry 4.0 represents a set of technological advances that provide intelligent networks, where machines and products in process interact without the need for human intervention, allowing the exchange of information instantly between the company’s units, being able to optimize and improve the decisions along the supply chain, as well as to enable a combination of custom and mass production (FUKUDA; MARIZ & MESQUITA, 2017). According to Stevan Junior, Leme and Santos (2018: 81), “The Fourth Industrial Revolution is driven by connectivity trends, advanced materials that enable sensor development, faster processing technology, advanced production networks, device networks of manufacturing and controlled by computers, allowing an interaction between the real and the virtual in a much more integrated way. “However, according to Pinheiro & Gargaglione & Gonçalves (2017) and Yanai et al. (2017) for the formation of Industry 4.0, four key components are required: • • • •

Cyber ​​Physical Systems (CPS): These are systems that allow the integration of the digital and physical world. They are composed of virtual units for the purpose of controlling physical units; Internet of Things (IoT): Network of objects, systems and platforms. It can be described as the relationship between things (products, services, places) and people, through various platforms and connected technologies; Internet of Services (IoS): When Internet of Things works properly, it is possible to identify opportunities and introduce new services. Is the provision of services offered via the internet; Smart Factories: In these factories CPS is used, where consumers, devices and systems form a dynamic production network, intelligently organized, generating tangible gains in efficiency, time and cost when compared to a traditional factory. There is communication between all sectors, working together, exchanging data at all times through the network.

According to CNI (2016), the key enabling technologies behind I4.0 include the Internet of Things, Big Data, Cloud Computing, Advanced Robotics, Artificial Intelligence, New Materials, and New Technology additive manufacturing (3D printing) and hybrid manufacturing (additive and machining functions on the same machine). However, in Brazil there is a certain delay in relation to Industry 4.0, since experts say that the national industry is in transit between industry 2.0 and industry 3.0, differing only in the automotive 1714

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sector, which has strong tendencies to be the pioneer to receive Industry 4.0 due to the high number of qualified professionals and the possibility to work in key sectors in the country. The major challenge for Brazil will be to focus forces on key factors for the development of this new level. Innovative strategic policies are needed, with incentives, availability of new technologies and the development of qualified professionals to work in this new scenario (PINHEIRO; GARGAGLIONE & GONÇALVES, 2017). Figure 1 shows the transition of Brazilian industry. Figure 1. Transition of Brazilian industry

Source: Prepared by the authors, adapted from Stevan Junior, Leme & Santos (2018, p. 137)

For Schwab (2016), the main effects that the fourth industrial revolution has on the business of all industries are: • • • •

Changes in customer expectations; Product enhancements are being made possible through easy access to data, improving productivity; Formation of new partnerships, as companies learn the importance of new forms of collaboration; and Transformation of operational models into new digital models.

In this new scenario, through the cyber-physical systems, the internet of things and the internet of services, the productive processes tend to become increasingly efficient, autonomous and customizable, with better synchronization of demands, enabling direct contact and in time with the chain of suppliers

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and customers. With this, production increases or decreases from the set of data and information received / transmitted, causing productivity to increase and operating costs gradually decrease (PINHEIRO; GARGAGLIONE & GONÇALVES, 2017). Still according to the authors, with this process of Industry 4.0, automation brings several benefits in the execution of the works, such as: • • •

Replacement of human capital in tasks that can put the physical integrity of people at risk, such as handling heavy or hot loads, nuclear power plants, furnaces; Increased economic development, since there is an increase in productivity and efficiency in the processes; Faster performance of tasks, with lower incidence and probability of errors.

According to CNI (2016: 17), “McKinsey estimates that, by 2025, processes related to Industry 4.0 could reduce equipment maintenance costs by 10-40%, reduce energy consumption by 10-20% and increase work efficiency by between 10% and 25% “. In this new scenario, the constant exchange of information between different people and companies will be increasingly common, generating new impacts and new issues regarding reliability, risks, sustainability, safety and know-how (STEVAN JUNIOR; LEME & SANTOS, 2018). Thus, in order to effectively implement and implement the I4.0 concepts, workers will have to combine know-how related to robotics and information technology, be open to changes and adaptations, as well as having multiple skills, flexibility and adaptability. new rules and work environments, and continuous interdisciplinary learning (VOLPE et al., 2017).

Skills and Competencies for Industry 4.0 According to Perasso (2016), David Ritter, CEO of Greenpeace Australia / Pacific said in a column on the fourth industrial revolution for the British newspaper The Guardian “The future of employment will be made by vacancies that do not exist in industries that use technologies new, in planetary conditions that no human being has ever experienced. “ For Volpe et al. (2017), based on a research carried out, the profile of the I4.0 professional should be developed and guided by the requirements of technical, multidisciplinary vision, sense of collaboration, language proficiency, critical sense and flexibility. However, to leverage the technologies adopted in I4.0, it is important to select employees with a high cognitive capacity to understand and use the adopted systems, as well as the ability to follow instructions to help learn the technology about the systems adopted. Thus, the concepts introduced by Industry 4.0 in production management processes have triggered changes in the skills and competencies required of employees as well as of people managers in the manufacturing processes, highlighting five competencies as essential for educational activities of employers and employees: communication and collaboration, creativity, self-management, problem-solving thinking and constant learning (VOLPE et al., 2017). The Industry 4.0 professional needs to improve on platforms essential to the industrial model and should learn how to handle software and specific programs such as 3D modeling software, information management and system optimization (VOLPE et al., 2017). For Schwab (2016), based on a survey of human resources managers of the largest employers in 10 industries and 15 countries on the impact on employment, work and skills by the year 2020, it was noted 1716

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that the greater demand is for complex problem solving skills, process skills, social and system skills, and less for physical skills or more specific technical skills. Therefore, according to Volpe et al. (2017), the profile of the new professional should prioritize characteristics such as: critical sense, use of new media, social intelligence, flexibility, abstraction ability (understanding and interpreting concepts and data), cross-cultural competence (knowing how to relate to people of different cultures and countries), interdisciplinarity (knowing how to work in multidisciplinary and global teams), collaboration at a distance (creating both online and virtual connections) and prioritization (ability to filter, retain and take advantage of only what is important). In this way, it is necessary to have knowledge and talent, as well as to develop personal skills and competences so that the market is very promising for the professional in Industry 4.0, aiming at clear and integrated communication so that transparency and accuracy are fundamental throughout the process.

Concept, Design, Implement, Operate: CDIO The CDIO™ Initiative (www.cdio.org) is an innovative educational framework for producing the next generation of engineers and consists of more than 120 schools around the world organized into 7 regions; Europe, North America, Asia, UK-Ireland, Latin America, Australia, New Zealand and Africa. These are led by one or several regional leaders appointed by each region. The CDIO™ Initiative adopted 12 standards to describe CDIO programs. These guiding principles were developed in response to program leaders, alumni, and industry partners who wanted to know how they would recognize CDIO programs and their graduates. The main role of these 12 CDIO Standards is to serve as a guideline for educational program reform and evaluation, create benchmarks and goals with worldwide application, and provide a framework for continuous improvement. There is no formal certification as a CDIO Program; each institution/institutional department self-certifies using the CDIO Standards and demonstrates certification to its normal accrediting agency or organization. The CDIO Standards allow other academics and industry to identify clearly CDIO Programs and their graduates. The 12 CDIO Standards: • • • •

Define the distinguishing features of a CDIO program; Serve as guidelines for educational program reform and evaluation; Create benchmarks and goals with worldwide application; and Provide a framework for continuous improvement.

The 12 CDIO Standards address program philosophy (Standard 1), curriculum development (Standards 2, 3 and 4), design-build experiences and workspaces (Standards 5 and 6), new methods of teaching and learning (Standards 7 and 8), faculty development (Standards 9 and 10), and assessment and evaluation (Standards 11 and 12).

Methodology As the term Industry 4.0 is quite recent, there is a certain difficulty in obtaining many references on the subject. However, this work also aims to contribute to make the subject more and more widespread, understood and shared by society in general. 1717

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Thus, the search for knowledge for the elaboration of this document was done using the bibliographic research, as well as the case study to present the results obtained. For Cervo, Bervian & Silva (2007: 60), “the bibliographical research seeks to explain a problem from theoretical references published in articles, books, dissertations and theses,” and the objective is to know and analyze cultural contributions, scientific and technical information on a particular subject, theme or problem, so that a new work can be developed, regardless of how it is used. According to Martins (2014: 141), a case study is defined as “an empirical investigation that investigates a contemporary phenomenon within its real-life context, especially when the boundaries between phenomenon and context are not clearly defined.” Thus, the context and the objectives of this research were initially defined, to carry out the bibliographical survey and the case study, in order to do the work elaboration. For that, the researches were carried out in books and articles related to the subject in question so that the theoretical reference was based on studies and research previously carried out by other authors.

Case Study The Centro Universitário Toledo Araçatuba - SP, UNITOLEDO, has more than 50 years of tradition in higher education, working in several areas such as education, health, business and engineering, both undergraduate and postgraduate. In the area of ​​engineering the institution has the courses of Civil Engineering, Mechanical Engineering, Production Engineering, Electrical Engineering, Chemical Engineering With the objective of aligning the growing needs of the labor market, among them, the concept of Industry 4.0, the institution has been working some actions in the scope of engineering teaching, based on the CDIO initiative: •

•

• • • •

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Integrating Projects: it was instituted the realization of an integrative project from the first semester of the engineering courses until the eighth semester, with the objective of integrating concepts and practices of the disciplines of each semester. The CDIO standards developed in the integrative projects can be seen in Table 2. Laboratory of Modeling and Simulation of Productive Systems: conceived with the objective of allowing the modeling and simulation of productive systems, aiming at the identification and solution of real problems found in the industrial environment. In this lab the available resources are ProModel software, the Lean Board Game and business management software. Laboratory of Robotics and Industrial Automation: designed with the objective of allowing the use of robotics in real issues related to industrial automation. Groups and Lines of Research: scientific research is being organized in undergraduate courses with topics related to the real needs of companies. Through research projects and scientific initiation projects. Active Learning Methodologies: use of active learning methodologies such as Project Based Learning, Problem Based Learning in disciplines and also in integrative projects. Introduction to Engineering: in the first two semesters of engineering courses the Introduction to Engineering I and II discipline is an introductory course that provides a framework for the practice of engineering in the elaboration of products, processes and systems construction, in addition to introduce the personal and interpersonal skills essential to professional practice.

 The New Challenges in the Training of the Engineer for the Industry 4.0

•

Partnerships with Institutions: some activities are carried out in partnership with institutions that work directly with industry issues, such as SENAI, where the exchange of experiences and activities together complements the student’s training. Another partnership is with CIESP aimed at aligning the skills needed for the future professional.

Table 2. CDIO standards developed in the integrator project CDIO Standards

Description

Note

2. Learning Outcomes

Specific, detailed learning outcomes for personal and interpersonal skills, and product, process, and system building skills, as well as disciplinary knowledge, consistent with program goals and validated by program stakeholders

We identified the personal and interpersonal skills to be developed throughout the project, as well as the skills of building products, processes and systems.

3.Integrated Curriculum

A curriculum designed with mutually supporting disciplinary courses, with an explicit plan to integrate personal and interpersonal skills, and product, process, and system building skills

The course subjects allow integration for the development of multidisciplinary projects.

5.Design-Implement Experiences

A curriculum that includes two or more designimplement experiences, including one at a basic level and one at an advanced level

The disciplines allow each semester to elaborate integrative projects with the theme of development of new products, processes or systems.

6.Engineering Workspaces

Engineering workspaces and laboratories that support and encourage hands-on learning of product, process, and system building, disciplinary knowledge, and social learning

The institution’s laboratories used in the project allow the production of products from several industrial segments.

7. Integrated Learning Experiences

Integrated learning experiences that lead to the acquisition of disciplinary knowledge, as well as personal and interpersonal skills, and product, process, and system building skills

The integration with another course of the institution throughout the integrating project made possible the exchange of information and experiences that contributed to the development of established competencies.

8.Active Learning

Teaching and learning based on active experiential learning methods

The use of Project Based Learning enabled the use of active learning methodologies including students at the center of the teaching and learning process.

11.Learning Assessment

Assessment of student learning in personal and interpersonal skills, and product, process, and system building skills, as well as in disciplinary knowledge

Data collection before and after the project made it possible to evaluate the evolution of the students and also of the learning process.

CONCLUSION The changes brought about by new technologies increasingly demand that higher education institutions seek alternatives for a complete training of the future engineer, aligning the expectations and needs of the labor market. The development of personal and interpersonal skills is also necessary in this scenario, in addition to the knowledge and technical skills required for the professional in training. The actions carried out by the Centro Universitário Toledo Araçatuba - UNITOLEDO demonstrate an alignment with the new training needs of the engineer and that Industry 4.0 requires the professional that will work. The requirements of Industry 4.0 presented in the text allow the identification of technical and behavioral competencies that the actions carried out by the institution seek to apply in the process of teaching and learning.

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REFERENCES ACATECH, National Academy of Science and Engineering. (2013). Recommendations for implementing the strategic initiative INDUSTRIE 4.0. Disponível em: http://www.acatech.de/fileadmin/user_upload/ Baumstruktur_nach_Website/Acatech/root/de/Material_fuer_Sonderseiten/Industrie_4.0/Final_report__Industrie_4.0_accessible.pdf Alvarenga, N. E. T. O. (2008). Rivadávia Correa Drummond de. Gestão de conhecimento nas organizações: proposta de mapeamento conceitual integrativo. São Paulo: Saraiva. Assad, N. E. T. O. (2017). A busca de uma identidade para a indústria 4.0. In XXXVII Encontro Nacional de Engenharia de Produção, 2017. Joinville/SC: Enegep. Cervo, Bervian, & Silva. (2007). Metodologia científica (6th ed.). São Paulo: Pearson Prentice Hall. CNI. (2016). Confederação Nacional da Indústria. Desafios para a Indústria 4.0 no Brasil. Brasília: CNI. Fukuda, Mariz, & Mesquita. (2017). Impactos da Indústria 4.0 na gestão de operações. In XXXVII Encontro Nacional de Engenharia de Produção, 2017. Joinville/SC: Enegep. Junior, Leme, & Santos. (2018). Indústria 4.0: fundamentos, perspectivas e aplicações. São Paulo: Érica. Martins. (2014). Guia para elaboração de monografia e TCC em engenharia de produção. São Paulo: Atlas. Pádua, . (2016). Inovação é tudo. São Paulo: Atlas. Perasso. (2018). O que é a 4ª revolução industrial - e como ela deve afetar nossas vidas. BBC News Brasil. Disponível em: https://www.bbc.com/portuguese/geral-37658309 Pinheiro. (2017). Indústria 4.0: uma análise conceitual do tema, seus impactos na economia e a colocação do profissional neste novo cenário. In XXXVII Encontro Nacional de Engenharia de Produção, 2017. Joinville/SC: Enegep. Scherer. (2016). Gestão da inovação na prática: como aplicar conceitos e ferramentas para alavancar a inovação. 2. São Paulo: Atlas. Volpe. (2017). Habilidades e competências do profissional para o ambiente da Indústria 4.0: uma revisão sistemática. In XXXVII Encontro Nacional de Engenharia de Produção, 2017. Joinville/SC: Enegep. Yanai. (2017). O desenvolvimento da Indústria 4.0: um estudo bibliométrico. In XXXVII Encontro Nacional de Engenharia de Produção, 2017. Joinville/SC: Enegep.

This research was previously published in Redesigning Higher Education Initiatives for Industry 4.0; pages 326-335, copyright year 2019 by Information Science Reference (an imprint of IGI Global).

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Employer’s Role Performance Towards Employees’ Satisfaction:

A Study of SME Industry 4.0 in Malaysia Siti Noorjannah Abd Halim Universiti Sains Malaysia, Malaysia Siti Noorhaslina Abd Halim Universiti Teknologi MARA, Malaysia

ABSTRACT The wave of the Fourth Industrial Revolution (IR4.0) is a phenomenon in which one or more technologies are replaced by another technology in a short amount of time. In small and medium-sized enterprises (SMEs), some internal and external problem are occurring that suggest change from classical to technological approach. Thus, this chapter aims to establish the relationship between the employees’ satisfaction toward their employer’s role performance. Based on the power-dependence and agency theories, this study contributes to the SMEs industry in Malaysia and will involve IR4.0 by offering a much more comprehensive theoretical perspective to aid understanding and prepare for the revolution internally. The sample of this study comprises of employees who are working in various sectors of the SMEs industry. G-power technique was employed to find the minimum sample size in this study. Meanwhile, the SPSS and PLS will be used to analyse the data. The practical implication of this research concerns the factors that can enhance employee satisfaction if their company jumps into the IR4.0. Thus, the employer should play the right role to make sure the employees are ready and well prepared for the revolution despite there being environmental uncertainty happening in the process.

DOI: 10.4018/978-1-7998-8548-1.ch087

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 Employer’s Role Performance Towards Employees’ Satisfaction

1.0 INTRODUCTION The movement towards the Fourth Industrial Revolution (IR4.0) is rapidly changing in which one or more technologies are replaced by another technology in a certain period of time. The term of “Industry 4.0” is originating from a project in the high-tech strategy which promotes digitalization of manufacturing by the German government (Kowang et al., 2019). This concept applies the current trend of automation and data exchange including cyber-physical systems, the Internet of things, cloud and cognitive computing in manufacturing technologies (Hermann, Pentek, & Otto, 2015). Thus, this on-going technology development requires organization change to keep up with the competition in industry. There would be a problem with Small and Medium-Sized Enterprises (SMEs) to move from classical to technological approach for the changes would bring an impact on the employees whereby they might lose their job because of the digitalisation application in company (Kleindienst & Ramsauer, 2012). Some of these researchers argue with losing job when applying this revolution because by having digitalisation approach, this may help employment rates go up and more advanced (Zambon, Cecchini, Egidi, Saporito, & Colantoni, 2019). The top management should apply the change models when they want to move for IR4.0 in their manufacturing companies. The term industrial 4.0 itself is a well-known revolution phenomenon in today’s business strategy(Ghaz, 2017). This phenomenon will give direct and indirect impact to the labour market in SMEs industry. The labour market here means a place for workers and employees (to) interact with each other(Shanock & Eisenberger, 2006). For instance, employers compete to hire the best employees for their organization, while workers compete for the best satisfying job as they can. Thus, the role performance from employers is very important and become the main role in order to gain satisfaction from employees. The dispersal of IR4.0 within companies or organization depends on their size. For instance, the large companies which are having more resources, processes and more structured tend to deploy IR4.0 technologies more advance as compared to SMEs companies (Woon, Kei, May, Yi, & Mei, 2019). However, SMEs also should be catching up with this revolution from becoming victims of large companies which are having more resources and skills compare to them (Saleh & Ndubisi, 2006). Thus, the employer of SMEs companies should play their role and guide the employees to be prepared for this revolution in terms of competition with the large companies out there. The role performance from the employer becomes important to organization especially to deal with employees. In SMEs, (the) employer really depends on employees in terms of production and services of their companies. Some of the employees cannot understand why their companies have to apply this IR4.0 and keep changing from time to time (Safar, Sopko, Bednar, & Poklemba, 2018). To emphasize, the employer has to explain clearly and show a good behaviour in the way to change into this new revolution (Griffin & Parker, 2007). Thus, the IR4.0 in this study refers to the environmental uncertainty which is known as a moderator in this study. In a way the employer shows a good performance to their employee, the revolution might disrupt the satisfaction from the employees (Luco, Mestre, Henry, Tamayo, & Fontane, 2018). Some of the changes will make the employees feel not satisfied and unease with that. Likewise, adopting a new technology in companies, may take some times for employees to be suit with it especially in SMEs industries. With limited numbers of resources and employees, the employer should strengthen their role to encourage their employees in these changes (Fauziah, Yusoff, Jia, Azizan, & Ramin, 2013). Thus, environmental uncertainty might strengthen or weaken the relationship between role performance or employer and employees’ satisfaction in this IR4.0. 1722

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2.0 BACKGROUND OF THE STUDY This study will look at the challenges and strategies for SMEs towards the IR4.0. In manufacturing companies, changes are most of the time happening drastically both in internal and external environment. This new revolution is characterized by an unknown level of talent and skill mobility, then the employees need to wide their potential to make sure their talent align with current revolution (Harrison, Kelly, Harrison, & Kelly, 2010). This trend makes a major transformation that involved almost every aspect of the business (Ostdick, 2017). The transformation would motivate the SMEs to make a move and keep competitive advantages in their industry field. For instance, employees should play their roles very well in order to help their company toward to IR4.0. There are two organizational change models that dominated the literature, that of planned and emergent change (Kidwell, Nygaard, & Silkoset, 2007). The planned model is applied when the operation of an organization is in stable and predictable environment where changes are driven by the top-down approach (Nkundabanyanga, Balunywa, Tauringana, Ntayi, & Stephen, 2014). This model assumes that structures, processes, technology and human skills, capabilities and knowledge can be changed due to successful organization. On the other hand, the emergent change model is applied when the organization operates in an unpredictable and complex environment that driven by a bottom-up approach (Louis, 1986). The IR4.0 is not a joke of SMEs especially in technology savvy that they must get in it. The era of globalization where opportunities are abundance creates chances for firms to apply the new technology (Schröder, 2017). SMEs are required to aggressively venture into their backyard and accept the IR4.0 challenges. This opportunity will remain them in the industry and still competitive among others. SMEs need to put their efforts to strategize their capabilities to accept the IR4.0 to respond to the global competition and business chances (Radanliev et al., 2017). As SMEs accept and apply new technology, their employees also have to be ready physically and mentally. For the SMEs, their firms operating and engaging in international business especially have no choice and have to accept IR4.0 by keep sustainable competitive advantages (Umrani & Johl, 2018). SME’s employee plays an important role in order to get successful in IR4.0. In the time, a firm decided for IR4.0, the employees must follow and try to make it happen (Saleh & Ndubisi, 2006). However, it is important to note that development and growth of SMEs arena have substantially contributed to the economic benefits. Thus, IR4.0 can be good or not is depending on the firm to handle it.

3.0 SMEs IN MALAYSIA The history of SMEs in Malaysia can be traced back to the late 1990s with playing an important role in Malaysian economy. During the 1990s, SMEs were more viable compared with large enterprises. However, Malaysia has recognized the importance of SMEs development since early in 1960s. In the mid-1990s, the functions of SMEs in the development of the Malaysian economy became more significant. For instance, SMEs have been at the core of Malaysia’s economic transformation since the 1990s to an upper-middle income nation and is an important driver of employment and growth (Saleh & Ndubisi, 2006). Thus, SMEs industries are significant besides they become the backbone of Malaysia’s economy until now. In line with Malaysia vigour to become a high-income nation by 2020, the country has given a special focus on SMEs industry in Malaysia. By 2020, Malaysia aims to push SMEs’ contribution to GDP to 41% and the share of the country exports from SMEs to 23%. In fact, the government of Malaysia is aware 1723

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of the SMEs’ potential for a way to move for IR4.0. There are many different sectors involved in SMEs activity such as agriculture, mining and quarrying, manufacturing, construction and services. Currently, the main contribution in SMEs activity are services and manufacturing with combined share of 82.4% in 2018. Hence, SMEs activities in Malaysia still relevant especially in Malaysian economic growth.

3.1 Malaysia’s SMEs Performance In Malaysia, SMEs become one of the established business activities that contribute to nation economy growth. Consequently, Malaysia has attracted much attention internationally by recoding an extraordinary economic performance. Malaysia’s 2012-2020 SMEs Masterplan seeks to advance SMEs development and increase their contributions to the economy. The key element of Malaysia Masterplan is to focus on innovation and technology adaptation. It is aligning with the new industry revolution 4.0 that require SMEs to enhance their innovation especially in technology adaptation. Overall performance of SMEs still on track to achieve the target of 41% to GDP by 2020 as envisaged under the Dasar Keusahawanan Nasional 2030 (DKN 2030) recently. In addition, based on the Department of Statistics Malaysia (DOSM), Malaysia’s SMEs recorded a higher contribution of 38.3& to GDP in 2018 amounting to RM521.7 billion as compared to 37.8 % amounting to RM491.2 billion in 2017. Thus, it shows that SMEs in Malaysia (are) still relevant and competitive advantage to Malaysia’s economy growth.

4.0 PROBLEM STATEMENT There are conflicting ideas about SMEs in IR4.0 between the employer and employees in their organization. This revolution disrupts structures of labour markets such as low-skilled and mid-skilled jobs may become vulnerable due to mechanisation of process, systems and people-oriented work (Safar et al., 2018). Role performance from employers is very important via it influences the behaviour of employees towards them (Raemy et al., 2018). For instance, when this revolution makes a change especially from human workforce to technology adaptive, it can be frustrated for employees. Since the early years, Malaysian government has put an attention and gave priority to develop the capabilities of SMEs to be more competitive in IR4.0. To energize SMEs towards IR4.0, they need to recognize the on-going technology transformation and rise on challenge of having the capability and willing to change it (Zambon et al., 2019). SMEs can get competitive advantage by offering high quality products at low price in the industry 4.0 era (Schröder, 2017). Thus, some employers do not play their role very well especially to achieve employees’ satisfaction. In fact, an early challenge to SMEs to changes from previous revolution to current revolution is lack of comprehensive strategy (Szamosi, 2002). It is because most of the business does not give their attention on digitalisation journey (Woon et al., 2019). Hence, it is limited to SMEs to adapt to current revolution and for routine application. Employees in SMEs industry are worry beucase of negative impact of automation on their job where most of them will lose their job in company (Ristuccia, 2019). It will occur as the technology such as arising out of machinery used as major systems in organization. For instance, if the company wants to change from human workforce to machine oriented, the employer should play their role by advising the employees properly (Kleindienst & Ramsauer, 2012). Thus, when SME industry makes major changes in the highest technology level, it will give priority to the cyber-physical systems to make decision on 1724

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their own without the need for the human. The reality is that, majority of Malaysian SME has been in the comfort zone for too long and is not ready to compete locally and internationally. In fact, if the IR4.0 arises, they feel that they cannot accept the challenges and not ready for a change especially for those who are already many years in this industry (Woon et al., 2019).

5.0 PURPOSE OF THE STUDY The purpose of the study is to review the literature and subsequently propose a model linking role performance and satisfaction with moderating effect of environmental uncertainty among SME industry employees in Malaysia. From the academic perspective is to review current literature which evaluates whether the role performance can build satisfaction by having the environmental uncertainty of the business. In addition, this study provides useful information about IR4.0 and how employers play their role to satisfy their employees during this industrial revolution changes. Employer should be interested to know the employees’ satisfaction with their role performance has significant relations payback (Yin & Qin, 2019). For instance, before turn to IR4.0, employer can consider back on that technology by taking consideration of their employees.

5.1 Research Questions The research questions of this study are as below: Does role performance positively and significantly affect satisfaction? Does the impact of role performance on satisfaction is higher with lower levels of environmental uncertainty than with higher levels of environmental uncertainty?

6.0 LITERATURE REVIEW 6.1 Power Dependency Theory It is a simple theory of power relations that had developed to resolve some ambiguities surrounding “power”, “authority”, “legitimacy”, and power “structures” that bring them together in a coherent scheme (Emerson, 2014). This theory analysed about the dependency on the power between two parties or partnerships and the resources that brings into the relationship (Berthon, Pitt, Ewing, & Bakkeland, 2003). This theory also stated that one party will depend on the other party based on that party has power and doing well (Davis & Cobb, 2010). Whenever the employees believe on their employer, they will depend on their employer especially in business strategy. At the same time, effective role performance influences the satisfaction very well. Thus, employees need to make sure that the quality is following the standard given. Dependency Theory can be defined to explain the development of economic in terms of external influences such as political, economic and cultural. There are some characteristics as the dependency theory which characterize on the international system that described the dominant and dependent. According

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to the Pfeffer (1993), there are some advantages for the members given that there is some expertise, easy to excess the resources, and the legitimacy.

6.2 Agency Theory Agency theory is a principle that used to describe and resolve issues especially in relationship between business principals and agents (Lafontaine, 1992). According to Kidwell, Nygaard, and Silkoset (2007), most commonly the relationship involved in stakeholders are the shareholder as a principal and company executive as an agent. Thus, the principal refers to employers and agent would be employees. Traditionally, Agency Theory has developed along two streams which are positivist and principleagent (Jensen & Jensen, 1983) both streams share a common unit of analysis (principle and agent). Both positivists and principle-agent stream also share common assumptions about people, organizations, and information. However, they differ from their mathematical rigor, dependent variable, and style. Table 1 shows the Agency theory overview of a variety of settings. Table 1. Agency theory overview in variety of settings Terms

Explanations

Key idea

Principal-agent relationship should reflect efficient organization of information and risk-bearing costs.

Unit of analysis

Contract between principal and agent

Human assumptions

Self-interest Bounded rationally Risk aversion

Organizational assumptions

Partial goal conflict among participants Efficiency as the effectiveness criterion Information asymmetry between principal and agent

Information assumption

Information as a purchasable commodity

Contracting problems

Agency (moral hazard and adverse selection) Risk sharing

Problem domain

Relationships in which the principal and agent have partly differing goals and risk preferences (e.g., compensation, regulation, leadership, impression management, whistle-blowing, vertical integration, transfer pricing)

Sources: Eisenhardt (1985)

There are two agency problems that arise from this context which is horizontal and vertical problems (Combs, Michael, & Castrogiovanni, 2004). The horizontal agency problem concerns about the brand and quality of the organization but for vertical agency problems more towards on employees’ satisfaction. The employer consistently has to deal with employees with varying degrees of risk tolerance. Thus, employers should play their role as employees amazingly.

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6.3 Role Performance The role of performance consists of the performing tasks which is related to the formal requirement and include the behaviours that are formally required by the company (Buil, Martínez, & Matute, 2018). To measure the role performance needs to include the factor analyses self-report. As an employer, the role that they play towards the employees is very important especially to maintain a good relationship between them (Raemy et al., 2018). In SMEs, the employer has to deal with the employees due to limited numbers of employees. Thus, every person in organization has to play their role in order to maintain the success of the business. According to Harmon & Griffiths (2008), the role performance is described such as “the total set of performance responsibilities played by the right person for the right position”. It is difficult to describe a specific role in organization due to each organization may split or divide the role according to their needs (Ouedraogo & Ouakouak, 2018). For instance, there are individual task behaviour, team member behaviours and organization member behaviour. Thus, employer in SME might have different role compare to the employer at large companies.

6.4 Satisfaction Satisfaction has been investigated as a positive affective behaviour resulting from the expectation being met (Dickey, Harrison McKnight, & George, 2008). The concept of satisfaction is referred to as “an overall affective orientation on the part of individuals regarding their work roles that they have currently” (Valaei & Rezaei, 2016). In SME business relationships, satisfaction is considered as a guiding philosophy that is an important factor of the success of the business (Altinay, Brookes, Yeung, & Aktas, 2014). The level of employees’ satisfaction is based on the partnership period and experience in their employers. Thus, in SME sector, employees satisfaction is defined as the positive perception from the employers based on good economic return and psychological expectation in captive the IR4.0. Employee will satisfy if they can achieve firm’s requirement especially on their work performance. Despite they need to learn new things and change their mind-set for IR4.0, they still do it well as good as they can. In this IR4.0 situation, they do not have any choice for that. Researchers commonly find that satisfaction is a strong predictor of behavioral outcomes, such as loyalty (Ravald and Gro¨nroos, 1996; Wang et al., 2006; Kassim and Abdullah, 2010). Employees with higher levels of satisfaction are more likely to remain in the firm and contribute more to the relationship (Robinson, 1997). Clark and Oswald (1995), describes satisfaction as a “self-reported positive emotional state resulting from the appraisal of one’s job or from job experiences”. Based on his review of empirical studies on satisfaction, Locke determined that seven work issues are typically associated with job satisfaction including mentally challenging work, personal interest in the specific job, work that is not too physically tiring, perceived equitable rewards, appropriate working conditions, employee self-esteem, management assistance in managing the workplace by minimizing conflict and ensuring that work is interesting and good pay or promotions are available.

6.5 Environmental Uncertainty Environmental uncertainty mean changes in the business environment that influence the development of company by having unexpected issues (H. Zhang & Lv, 2015). It arises out of contingencies that 1727

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describe the context of economic exchange become difficult to predict and cannot be specified in the contract (Geyskens & Steenkamp, 2006). More importantly, this environmental uncertainty is about how well the companies adapt to external environmental changes in their internal relationship especially for SMEs system (Michael, Harrison, & Joey, 2007). Previous study indicated that environmental uncertainty influences the development of company by having unexpected issues (K. Z. K. Zhang et al., 2015). Usually, environmental uncertainty has a negative effect on the allocation of decision rights to employees due to the employer exercises more control over decisions (Mumdziev & Windsperger, 2013). Therefore, the present study presents an extended employees’ satisfaction from the role performance shown by their employer towards the IR4.0. Environmental uncertainty refers to the firm external environment in terms of technology, consumer tastes and preferences and competitors’ actions; it is characterized by an absence of patterns, unpredictability, and unexpected change (Wang, Chueh, Lee, Wang, & Lee, 2013). Such environmental uncertainty required both parties to develop the capability to understand and adapt to environment. Thus, employees as a part of the business need to adapt and face any of the changes either it will give profit of not for their business due to revolution changes (Didonet et al., 2012).

6.6 Research Framework Figure 1. Research Framework

7.0 PROPOSITIONS DEVELOPMENT 7.1 Relationship between Role Performance and Satisfaction Related to the challenges for SME in IR4.0, employers should consider their employees for future planning (Umrani & Johl, 2018). This could be happening once employers change the human workforce to the technology savvy in their business. According to (Szamosi, 2002) one of the common issues existed in the relationship between employers and employees once the SME applied the industrial 4.0 in their company. The past study stated that employers should know employees’ needs and desire before involv-

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ing in the revolution (Luco et al., 2018). However, the company also have to look on their company sustainability and grow because it is important to remain competitive in the industry. It is important to build the role of employers due to increasing the employees’ satisfaction level (Harmon & Griffiths, 2008). This role performance consists of the performing tasks which related to the formal requirement and include the behaviours that are formally required by the company (Kowang et al., 2019). In fact, the IR4.0, employees need a very specific attention and guidance from the employer to make it happen. Thus, role performance from the employer is very important to make sure the employees are satisfied with guidance from the employer. Proposition 1: There is a significant and positive relationship between role performance and satisfaction.

7.2 Environmental Uncertainty as a Moderator between Role Performance and Satisfaction Employer plays an important role in their SME business especially their relationship with employees (Davies, Lassar, Manolis, Prince, & Winsor, 2011). Based on organizational set-up, the changes in external environment also need to be considered. (Hence), when the company decided on applying IR4.0, the employer and employees will have to work together and make sure to get competitive advantage in the market (Zambon et al., 2019). Thus, both parties have to be alert to changes in the business environment that will affect them. The effectiveness of cooperative strategy of the partners depends on the environmental uncertainty (Stehouwer, 2014). Therefore, the employers will be alert to the threat to their business at the same time will give more attention to their employees. Proposition 2: The impact of role performance on satisfaction is higher with lower levels of environmental uncertainty than with higher levels of environmental uncertainty.

8.0 RESEARCH METHODOLOGY This section will discuss the methods and procedures used in this research. The research approach in this study is quantitative research that attempts to collect facts and data to examine the relationship between independent and dependent variables and analysed it by using statistical method (Zikmund, 2010).

8.1 Research Design This is a cross-sectional study, with all variables and data gathered over a set period of time in order to answer the research questions. Data collection will be collected from SME’s employees in Malaysia. In this study, all variables will be measured at an individual level. The data will be collected by using survey questionnaire (quantitative study) and the respondents belonged to the SME industry in Malaysia.

8.2 Population and Sampling The population of this study is individual SME’s employees in Malaysia with focusing on Selangor, Kuala Lumpur, Perak and Penang. The chosen states based on the most SME landed in Malaysia. The 1729

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reliability test will be run to know the internal reliability of items in the questionnaires based on Cronbach’s Alpha value (values). Then, correlation coefficient and regression analysis will be used to check relationship between variables. The present research will utilise non-probability sampling to obtain information about those who are most readily (from selected location) available. Purposive sampling is used due to the SME’s employees are the only ones who can respond to the survey, or they can conform to some criteria set by the researcher.

8.3 Data Collection Method This study will adopt the primary data collection method by using survey questionnaire to respondent in SME industry in Malaysia. This method is pre formulated written sets of questions to which respondents record their answers and generally designed to collect large numbers of quantitative data (Sekaran & Bougie, 2016). The questionnaire will be collected from the respondents by personally administered questionnaires through drop off and collect method. For the personally administered questionnaires, the researcher will drop-off the questionnaires to the respondents and collect it after two weeks.

8.4 Measurement Scales This section consists of all variables that are discussed on the items and scales of measurement in detail. There are independent, dependent and moderator variables’ items.

8.4.1 Role Performance Role performance instrument is developed by(Shanock & Eisenberger, 2006) and Lynch et al. (1999) and consists of 11-items. This instrument is scored using a 5-Likert scale (Likert, 1932) ranging from 1=strongly disagree, 2=disagree, 3=neutral, 4=agree and 5=strongly agree. The purpose of this instrument is to investigate role performance measured the in-role performance and extra role performance obtained by the employees from their employer.

8.4.2 Satisfaction A scale developed by Andaleeb (1995) and (Dwyer, 1987) having 6 items was adopted and adapted to measure satisfaction construct. The 5-Likert scale (Likert, 1932), used as an instrument to score this satisfaction construct ranging from 1=strongly disagree, 2=disagree, 3=neutral, 4=agree and 5=strongly agree. The purpose of this instrument is to investigate satisfaction from employees’ perspective towards their employer. The factor loading for these items are statically significant (p < 0.05) and the Cronbach’s alpha for this constructs; satisfaction by Andaleeb, α = 0.71 and for Dwyer and Oh, α=0.779.

8.4.3 Environmental Uncertainty Environment Uncertainty instrument is developed by Duncan (1972) and (Bourgeois, Daniel, & Terence, 1978) and consists of 5-items and is scored using a 5-Likert scale (Likert, 1932) ranging from 1= never predictable, 2= unpredictable, 3= neutral, 4= predictable, and 5= always predictable. The purpose of this instrument as a moderator for role performance and satisfaction is to strengthen the relationship be1730

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tween these two variables. The factor loading for these items are statically significant (p < 0.05) and the Cronbach’s alpha for this constructs; satisfaction by Duncan, α = 0.70 and for Bourgeois et al., α=0.88.

9.0 CONTRIBUTIONS OF THIS STUDY Based on the preceding review of the literature and consistent effect of employers role performance and employees satisfaction with environmental uncertainty as moderator, a model has been developed as depicted in Figure 2.1. Role performance is viewed as an independent variable that has a direct effect on satisfaction. Besides, environmental uncertainty would be (a) moderator in this model. In terms of theoretical contributions, the current study contributed critically to the theoretical framework related to role performance and satisfaction with the environmental uncertainty as a moderator in SMEs’ activities. In addition, this study also contributes to the extending traditional theory of Power Dependency and Agency Theory. For instance, Power Dependency Theory is to resolve some doubts happen that related to the power of authority in SMEs business. On other hand, the Agency Theory also used in this study to describe and resolve issues regarding unexpected issues arises out of this industry. Moreover, this unexpected problem will affect the role performance of the employees in SMEs industry. The present study hopes to cover a number of key relationship business issues concerning the important of environmental uncertainty of SMEs’ business to several practitioners. In addition, this study will bring ideas holistically to the firms on how environmental uncertainty will strength or weaken the relationship of role performance and satisfaction of SMEs’ employee in their work field. Furthermore, this research will motivate them to enhance their performance while having any unexpected issues or problems in future. Thus, this study will benefit the SMEs to develop in future and get ready with any problem arise.

10.0 CONCLUSIONS There are a rising number of researches in IR4.0. However, most of the study focused on the process of how to be in IR4.0 and the impact on SMEs companies. In addition, there is lack of study that go through the relationship between employer and employee in SME on how they will deal with this revolution if their company decided to do so. Thus, there is a crucial need for future research to study further about employees’ satisfaction with their employers’ role performance when come to deal on IR4.0. This paper contributes to the on-going discussion about the challenges and difficulties for employer in SME industry to maintain their role performance towards their employees. There is role performance acts as independent variable in this study which is chosen based on the behaviour that requires form an employer to employees if they want to apply this revolution in their company. This is also supported by the previous research conducted by Safar et al., (2018), even though there is limited empirical research on the issues, it is possible to study and can contribute for future researches in the same field. Industrial revolution 4.0 is changing in terms of technology and it moves very fast. According to (Conti & Passarella, 2018), SME also should change into technology savvy and make sure that there is still competition in the industry. By addressing environmental uncertainty as the moderator in this study, the employer also has to look on it and make sure employees also understand why company needs for a

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change. Thus, employer with a good role performance towards their employees can help the company move forward and gain more profit in future.

11.0 FUTURE TRENDS Specifically, this study provides new features and benefits to the SMEs Industry 4.0, which this section presents a research plan according to an overarching goal to ensure that the strategic plan for future revolution period (goes well). In future, other new industrial revolution needs to be caught up and SMEs companies also should be ready on that time. Although, most research states that the IR4.0 suits with large companies due to their advanced resources and processes, but as SME companies also should be on that position and ready for changes (Woon et al., 2019). It is important especially to maintain in this global business where everything based on technology savvy used.

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Mumdziev, N., & Windsperger, J. (2013). An Extended Transaction Cost Model of Decision Rights Allocation in Franchising. The Moderating Role of Trust, 182(February), 170–182. Nkundabanyanga, S. K., Balunywa, W., Tauringana, V., Ntayi, J. M., & Stephen, K. (2014). Board role performance in service organisations : the importance of human capital in the context of a developing country. Academic Press. Ouedraogo, N., & Ouakouak, M. L. (2018). Impacts of personal trust, communication, and affective Commitment on Change Success. Journal of Organizational Change, 31(3), 676–696. doi:10.1108/ JOCM-09-2016-0175 Radanliev, P., Roure, D. C. De, Nurse, J. R. C., Burnap, P., Anthi, E., Ani, U., … Montalvo, R. M. (2017). Design principles for cyber risk impact assessment from Internet of Things (IoT). Academic Press. Raemy, P., Beck, D., Hellmueller, L., Raemy, P., Beck, D., & Hellmueller, L. (2018). Swiss Journalists ’ Role Performance: The relationship between conceptualized, narrated, and practiced roles. Journalism Studies, 0(0), 1–18. Ristuccia, C. (2019). Industry 4.0: SMEs Challenges and Opportunities in the Era of Digitalization. Academic Press. Robinson, M. (1997). 1997_Morrison and Robinson_a model of how psychological contract violation develops.pdf. Academic Press. Safar, L., Sopko, J., Bednar, S., & Poklemba, R. (2018). Concept of SME Business Model for Industry 4.0 Environment. Academic Press. Saleh, A. S., & Ndubisi, N. O. (2006). An Evaluation of SME Development in Malaysia. Academic Press. Schröder, C. (2017). The Challenges of Industry 4 . 0 for Small and Medium-sized Enterprises. Academic Press. Shafer, W. E., & Wang, Z. (2010). Effects of ethical context on conflict and commitment among Chinese accountants. Academic Press. Shanock, L. R., & Eisenberger, R. (2006). When Supervisors Feel Supported : Relationships With Subordinates. Perceived Supervisor Support, Perceived Organizational Support, and Performance, 91(3), 689–695. Stehouwer, K. (2014, Dec.). Northwood University Pioneers Undergraduate Major in Franchising Management. Olive Software. Szamosi, L. T. (2002). Just what are tomorrow’s SME employees looking for? Academic Press. Umrani, A. I., & Johl, S. K. (2018). How Different Ownership Structures Perform in Industry 4.0: A Case of Malaysian Manufacturing SMEs. Academic Press. Umrani, A. I., & Johl, S. K. (2020). How Different Ownership Structures Perform in Industry 4.0: A Case of Malaysian Manufacturing SMEs. Academic Press.

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Valaei, N., & Rezaei, S. (2016). Job satisfaction and organizational commitment. Management Research Review, 39(12), 1663–1694. doi:10.1108/MRR-09-2015-0216 Wang, E. T. G., Chueh, N., Lee, A., Wang, E. T. G., & Lee, N. C. (2013). Using power regimes to explore perceived environmental uncertainty. Industrial Management & Data Systems, 113(7), 950–966. doi:10.1108/IMDS-02-2013-0072 Woon, B. C., Kei, C. W., May, L. S., Yi, T. Y., & Mei, T. J. (2019). Reasons Against Audit Exemption Among Sme. Academic Press. Yin, Y., & Qin, S.-F. (2019). A smart performance measurement approach for collaborative design. Advances in Mechanical Engineering, 11(1), 1–15. doi:10.1177/1687814018822570 Zambon, I., Cecchini, M., Egidi, G., Saporito, M. G., & Colantoni, A. (2019). Development for SMEs. Academic Press. Zhang, H., & Lv, S. (2015). Effect of HR practice on NPD performance The moderating role of environmental. Nankai Business Review International, 6(3), 256–280. doi:10.1108/NBRI-03-2015-0008 Zhang, K. Z. K., Benyoucef, M., & Zhao, S. J. (2015). Computers in Human Behavior Consumer participation and gender differences on companies ’ microblogs : A brand attachment process perspective. Computers in Human Behavior, 44, 357–368. doi:10.1016/j.chb.2014.11.068

This research was previously published in Challenges and Opportunities for SMEs in Industry 4.0; pages 140-154, copyright year 2020 by Business Science Reference (an imprint of IGI Global).

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Section 7

Critical Issues and Challenges

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Chapter 88

Analysis of Challenges Responsible for the Slow Pace of Industry 4.0 Diffusion Chetna Chauhan Indian Institute of Management, Rohtak, India Amol Singh Indian Institute of Management, Rohtak, India

ABSTRACT The pace of Industry 4.0 adoption in manufacturing industries has been slow as it is accompanied by several barriers, specifically in the emerging economies. The current study intends to identify and understand the landscape of these challenges. Further, this paper prioritizes the challenges on the basis of their relative importance. To achieve this objective, the authors combine the fuzzy delphi approach along with the fuzzy analytical hierarchy process. Additionally, a sensitivity analysis is done to enhance robustness of the findings. The global rankings of the challenges reveal that the most significant factors that hamper the full realization of smart manufacturing include cybersecurity, privacy risks, and enormously high number of technology choices available in the market. The analysis offers insights into the reasons for the slow diffusion of smart manufacturing systems and the results would assist managers, policymakers, and technology providers in the advent of manufacturing digitalization.

1. INTRODUCTION Industry 4.0, which is the fourth industrial revolution, has become a prominent topic across the global community of academicians, practitioners, and policymakers in recent times (Munirathinam, 2020; Sari, Gules, & Yigitol, 2020). In the era of transformation of business trends, it is necessitated for the companies to embrace the fourth industrial revolution in their operations and broader supply chain networks (Kiel, Müller, Arnold, & Voigt, 2017; Li, 2018; Reischauer, 2018). The realization of this change is marked by the integration of networked manufacturing systems, multiple smart factories, and digital DOI: 10.4018/978-1-7998-8548-1.ch088

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 Analysis of Challenges Responsible for the Slow Pace of Industry 4.0 Diffusion

engineering integration along the product’s value chain (Barata, Rupino Da Cunha, & Stal, 2018). The smart factory is a fundamental unit and critical feature of Industry 4.0 (Liu & Xu, 2016). It involves the amalgamation of sensors, simulations, data-dependent models, predictive engineering, computing platforms along with information and communication technology (ICT) (Abed, 2016; Kusiak, 2018; Qu et al., 2016; Tang, Cao, Zheng, & Huang, 2015). Adapting these technologies in processes will help manufacturing firms to gain customer-centricity while improving operational performance (Nigappa & Selvakumar, 2016; Szalavetz, 2019). It will also help to increase employee wellbeing by driving zeroincident production set-up (Luthra, Garg, Mangla, & Singh Berwal, 2018). From the perspective of an emerging economy, smart manufacturing can impel the manufacturing sector and help India become a truly global hub (Luthra, Kumar, Zavadskas, Mangla, & Garza-Reyes, 2020). However, despite the advantages of industry 4.0 as underlined above, the actual pace of smart manufacturing adoption has been slow (Luthra et al., 2018; Deloitte 2019). Incorporation of industry 4.0 principles in manufacturing requires synchronized and focused action from various stakeholders such as managers, policymakers and members in the supply chain (Lasi, Fettke, Kemper, Feld, & Hoffmann, 2014). Managers face issues while translating smart manufacturing opportunities into concrete strategies for their business. The literature has outlined the hurdles faced by firms in their decision to move from traditional to smart manufacturing such as lack of willingness of top managers (Luthra et al., 2018; Luthra & Mangla, 2018) and cost of implementation (Gokarn & Kuthambalayan, 2017; Sanders, Elangeswaran, & Wulfsberg, 2016; Sommer, 2015). The realization of smart manufacturing is not contingent upon a few factors but depends upon various factors, often spanning organizational boundaries. Therefore, in the present study, we set out to present a nuanced understanding of challenges from the perspective of the firms that are into the process of digitalization but their pace of this transformation is inhibited by various factors. We make a departure from existing few studies (e.g., Kamble et al., 2018; Sanders et al., 2016) that have highlighted the barriers towards the implementation of smart manufacturing in firms where firms have just started the process or are at the very initial stages of this transformation. This study identifies factors critical for implementation of smart manufacturing where firms have had some experiences with the process. Therefore, we understand that these firms have sufficient funds and willingness to undergo transition; however, they face challenges in transforming their industry 4.0 vision into concrete business outcomes. We underline the challenges faced by these firms by referring to the extant literature and augment them with the help of expert opinions. These challenges are then analyzed for the case of a manufacturing company that is undergoing a transition from traditional to smart manufacturing. Therefore, the purpose of the present work is to seek answers to the following research questions: • •

What are the challenges faced by firms undergoing a transition from traditional to smart manufacturing in India? How to determine the priority of these challenges, that affect manufacturing digitalization?

In order to address this research objective, set in the context Indian manufacturing industry, important challenges reported in the literature were identified in the comprehensive survey of literature. We incorporate fuzzy Delphi technique to finalize the identified challenges. Fuzzy analytical hierarchy process (FAHP) is applied to establish the relative prominence of the challenges. Since AHP is not sufficient towards managing the uncertainty and vagueness that accompanies human judgments, therefore, fuzzy logic is applied (Kumar et al., 2018). 1738

 Analysis of Challenges Responsible for the Slow Pace of Industry 4.0 Diffusion

The rest of the study is structured as follows. A survey of literature to identify challenges linked with the adoption of Smart Manufacturing is presented in Section 2. The research methodology is explained in Section 3. The case application of the industrial component manufacturing company is discussed in Section 4. In Section 5, we apply the proposed methodology to the case. A sensitivity analysis is done in Section 6. In Section 7, we carry out the discussion of the results obtained from the analysis. The academic and managerial implications are depicted in Section 8. Conclusions, limitations, as well as directions for future research, are provided in Section 9.

2. LITERATURE REVIEW 2.1. Smart Manufacturing Prosperity of nations is vastly dependent on the manufacturing sector (World Economic Forum, 2012). More than 70% of variations in the income of 128 countries is explained by differentiation in manufacturing exports (Deloitte, 2013). Several authors contend that industry 4.0 will not only bring about a change in the economic aspect but will also act as breakthrough for the cultures and societies (Oztemel & Gursev, 2018; Ramakrishna, Ngowi, Jager, & Awuzie, 2020) While the world is witnessing the wave of digitalization, the issues are becoming increasingly relevant to emerging economies such as India because it is anticipated that most of the manufacturing in the world will take place Asian countries in the next two decades (Hu & Hsu, 2010; Luthra et al., 2020) The ‘Industry 4.0’ wave is going to play an important role in deciding the successful manufacturing destinations it would change the way manufacturers conceptualize, manufacture, and remanufacture the products (AIMA, 2018). To realize the digitalization benefits, emerging economies need to brace themselves for change that ensues with the implementation of technologies and reap the benefits thereof (Dutta, Kumar, Sindhwani, & Singh, 2020; Islam, Marinakis, Majadillas, Fink, & Walsh, 2020). Smart manufacturing systems have dynamic structures that represent a future form of industrial networks (Ivanov, Dolgui, Sokolov, Werner, & Ivanova, 2016) as they combine digital technologies with physical technologies (Kolberg, Knobloch, & Zühlke, 2017). This type of integration provides opportunities for mass customization and reduce the product development time (Fatorachian & Kazemi, 2018b; Strozzi, Colicchia, Creazza, & Noè, 2017; Zhong, Xu, Chen, & Huang, 2017). The broad areas related to manufacturing that will be enhanced by industry 4.0 design principles include factory visibility, material circularity, planning, maintenance, total quality management, and connected supply chain (Ben-Daya, Hassini, & Bahroun, 2019; Kerin & Pham, 2020). The key principles that govern the factories of the future are interoperable, virtual, modular and decentralized systems that lead to mass personalization, real-time tracking and customer centricity (Ghobakhloo, 2018). These principles are led by key technologies such as Cyber-Physical Systems (CPS), IoT, Robotics, Big-data, Cloud applications, Augmented Reality, RFIDs, and ICTs, to name a few. Smart manufacturing adoption will improve competency of manufacturing firms through innovation, cost-reduction, efficient utilization of resources, recycling, and reuse (Dubey et al., 2017; Thoben et al., 2017). The extant literature underlines and emphasizes some of the important performance outcomes of digitized manufacturing as focus on quality along with quantity, fostering talent, and environmental focus (Büchi, Cugno, & Castagnoli, 2020; Kerin & Pham, 2020; Li, 2018). The systematic perspective on industry 4.0 has made researchers and policymakers aware of the importance of powerful institutional

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 Analysis of Challenges Responsible for the Slow Pace of Industry 4.0 Diffusion

mechanisms to bring out the technological change in the form of digitalization initiatives. Prominent manufacturing countries across the globe have begun to formulate policy approaches to promote Industry 4.0 (Lu & Weng, 2018).

2.2. Challenges Towards Smart Manufacturing In this study, challenges faced by firms that are undergoing the implementation of smart manufacturing were identified from a systematic literature survey and experts’ opinion. In the first phase, a literature survey was carried out by searching search strings such as Industry 4.0, manufacturing digitalization, IoT, smart manufacturing, Industry 4.0, challenges to Industry 4.0, industry 4.0 roadmap, and factors affecting smart manufacturing. These searches were supplemented by cross-referencing for identifying significant studies. ProQuest and Scopus databases, coupled with a search on Google Scholar, were done to investigate the research papers. In total, 75 papers were met the criteria of present study. The criteria that guiding for filtering the articles is as follows: articles published in the English language in peer-reviewed academic journals and proceedings of conferences and articles in press available were considered for understanding the challenges to smart manufacturing. The articles that have a clear focus on industry 4.0 technologies, and one or more aspects of smart manufacturing were selected. Twenty-two challenges were identified from the comprehensive literature survey. Twenty-two challenges towards Smart Manufacturing were finalized in the first phase of the study. These challenges are listed in Table 1. Table 1. Challenges to smart manufacturing Dimension

Codes

Barrier

Description

Sample References

Coordination problem across different units or departments

Problems in exchange of information across departments due to lack of uniformity in formats, and differences in allocation of information technology resources

(Olsen & Tomlin, 2020; Penas, Plateaux, Patalano, & Hammadi, 2017)

Interoperability issues

The companies seek industry 4.0 solutions from different service providers, leading to interoperability issues between the different smart systems

(Lu, 2017)

Lack of innovativeness

Industry 4.0 is regarded as a significant agent of change in our current industrial system. Therefore Industry 4.0 calls for a systematic process for business model innovation

(Frank, Mendes, Ayala, & Ghezzi, 2019)

M4

Lack of digital strategy

Clear and coherent digital strategies are required to transform the business by digitization. Organizations that are in the nascent stage of digitalization don’t understand the implications of digital technologies

(Jentsch, Riedel, Jäntsch, & Müller, 2013; Tan, Zhan, Ji, Ye, & Chang, 2015; Wang, Wan, Zhang, Li, & Zhang, 2015)

M5

Dealing with employee resistance to change

Industry 4.0 would lead to a considerable restructuring of jobs as well as job redundancy. This might lead to resistance from employees necessitating serious consideration of management

(Grangel-Gonzalez et al., 2016; Schröder, Falk, & Schmitt, 2015)

M6

Absence of adequate management systems

Systems such as standard operating procedures, knowledge management systems are absent in the organization

(D. Lin, Lee, Lau, & Yang, 2018)

M1

M2

M3 Management

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 Analysis of Challenges Responsible for the Slow Pace of Industry 4.0 Diffusion

Table 1. Continued Dimension

Policy

Codes

Description

Sample References

P1

Cybersecurity and privacy

The legal issues surrounding Industry 4.0 include data ownership, data privacy, and protection; regulatory issues concerning new Industry 4.0 products.

(Ani, He, & Tiwari, 2017; D. Dutta & Bose, 2015; Kadera & Novak, 2017; Kiel, Müller, et al., 2017; Opresnik & Taisch, 2015; Rong, Hu, Lin, Shi, & Guo, 2015; Sari et al., 2020)

P2

Government policy and regulation

National policies should encourage researching Industry 4.0 topics and accelerate the deployment and application of Industry 4.0 technologies

(Kagermann, 2015; Shrouf & Miragliotta, 2015)

P3

Lack of support from international platforms

International rules by agencies like the World Trade Organization, governing cross-border flow of data and associated products/ services, need revision

(Bonomi, Milito, Natarajan, & Zhu, 2014)

P4

Trade restrictions

Data laws can also act as a trade barrier. Countries have separate, different, and often non-transparent rules on cross-border data flows

(T. Chen & Tsai, 2016; Meltzer, 2015)

Skill deficit

The shift towards “Industry 4.0” will require specialized skills in its key technologies and principles. Both managerial as well as technical skill shortages are already known to exist in these areas

(IBEF, 2019) (L. Chen & Fong, 2012; D. Dutta & Bose, 2015; Kadera & Novak, 2017; Opresnik & Taisch, 2015; Rong et al., 2015)

R2

Data integration challenge

Data Integration means coordination of diversified databases, software, equipment, and personnel into a coalition. The challenge is to integrate them efficiently within the existing design and manufacturing environment

(Kolberg et al., 2017; Lalanda, Morand, & Chollet, 2017; Schröder et al., 2015)

R3

Too many choices available

Adoption of smart manufacturing standards has a high initial cost. Raising of funds is important in the initial stages

(IBM, 2019)

R4

Broadband infrastructure

The immediate challenges for digital initiatives in developing countries ownership and maintenance of connectivity, speed, and broadband infrastructure

(Islam, Marinakis, Majadillas, Fink, & Walsh, 2018; Sommer, 2015)

Accessibility issues

To ensure the integrity of a smart factory, it is important that the system should be able to verify where it came from and control how can it be accessed

(Ly, Lai, Hsu, & Shih, 2018)

R6

Training requirements

A wide range of initiatives like collaborating for curricular development and training the employees, developing skill training methodologies, building a cloud IT platform for sharing methodologies

(Liboni, Cezarino, Jabbour, Oliveira, & Stefanelli, 2019; K. Lin, Shyu, & Ding, 2017; Longo, Nicoletti, & Padovano, 2017; Schröder et al., 2015)

R7

Inferiority of existing data

The existing data do not support the decisionmaking process

(Luthra et al., 2018; Luthra & Mangla, 2018)

R1

Resource

Barrier

R5

continues on following page

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 Analysis of Challenges Responsible for the Slow Pace of Industry 4.0 Diffusion

Table 1. Continued Dimension

Codes

Barrier

Description

Sample References

S1

Lack of clarity towards digitalization benefits

There is a lack of clarity towards perceived direct and indirect benefits of industry 4.0

(D. Lin et al., 2018)

S2

Lack of standards

A standard reference architecture is required for the successful transformation of a firm from traditional firm to digitalized firm

(Jung, Choi, Kulvatunyou, Cho, & Morris, 2017; Oesterreich & Teuteberg, 2019)

S3

Reluctance from partners in the supply chain

Industry 4.0 adoption in the focal firm is affected in the focal firm if its supply chain partners are reluctant towards coordination for implementation of smart manufacturing

(Ben-Daya et al., 2019; Martinez, Bastl, Kingston, & Evans, 2010; Saldivar et al., 2015)

S4

Contractual issues

Issues such as liability regarding the responsibility for damage caused by autonomous equipment might arise

(Narendra, Norta, Mahunnah, Ma, & Maggi, 2016)

Data ownership issue while outsourcing

Outsourcing for industry 4.0 solutions requires a deep level sharing of information between customer organization and vendor(s). Intellectual property management and data protection issues pose a challenge for businesses utilizing outsourcing

(Santos, Loures, Piechnicki, & Canciglieri, 2017)

Stakeholder

S5

3. RESEARCH METHODOLOGY 3.1. Fuzzy Delphi Method Delphi method is a systematic forecasting method based on interaction. This method banks upon experts and is used when a clear resolution of an issue is not available. It is widely applied as an approach to eliminate the less important variables (R.-H. Chen, Lin, & Tseng, 2015; Kumar et al., 2018; Mahajan, Linstone, & Turoff, 2006). The Delphi method is appropriate when there is knowledge deficiency about a phenomenon (Dalkey & Helmer, 1963) and is popularly used for business forecasting. Fuzzy based Delphi was introduced by Ishikawa et al. in 1993 to overcome the drawbacks associated with Delphi method (Ishikawa et al., 1993). For example, traditional Delphi is more costly and time-consuming as it comprises of repeated surveys. Several researchers have applied Fuzzy Delphi method in integration with fuzzy AHP approach to facilitate group decision making (Bouzon, Govindan, Rodriguez, & Campos, 2016; Hsu, Lee, & Kreng, 2010; Shi, Wu, & Tseng, 2017). We utilize fuzzy Delphi to enable group decision-making, intending to understand the challenges towards industry 4.0 initiatives in the Indian manufacturing industry. The modus operandi of the fuzzy Delphi method given below: Step 1: In first step, different challenges faced in manufacturing digitalization are identified. The challenges encountered during the industry 4.0 adoption in manufacturing industry are enlisted in Table 1. Step 2: The challenges identified in the previous step are evaluated by our panel of experts. Their judgment is captured using the linguistic scale given in Table 2.

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 Analysis of Challenges Responsible for the Slow Pace of Industry 4.0 Diffusion

Let the jth (where j = 1, 2, 3…..m) challenge evaluation of the ith expert (where i =1, 2, 3….n) is triangular fuzzy number x ij : x ij = (pij , qij , rij )

(1)

The fuzzy weights of the challenge aj are calculated as follows: Aj = (p j , q j , rj ) p j = min (pij )

(

) = max (r )

q j = ∏ni =1 qij rj

1/n



(2)

ij

Step 3: The importance (S j ) of each challenge is computed applying the mean method. A value α , which is a threshold value, is set for accepting the challenge into our list. If the calculated importance is less than the threshold, that particular factor is omitted: S j = (p j + q j + rj ) / 3

(3)

Table 2. Linguistic scales Linguistic Variables

Fuzzy Number

Very low

(0, 0, 0.1)

Low

(0, 0.1, 0.3)

Medium low

(0.1, 0.3, 0.5)

Medium

(0.3, 0.5, 0.7)

Medium high

(0.5, 0.7, 0.9)

High

(0.7, 0.9, 1.0)

Very high

(0.9, 1.0, 1.0)

3.2. Fuzzy AHP Saaty developed AHP as a tool for multi-criteria decision-making (MCDM) problems. AHP simplifies the complex decision-making problem by decomposing it into sub-problems (Saaty, 2008). It converts the decision problem into a hierarchy arrangement which consists of goal, criteria, and sub-criteria. AHP deliberates upon the judgments of the expert decision-makers. However, this technique does not resolve the imprecision and ambiguity involved in the human judgments (Ishizaka & Labib, 2009; Ly et al., 2018). Fuzzy AHP, which is based on the fuzzy set theory proposed by Zadeh (Zadeh, 1965) is therefore, adopted. Many methods for the fuzzifying the AHP process have been recognized by the scholars in the extant literature (Buckley, 1985; van Laarhoven & Pedrycz, 1983). Among them, one of

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 Analysis of Challenges Responsible for the Slow Pace of Industry 4.0 Diffusion

the widely used approaches is Chang’s extent-analysis-based method (Tyagi, Kumar, & Kumar, 2018). Chang’s extent analysis is based on the calculation of synthetic extent values of the triangular fuzzy number (Chang, 1996). These values were based on pair-wise comparisons. An extent analysis for each criterion concerning goal gi is conducted in this method. The extent-analysis-based tool is used to generate a set for satisfying the goal. This set is called a satisfied extent. Let V= {v1, v2, .. ., vn} be the criterion set, and T = {t1, t2, .. ., tn}be the goal set. For applying Chang’s extent analysis, each challenge is subjected to an extent analysis. ‘m’ extent analysis values for each of the challenge are obtained by using the following: Ag1 , Ag2 , Ag3 , Ag4 , … Agm , i = 1, 2, 3 …n i

i

i

i

(4)

i

where all the Agj ( j = 1, 2, 3 … 4) are triangular fuzzy numbers (TFNs). i

The steps are as follows: Step 1: The challenges finalized by the fuzzy Delphi approach are evaluated by experts on the basis of a linguistic scale. The linguistic scale is given in Table 3. After that, the linguistic terms are altered into the corresponding TFNs. The expert inputs are combined to calculate the elements of the pairwise comparison matrix by applying Buckley’s geometric mean method (Buckley, 1985). Assume that there are ‘n’ challenges. Pairwise assessment of challenge i with challenge j gives a square fuzzy matrix An×n. Ãij denotes the relative importance of challenge i with respect to challenge j. In the fuzzy matrix A, Ãij = (1, 1, 1) when i = j and Ãji = 1/ Ãij: Ã  11 Ã12 …. Ã1 j ….. Ã  21 Ã22 …. Ã2 j ….  : : A= : …. ….  Ã Ã 31 Ã 32 ij   Ã41 Ã42 …. Ãnj …. 

Ã1n  Ã2n  :   Ã  in  Ãnn  

(5)

If there are K experts in the panel, the pairwise comparison matrix elements are computed as follows using the Buckley’s approach:

(

Ãij = Ãij1 ⊗ Ãij2 ⊗ Ãij3 ⊗ Ãij4 ⊗ Ãij5 ⊗ … Ãijk

)

1/K



(6)

Step 2: Computing the fuzzy synthetic degree with respect to the ith object as follows: Fi = Σ j =1Agj ⊗ Σni =1Σmj =1Agj  i i  

−1



‘Fuzzy addition’ of m extent analysis values of a particular matrix are done as follows:

1744

(7)

 Analysis of Challenges Responsible for the Slow Pace of Industry 4.0 Diffusion

m m  m  j A = a , b , c ∑ gi ∑ j ∑ j ∑ j  j =1 j =1 j =1 j m

(8)

where (a, b, c) is a triangular fuzzy number in which ‘a’ is the lower limit value, ‘b’ is the middle value and the upper limit value is ‘c.’ −1

  n m For obtaining  ∑∑Agj  fuzzy addition of Agj values is performed as follows: i  i =1 j =1 i  n m n n  n   j A = a , b , ∑∑ gi ∑ i ∑ i ∑ci  i =1 j =1 i =1 i =1 j

(9)

The inverse of the above vector is calculated:   n m j   A  ∑∑ gi   i =1 j =1 

−1

   1 1 1    = n , , n n    ∑ i =1ci ∑ i =1bi ∑ i =1ai 

(10)

Step 3: Degree of possibility of two TFNs A1 and A2 is computed using the following relation: V (A2 > A1 ) = supy >y  min µA (y ), µA (y ) 2 1  1 2 

(11)

where A13 A2, i.e., (a2, b2, c2)3 (a1, b1, c1) µA (y ) and µA (y ) are the values on the axis of membership 1

2

function of each criterion. The condition of V(A2 3 A1) = 1 is b2 3 b1. If b2 £ b1, assume V(A2 3 A1) = hgt (A2 Ç A1). Then:     1if b 2 > b1  V (A2 > A1 ) = µd = 0 if a2 > c2    a1 − c2 otherwise   (b2 − c2 ) − (b1 − a1 ) 

(12)

Figure 1 demonstrates the intersection of two triangular fuzzy numbers. The ordinate of highest intersection point D between μA1 and μA2 is ‘d.’ The degree of possibility for a TFN, greater than k TFNs, Ai (i = 1, 2, ...k) is defined as: V (A ≥ A1, A2 , A3 ...Ak ) = V (A ≥ A1 ) and (A ≥ A2 ) and (A ≥ Ak )   = minV (A ≥ Ai ), i = 1, 2, 3...k

(13)

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 Analysis of Challenges Responsible for the Slow Pace of Industry 4.0 Diffusion

Figure 1. The interaction between A1 and A2 (Chang 1996)

Let s’(Ai) = min V (Pi 3 Pk) for k =1, 2, 3……. n; k 1 i. The weights are given as:

(

)

T

W ' = s ' (H 1 ), s ' (H 2 ),...s ' (H n )

(14)

where Hi (i=1,2,3,4…. n) are n elements whose minimum degree of possibility of one fuzzy number being greater than others is considered. Step 4: Calculate the priority weight of each challenge by normalization of the above weight vector:

(

)

T

W = s (H 1 ), s (H 2 ),...s (H n )

(15)

where W gives the priority weight of one challenge or challenge dimension over the other. Once we obtain the weight vector, it is important to check the consistency ratio of the matrix obtained by pair-wise comparisons. Therefore, the consistency ratios for both ‘mean values matrix’ and the ‘geometric means matrix’ are calculated (Gogus & Boucher, 1998). As suggested by Saaty, for internal consistency for each type of matrix, a consistency ratio 0.60 is kept as a threshold to decide whether a particular challenge can be included or not (Kumar et al., 2018). We also asked the experts to suggest any other important challenge which was not enlisted through the literature survey. However, the experts did not suggest any other challenge and followed the results of fuzzy Delphi method for finalizing the challenges. Hence, fifteen challenges are identified in this study. Expert feedback was utilized to further classify these challenges into the categories that include management, policy, resource, and stakeholders. Figure 2. A hierarchy model of selected barriers

5.2. Computing Pairwise Comparison Matrix Elements A hierarchy model of challenges was developed by consulting the experts, as shown in Figure 2. The hierarchy model has multiple levels which include: prioritizing the challenges in adopting smart manufacturing initiatives in the industrial component manufacturing industry (level 1, goal set); dimension of challenges (level 2, criteria); challenges (level 3, sub-criteria). The pair-wise assessment for each dimension of challenges with other dimensions of challenges as well between sub-dimensions under

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 Analysis of Challenges Responsible for the Slow Pace of Industry 4.0 Diffusion

each dimension was done by the six experts. Each expert in the panel gave a rating to each dimension based on its relative importance in the pairwise comparison. The experts used the linguistic scale defined in Table 3 for rating these challenges. After that, the linguistic scales were replaced by corresponding fuzzy number according to Table 3. Sample calculation of the triangular fuzzy number when management dimension is compared with policy dimension is given as follows: Ã12 = ((2,3,4) Ä (2,3,4) Ä (1,2,3) Ä (1,1,1) Ä (1,2,3) Ä (1/3,1/2,1))1/6 = (1.0491, 1.6189, 2.2894) Table 5 gives the pairwise comparison matrix for the four challenge dimensions. Table 5. Pairwise comparison matrix for barrier dimension M

P

R

S

M

1

1

1

1.0491

1.6189

2.2894

1.1225

1.6984

2.1822

0.7418

1.1225

1.6189

P

0.4368

0.6177

0.9532

1

1

1

0.8327

1.3480

2.0396

1.0491

1.6189

2.2894

R

0.4582

0.5888

0.8909

0.4903

0.7418

1.2009

1

1

1

0.9437

1.4262

1.9520

S

0.6177

0.8909

1.3480

0.4368

0.6177

0.9532

0.5123

0.7012

1.0596

1

1

1

CRm=0.037, CRg=0.091

5.3. Estimation of Weights With Fuzzy Extent Analysis The dimensions weights of challenges are calculated using extent analysis method, as explained in Section 3.2. The different values of the fuzzy synthetic extent concerning four main dimensions are denoted by AM, AP, AR and AS, respectively. By using Equation (7) the fuzzy synthetic extent values are computed as follows: AM= (3.91, 5.43, 7.09) Ä (1/22.77, 1/16.99, 1/12.69) = (0.17, 0.32, 0.55) AP= (3.31,4.58,6.28) Ä (1/22.77, 1/16.99, 1/12.69) = (0.14, 0.26, 0.49) AR= (2.89, 3.75, 5.04) Ä (1/22.77, 1/16.99, 1/12.69) = (0.12, 0.22, 0.39) AS= (2.56, 3.20,4.36) Ä (1/22.77, 1/16.99, 1/12.69) = (0.58, 0.18, 0.34) Degree of possibility of Ai over Aj (i 1 j) is computed by using Equations (11) and (12): V (AM 3 AP) = 1, V (AM 3 AR) = 1, V (AM 3 AS) = 1 V (AP 3 AM) = 0.865, V (AP 3 AR) = 1, V (AP 3 AS) = 1

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 Analysis of Challenges Responsible for the Slow Pace of Industry 4.0 Diffusion

V (AR 3 AM) = 0.695, V (AR 3 AP) = 0.838, V (AR 3 AS) = 1 V (AS 3 AM) = 0.567, V (AS 3 AP) = 0.710, V (AS 3 AR) = .871 Equation (13) is used to compute the priority weights: S’(H1) = min (1, 1, 1) S’(H2) = min (0.865, 1, 1) S’(H2) = min (0.695, 0.838, 1) S’(H3) = min (0.567, 0.710, 0.871) W’ = (1, 0.865, 0.695, 0.567) Final weight is obtained by the normalization of W’ using Equation (15). The final weights of the dimension are: W = (0.320, 0.276, 0.222, 0.181) The consistency of the matrices was checked using the consistency ratio method. Similarly, the weights of the challenges within each dimension are also obtained. The preference of challenge dimensions and their ranks are shown in Table 6. The ‘management’ dimension of challenges obtained the highest weight, followed by policy challenges, resource challenges and stakeholder challenges. Table 6. Relative weights using fuzzy synthetic extent method Fuzzy Sum of Each Row

Fuzzy Synthetic Extent

3.9134

5.4397

7.0905

0.1718

0.3202

0.5587

3.3186

4.5846

6.2823

0.1457

0.2698

0.4950

Degree of Possibility of Mi > Mj 1.000 0.865

2.8922

3.7568

5.0439

0.1270

0.2211

0.3974

0.695

0.838

2.5668

3.2098

4.3608

0.1127

0.1889

0.3436

0.567

0.710

Degree of Possibility

Relative Weights

1.000

1.000

1.000

0.320

1.000

1.000

0.865

0.2767

1.000 0.871

0.695

0.2222

0.567

0.1813

Similarly, the challenges weights within the dimensions are also calculated. The calculated weights of the challenges within each dimension are depicted in Table 7 to Table 10. The global weights and relative priority weights and ranks are calculated as shown in Table 11. The global weights were generated by computing the product of dimension weight and the weight of the associated challenge. Table 11 also shows the global ranks the challenges. The result demonstrates that the government policy and regulation (P1) challenge is most prioritized, while the lack of standards (S2) challenge took up the last position.

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 Analysis of Challenges Responsible for the Slow Pace of Industry 4.0 Diffusion

Table 7. Pairwise comparison matrix for management barriers M1

M2

M3

M4

Relative Weights

M5

M1

1

1

1

0.5774

0.8909

1.4422

0.7783

1.0699

1.5131

0.7783

1.0699

1.5131

1.0000

1.5131

2.0396

0.209

M2

0.6934

1.1225

1.7321

1

1

1

0.9347

1.3480

1.9786

1.2009

1.5874

1.8860

1.2599

1.8171

2.2894

0.253

M3

0.6609

0.9347

1.2849

0.5054

0.7418

1.0699

1

1

1

1.1776

1.7321

2.4019

0.8736

1.4422

2.2894

0.221

M4

0.6609

0.9347

1.2849

0.5302

0.6300

0.8327

0.4163

0.5774

0.8492

1

1

1

1.6189

2.6960

3.7316

0.218

M5

0.4903

0.6609

1.0000

0.4368

0.5503

0.7937

0.4368

0.6934

1.1447

0.2680

0.3709

0.6177

1

1

1

0.099

CRm=0.029, CRg=0.076

Table 8. Pairwise comparison matrix for policy barriers P1 P1

1

P2

1

1

1.1776

Relative Weights

P3

1.9442

2.8845

1.2599

2.0396

2.7495

.537

P2

0.3467

0.5144

0.8492

1

1

1

1.2009

1.6984

2.4183

.339

P3

0.3637

0.4903

0.7937

0.4135

0.5888

0.8327

1

1

1

.123

CRm=0.026, CRg=0.085

Table 9. Pairwise comparison matrix for resource barriers R1

R2

R3

Relative Weights

R4

R1

1

1

1

1.0000

1.5874

2.2649

0.5246

0.6934

0.9532

1.1487

1.6438

2.2679

0.287

R2

0.4415

0.6300

1.0000

1

1

1

0.4903

0.6609

1.0000

1.2599

2.2894

3.3019

0.268

R3

1.0491

1.4422

1.9064

1.0000

1.5131

2.0396

1

1

1

2.1822

3.2377

4.2628

0.422

R4

0.4409

0.6084

0.8706

0.3029

0.4368

0.7937

0.2346

0.3089

0.4582

1

1

1

0.021

CRm=0.02, CRg=0.05

Table 10. Pairwise comparison matrix for stakeholder barriers S1

S2

Relative Weights

S3

S1

1

1

1

1.6984

2.7495

3.7719

1.1225

1.3480

1.5131

0.621

S2

0.2651

0.3637

0.5888

1

1

1

0.4368

0.6177

0.9532

0.030

S3

0.6609

0.7418

0.8909

1.0491

1.6189

2.2894

1

1

1

0.349

(CRm=0.02, CRg=0.05)

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 Analysis of Challenges Responsible for the Slow Pace of Industry 4.0 Diffusion

Table 11. Global weights and global rank of criteria Category

Management

Policy

Resource

Stakeholder

Relative Weight

0.320

0.277

0.222

0.181

Barrier

Relative Weights

Relative Rank

Global Weights

Global Rank

M1

Coordination problem across different units or departments

0.209

4

.066

8

M2

Interoperability issues

0.253

1

.080

5

M3

Lack of innovativeness

0.220

2

.070

6

M4

Lack of digital strategy

0.218

3

.069

7

M5

Dealing with employee resistance to change

0.099

5

.031

13

P1

Cybersecurity and privacy

.537

1

.148

1

P2

Government policy and regulation

.339

2

.093

4

P3

Lack of support from international platforms

.123

3

.034

12

R1

Skill deficit

0.287

2

.064

9

R2

Data integration challenge

0.268

3

.059

11

R3

Too many choices available

0.422

1

.094

3

R4

Broadband infrastructure

0.021

4

.01

14

S1

Lack of clarity towards digitalization benefits

0.621

1

.112

2

S2

Lack of standards

0.030

3

.006

15

S3

Reluctance from supply chain partners

0.349

2

.063

10

6. SENSITIVITY ANALYSIS The analysis of sensitivity is essential in the multi-criteria analysis as the weights of challenges are calculated on the basis of judgments of the experts (Govindan, Mangla, & Luthra, 2017; Kumar et al., 2018; Pitchipoo, Venkumar, & Rajakarunakaran, 2013). From Table 5, ‘Management’ dimension has the highest priority or global weight and therefore, is ranked first. Therefore, this dimension is varied from 0.1 to 0.9. The step size for the variation is 0.1. When management dimension takes a value of 0.1 and 0.2, the highest change is recorded in ‘policy’ dimension. The weight of the policy dimension takes a steep surge, as depicted in Table 12 and becomes the top priority challenge. However, for values of management dimension larger than or equal to 0.3, the ranking of the challenges remains the same as the original ranks. Figure 3 also illustrates the change in ranks of specific challenges due to variation in global priority when the weight of management dimension is varied. The same procedure is followed for the ‘policy’ dimension which has the second highest priority. The values are varied from 0.1 to 0.9 which leads to a large variation in management dimension weight, as shown in Table 13. Figure 4 illustrates the change in the ranks of specific challenges.

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 Analysis of Challenges Responsible for the Slow Pace of Industry 4.0 Diffusion

Table 12. Priority values of dimensions’ after varying the values of “management” dimension Global Priority Values After Varying “Management Barrier Dimension”

Dimension of Barrier

Normal

Management

0.320

0.1000

0.2000

0.3000

Policy

0.277

0.3666

0.3259

Resource

0.222

0.2938

Stakeholder

0.181

0.2396

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0.4000

0.5000

0.6000

0.7000

0.8000

0.9000

0.2852

0.2444

0.2037

0.1629

0.1222

0.0815

0.0407

0.2612

0.2285

0.1959

0.1632

0.1306

0.0979

0.0653

0.0326

0.2129

0.1863

0.1597

0.1331

0.1065

0.0798

0.0532

0.0266

Figure 3. Sensitivity analysis outcome on varying policy dimension

Table 13. Priority values of dimensions’ after varying the values of “Policy” dimension Dimension of Barrier

Global Priority Values After Varying “Policy Barrier Dimension” Normal

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

Management

0.320

0.3984

0.3541

0.3098

0.2656

0.2213

0.1770

0.1328

0.0885

0.0442

Policy

0.277

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000

0.9000

Resource

0.222

0.2764

0.2456

0.2149

0.1842

0.1535

0.1228

0.0921

0.0614

0.0307

Stakeholder

0.181

0.2253

0.2003

0.1752

0.1502

0.1252

0.1001

0.0751

0.0501

0.0250

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 Analysis of Challenges Responsible for the Slow Pace of Industry 4.0 Diffusion

Figure 4. Sensitivity analysis outcome on varying policy dimension

7. DISCUSSION The results reveal that the ‘management’ dimension has the maximum priority with a weight score of 0.320. The relatively higher score demonstrates the importance of resolving management related issues for the digitalization initiatives in the Indian context. As indicated by the results of our study, the interoperability issue faced by the firms (M2) is the greatest challenge, followed by a lack of innovativeness (M3). The firms obtain different digitalization solutions from different vendors, which leads to the problem of interoperability, wherein the different technological solutions are incompatible (Sanders et al., 2016). This incompatibility issues, which are by and large avoidable, arise due to the mismatched data formats, firm-specific software, and individual vendor’s confidentiality terms. Also, innovation-driven breakthroughs are required by convergence and integration of existing technologies for smart manufacturing systems (Kagermann, 2015; Lin et al., 2017). However, firms fall short in terms of innovativeness and, therefore, find it difficult to assimilate digital technologies into their existing systems. The lack of digital strategy (M4) comes out to be the third important challenge under the management dimension. Firms planning for digital transformation need to strategically understand where to invest their resources and which advanced technologies can best serve their needs. Nonetheless, organizations struggle to identify strategic fields of action that can help them to move forward in terms of industry 4.0 adoption (Erol, Schumacher, & Sihn, 2016). The next challenge is the coordination problem across different units or departments (M1). The poor coordination between different units and departments can lead to loss of opportunity as a large amount of data gets blocked in one department. This may also result in a situation where different departments differ in terms of the level of digitization (Ben-Daya et al., 2019). Dealing with employee resistance to change (M5) is the subsequent challenge in this category. Technological change may hurt employment, as it would require the restructuring of jobs. Moreover, there are concerns that Industry 4.0 might pave way to technological unemployment (Roblek, Meško, & Krapež, 2016). These issues pose a challenge for the management to gather support from the front-end employees.

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 Analysis of Challenges Responsible for the Slow Pace of Industry 4.0 Diffusion

The policy dimension ranks second with a weight score of 0.277. Industry 4.0 comprises of horizontal integration in the value chain. Horizontally integrating customers and supply chain partners will lead to new types of cooperation models for the firms (Cotteleer, 2017). In this scenario, legal issues such as liability and security of intellectual property have gained importance. It is important to have the right regulatory impetus from governments concerning industry 4.0 research, infrastructure, and legal concerns. The most important type of challenge in this category includes those related to cybersecurity and privacy threats (P1). Manufacturing is the second-most targeted industry for attacks and third most likely to have security incidents according to the IBM X-Force Threat Intelligence Index (2018) (IBM, 2019). Moreover, it is extremely challenging to build a superior level IoT-based cybersecurity for smart manufacturing due to its complex dynamic management processes (Y. Lu, 2017). In this particular category of challenges, government policy and regulation (P2) is another important challenge. In a developing country like India, a future-ready industrial policy in synchronization with the challenges and opportunities for India within the fourth industrial revolution is of extreme importance. The policy must offer an overarching umbrella framework for facilitating digitalization. For instance, China has launched a made-in-China-2025 plan that aims towards a major breakthrough for a steadfast journey towards manufacturing digitalization (D. Lin et al., 2018). The next challenge in this category is the lack of support from international platforms (P3). A fresh orientation of provisions in international trade agreements has become necessary to facilitate the digital economy. Cross-border data flows become indispensable as the economy becomes increasingly digital. International trade agreements need to act to the challenges posed by cyber sovereignty, tough regulatory measures, and lack of interoperability of domestic consumer protection laws (Strange & Zucchella, 2017). The third dimension includes resource-related challenges with a weight of 0.222. In this dimension, too many available choices (R3) is the most important challenge, followed by a skill deficit (R1). The managers are bombarded with information on too many software solutions available in the market. Business leaders find it difficult to choose between the immensely large number of available options and catch up with the high pace of new solutions coming up frequently in the market. The next challenge in this category is the data integration challenge (R2). A typical plant has multiple PLCs and controllers that have disparate protocols. These differentiated protocols lead to non-uniform data collection and storage. It has been witnessed that about 60% of Industry 4.0 program budgets and about 60% of the time is consumed by data integration projects (Samuel, 2019). The last challenge under this dimension is broadband infrastructure (R4). Establishing infrastructure and its maintenance is an important challenge associated with industry 4.0 adoption. The fourth dimension includes stakeholder related challenges with a weight of 0.181. In this dimension, the lack of clarity towards digitalization benefits (S1) is the most important challenge. The effects and implications of industry 4.0 are still uncertain as researchers and practitioners make contradictory statements on its potential benefits and risks (Kiel, Arnold, & Voigt, 2017; Kiel, Müller, et al., 2017; Müller, Kiel, & Voigt, 2018). This perceived ambiguity by the stakeholders towards digitalization benefits poses a challenge towards its adoption. Reluctance to change by supply chain partners (S3) is the next important challenge. It is especially important to involve every stakeholder in order to drive organizational transformation through joint action (Kiel, Arnold, et al., 2017). However, if the supply chain partners are reluctant towards the change, it becomes difficult for the focal firm to initiate necessary actions. Lack of standards (S2) is the next prioritized challenge in the list. Developing common standards and architecture to support these standards is crucial for the successful implementation of smart manufacturing initiatives (Liao, Deschamps, Loures, & Ramos, 2017; Moghaddam, Cadavid, Kenley, 1756

 Analysis of Challenges Responsible for the Slow Pace of Industry 4.0 Diffusion

& Deshmukh, 2018). Companies need to eliminate information blackholes by creating unified, global integration platforms in their organization (Tjahjono, Esplugues, Ares, & Pelaez, 2017).

8. ACADEMIC AND MANAGERIAL IMPLICATIONS In the context of emerging economies such as India, Industry 4.0 is understood as a relatively novel concept and requires detailed studies regarding the issues faced by managers in the process of firm digitalization. The work contributes to the literature by underlining the identifying the challenges countered by the case company in the journey towards industry 4.0 adoption. The study helps the academicians and practitioners by categorizing these challenges into relevant dimensions by conducting a thorough evaluation by experts. The two important dimensions underlined by our study include management and policy, wherein cybersecurity, privacy risks, and interoperability issues are some of the most prominent factors that play a role in dampening the pace of firm digitalization. Industry practitioners must pay special attention to these challenges and make efforts towards belittling their effect. We develop a benchmark model of challenges through our comprehensive analysis. Managers and policymakers must consider finding a solution to these challenges for managing industry 4.0 initiatives. The present work will pave the way for the academicians to carry out empirical studies in the field for evaluation of different organizations and their susceptibility to the challenges. The present can also be considered as a preliminary work towards the development of theory in the context of Industry 4.0 implementation. The framework proposed in this study will help the scholars for further research work in the field, such as the exploration of potential solutions for addressing these challenges.

9. CONCLUSION Industrial leaders are moving towards digitalization for driving both revenue growth and operational efficiencies. To assist the digitalization of the manufacturing industry, the present work seeks to amalgamate digitalization challenges faced by them. The first contribution of the present work has been enlisting the challenges faced by the industrial component manufacturing industry in the advent of digitalization. Further, the fuzzy Delphi approach was utilized in finalizing the challenges. A systematic review of literature, coupled with data triangulation, has enhanced the reliability of the findings in this study. Twenty-two challenges were initially uncovered by the survey of the literature, and these were further classified into four categories following the feedback from experts. Secondly, we prioritized these challenges to assist the management towards the adoption of industry 4.0 in the manufacturing industry. The present study uses FAHP to carry out the prioritization of challenges. As per the findings, the order of the challenge dimensions is specified as management-policy-resource-stakeholders. Similarly, the priority of challenges across each dimension is also calculated. It is evident from the results that management dimension is of immense importance. This finding values the importance of effective leadership by managers to successfully implement digitalization initiatives. The present work also has some limitations. The identification of challenges faced by firms in the emerging economy context was difficult as most literature in the field comes from the developed economies. Considering the nascent stage of industry 4.0 in developing countries, some other important challenges may emerge in the future. For example, in the present study, ‘inferiority of existing data’ was 1757

 Analysis of Challenges Responsible for the Slow Pace of Industry 4.0 Diffusion

eliminated at the preliminary stage. However, in the future as more companies, especially small and medium enterprises (SMEs) move towards digitization, this issue might gain significance. The extant literature has also highlighted that the roadmap for the adoption for industry 4.0 has to be unique that envisions the challenges, that are specific to the SMEs (Mittal, Khan, Romero, & Wuest, 2018). Also, in the present study, challenges such as the high cost involved have largely been ignored since our study focuses on the issues faced by giant manufacturing firms that have sufficient funds and willingness to adopt industry 4.0. These firms are undergoing the process of digitalization to enhance their competitiveness. In future studies, the identified challenges may also be analyzed for interrelationships, using ISM, ELECTRE, DEMATEL, ANP, etc. Also, the qualitative nature of the study limits the generalizability of results to different sectors. In the future, qualitative research methodology, such as grounded theory, can help the researchers can bring out detailed insights about the challenges towards the implementation of Industry 4.0.

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Industrial Occupational Safety: Industry 4.0 Upcoming Challenges Susana Pinto da Costa https://orcid.org/0000-0001-7440-8787 University of Minho, Portugal Nélson Costa https://orcid.org/0000-0002-9348-8038 University of Minho, Portugal

ABSTRACT The new industrial revolution will encompass massive change. Manufacturing Companies are pursuing digitalization and trying to figure out how to implement collaborative robots, all the while trying to manage data safety and security. It is a big challenge to deal with all the needed infrastructures to handle the big data digitalization provides whilst having to account for the shielding of it. Even more so when one has to succeed at it while taking care of the workers, the sustainability of their jobs, the implementation of safe practices at work, based on the contributions of the whole, through efficient vertical communication, imbued with Safety Culture and aiming the sustainability of the Company itself. This chapter proposes to address the role of standardization in managing industry 4.0, where culture, Risk Management and Human Factors are key, and how the tools provided by these norms may contribute to nimbly balance each Company’s needs.

INTRODUCTION As the new industrial revolution sets in, competitive Companies find themselves overwhelmed with the Herculean task of diligently managing the traditional workers’ safety and health, quality, environment, and the increasingly complex sustainability as they try to introduce into it the new challenges brought by industry 4.0. Starting with digitization, whereby all hard copies of product manuals, instructions, customer files and repair handbooks were progressively made available and accessible in a digital format, through digitalization, where the digitization of analog data was used for applications that simplify standard work DOI: 10.4018/978-1-7998-8548-1.ch089

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 Industrial Occupational Safety

practices, all the way into digital transformation, made possible because of digitization and digitalization, which enable data to be easily accessible for use across several interfaces, platforms and devices. Digital transformation entails the devising new business applications that integrate all this digitized data and digitalized applications, and has brought artificially intelligent finite-state machines (FSM), predictive maintenance, crowdsourcing and augmented reality tools. Digital transformation business innovations are revolutionizing industry, and are aimed at saving companies’ time and money. Like the demise of Blockbuster® and Kodak®, this new industrial revolution will take a toll on companies who are unable to keep pace with the digital transformation, as they are in serious danger of becoming obsolete. The advent of digital transformation, along with automation and the development of the Internet of Things (IoT) are believed to be the catalysts of this fourth industrial revolution (Lampropoulos, Siakas, & Anastasiadis, 2019). Furthermore, their synergetic effect promotes their faster development. Nowadays, the IoT is ubiquitous; it is present in everyone’s daily life in the most varied work and leisure activities, through the smart devices whose embedded systems for sensing, communicating, data collecting, storing and processing, allow us to always be connected to work, to each other, to a wide range of services, and so much more, by bridging the physical world and the digital world. Transformation really is one of the keywords to characterize this fourth revolution; not only because it applies to the creation of a virtual world from the transformation of the physical, but also for what it allows. As a matter of fact, these evolving technologies present great potential to companies, whereby these smart systems are able to remotely sense an array of physical dimensions, collecting and storing a bulk of data, processing these data, interpreting it and acting on it, by adapting, readjusting, delaying, stopping, accelerating, or otherwise performing according to its decision-making autonomous process. Industry 4.0 is, therefore, characterized by bringing together more traditional industrial and manufacturing practices and processes, and state-of-the-art innovative, disruptive technologies like IoT, large-scale machine-to-machine (M2M) communications and cyber-physical systems (CPSs). Undeniably, smart would be the second keyword that would best describe this revolution; by fostering “self-maintainability, self-optimization, selfcognition, and self -customization into the industry”, Industry 4.0 envisions the transformation of the classic industries into intelligent industries (Lampropoulos et al., 2019). CPSs provide the environment for machines to process data via a wireless connected embedded system, posing as the bridge between the tangible, physical world and the intangible, digital world. So, basically, CPSs embody the duality transformation and smart manufacturing. According to Lampropoulos et al. (2019), CPSs differ from the traditional embedded systems in that they contain control algorithms and computational capacities that make up for “cybertwined services”, and other physical assets and differentiated computational skills, establishing networked interactions and encompassing a bulk of methodologies that are transversal to several disciplines. These systems are projected to accept physical inputs and provide physical outputs while interacting with humans and support them on their tasks through innovative communication modalities. Hence, the safety issue related to CPSs is paramount, for the success of these systems in tied to how seamlessly and effectively this interaction with humans performs. The amount of data that will be available for analysts to decide upon through these technologies is overwhelming. But it will not necessarily mean having to spend a tremendous amount of physical space to store all the information that can be gathered simultaneously at various locations within the company, in fractions of seconds timeframes, and process it. The Cloud (short for Cloud computing) will manage all the storage and computing necessary in the digital world, through several computer servers and within-Cloud resources.

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It is a big challenge to deal with all the needed infrastructures to handle the big data digitalization provides whilst having to account for the shielding of it. An even bigger challenge is to succeed at it while taking care of the workers, the sustainability of their jobs, the implementation of safe practices at work, based on the contributions of the whole, through efficient vertical communication, imbued with Safety Culture and aiming the sustainability of the Company itself. Standards have proven useful and efficient in the past, but it is the authors strong belief that these standards may be used to tackle transversely all the challenges that Industry 4.0 entails. Risk Management is the common way to perform risk monitoring, whereby installation, personnel, assets and outcomes are protected against the adverse consequences of risks and the severity and uncertainty of losses is diminished. It is composed of risk assessment (which, in turn, includes risk analysis) and risk control. It entails the identification of hazards and their extension (e.g., number of workers exposed), valuation of the risk and comparison with reference values and control of risks that endanger people, environment and heritage. It involves planning, coordination and control of activities, assets and resources to minimize the impact of uncertain events. Simply put, it consists of risk assessment and risk control and comprises a set of preventive measures and prevention policies that enable risk reduction or, better yet, elimination. This chapter proposes to address the role of standardization in managing industry 4.0, where Safety Culture, Risk Management and Human Factor are key, and how the tools provided by these norms may contribute to nimbly balance each Company’s needs.

BACKGROUND Occupational Safety and Hygiene are often perceived as a unique concept (or, at least, difficult to distinguish) when they are, in fact, very distinct terms. To this contributes the fact that, generally, one uses the “train” of words “Occupational Safety and Hygiene” to define a discipline which deals with the management of occupational hazards. Even though the areas of Hygiene and Safety intersect, they can be defined individually. Like this, whereas Occupational Hygiene is devoted to studying, assessing and controlling occupational environmental hazards (e.g., poor lighting, extreme temperatures, excessive noise, chemical contaminants, vibrations), aiming occupational disease prevention, Occupational Safety is focused on studying, assessing and controlling the risks of operation (e.g., obstacles, sharp edges, safety protection-lacking machinery, falling objects) in order to prevent the occurrence of accidents and other anomalous situations at work. Indeed, both disciplines may focus on the same hazard but will address it differently. For instance, considering the physical hazard noise, while Occupational Safety will address it as an element that may predispose workers to be more distracted or tired and hence more likely to suffer an accident (or which may prevent the hearing of relevant audible warnings thus compromising their physical integrity), Occupational Hygiene will address it as a factor that may contribute to the development of work-related hearing loss (or other occupational disease related to noise exposure). The concern for the Health and Safety of workers had a scattered, almost inexpressive, beginning, having the first been attributed to Hippocrates, who defined saturnism as lead poisoning owing to metal extraction exposure, causing stomach contractions and hardening of the abdomen. Georgius Agricola and Georg Bauer, two German doctors who lived between the end of the sixteenth century and the beginning of the seventeenth century, left records of diseases afflicting the mineworkers, as did Paracelsus (Freitas, 2016; Rodrigues, 2006). Traditionally, Bernardo Ramazzini (1633-1714) is considered the founder of Occupational Safety and Health (OSH), as he was the first to systematically treat work-related diseases, 1769

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having his work, “De morbis artificium diatribe” [Diseases of Workers], been published in 1700 (Freitas, 2016; Rodrigues, 2006). The foundation of the International Labour Organization (ILO) in 1919 marks a new era for the OSH, wherein concerns regarding this matter take a primary position (Rodgers, Lee, Swepston, & Van Daele, 2009). The advocacy of prevention measures (e.g., per trade, fields of activity, products manufactured and being used) begins (Miguel, 2014). It is estimated that, all over the world, and on a daily basis, 5,000 people die as a result of accidents or occupational diseases, accounting for two million workers’ deaths each year. Worldwide, 270 million occupational accidents occur and 160 million occupational diseases are reported, per year (Freitas, 2016, citing the ILO). In the European Union alone, hundreds of millions of workdays are lost every year as a result of poor working conditions, which is disturbing, not only due to the human loss they represent, but also because of its average impact which is estimated of 4% on gross domestic product (Freitas, 2016; Leão, Costa, Costa, & Arezes, 2018).

MAIN FOCUS OF THE CHAPTER According to Lima (2004), the transnationalization of economic and social relations has commanded profound changes in the organization of labour processes, imposed by the increasing productivity and costs reduction, which are not usually accompanied by improvements in working conditions. Indeed, in order to cope with the flexibility of markets and of labour itself imposed by globalization, Companies resort to atypical forms of labour, so as to adjust the quantity and availability of labour to market imperatives, which, to a certain extent, accentuate the workers’ insecurities and lead to the loss of expectations, given their uncertainties in the performance of their functions, causing the decay of their ability to deal with the unexpected. Indeed, globalization has prompted the Industry 4.0 paradigm, whereby to Companies are compelled to evolve beyond mass production and thrive through more advanced Manufacturing, leading Companies to implement several technological solutions and process automation (Bragança, Costa, Castellucci, & Arezes, 2019; Colim, Costa, Cardoso, Arezes & Silva, 2019; Leão et al., 2018). This technological revolution aims to make industrial production more efficient, more flexible and of higher quality through the adoption of smart technology. These trends will predictively affect the work organization, the way it is carried out, and certainly the Health and Safety of workers (Colim et al., 2019; Rodrigues, 2006). The swift progress of information and communication technology (ICT) and internet of things (IoT) have laid the groundwork for making the adoption of new technologies by Manufacturing Companies (including automated Manufacturing systems) possible. However, for the Companies to thrive, they must keep up with technological development. For instance, even though technology has enabled Companies to gather a bulk of data regarding production and the workers themselves, the pace of the big data-supporting technologies is not keeping up with the Companies’ needs, so Companies are, on the one hand, striving to make sense out of the amount of data they are collecting and, on the other hand, they are worried about keeping that data secured, by preventing cyber-attacks. One can only imagine the impact of a cyber-attack on a Company with advanced cyber-physical systems that closely work and cooperate with Human workers. And not all Companies can afford state-of-the-art technology. Even though it is generally seen as a legal obligation, industrial Safety can actually have many benefits, including the assurance of regulatory compliance and increased productivity. There are several occupational Safety and Health systems, which ensure the correct identification of hazards and the occupational risks thorough assessment and management. It is important to systematize the process of Risk Management, by identifying the conditions of all machines and work tools, the nature of work and 1770

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the production process so that any latent or manifest hazards, which may cause harm to workers, impact sustainability or otherwise compromise the integrity of the Company and its workers can be noticed and acted upon (Lima, 2004). Indeed, it has been proven that careful, rigorous and systematic risk assessment is one of the best prevention strategies to reduce the work-related accident occurrence (Colim et al., 2019; INE, 2019). The issue (which Companies have also been struggling with and sharing their concern) is that the work paradigm is undergoing a process of profound change at such a rapid pace that the efforts that have been made by Companies have not been fast enough nor efficient enough to for them cope. Automated solutions (robots) have been resorted to increase production and release the worker from more difficult and hazardous tasks, according to the ILO. Automation in Manufacturing context replaces, to some extent, cognitive and physical human labour. Since automated Manufacturing systems are perceived to be efficient, automation is often viewed as a tool that can potentially enhance Manufacturing competitiveness (Colim et al., 2019 citing Salim & Johansson, 2018). This provides many bottom-line benefits for manufacturers, but they are also inherently dangerous and it is the responsibility of manufacturers to ensure a safe production environment for workers (Colim et al., 2019). The lack of resources available and poor education regarding the cost-benefit relationship of investing in Health and Safety prevention strategies which, in turn, cause constraints in complying with OSH regulations, have been associated with the occurrence of accidents (Leão et al., 2018, citing Giaccone, 2010). Indeed, a study by Moktadir, Ali, Kusi-Sarpong and Shaikh (2018) focused on the Bangladeshi leather industry showed that the most pressing challenge that could hinder the implementation of Industry 4.0 was the “lack of technological infrastructure”. Sustainability may also profit greatly from Industry 4.0, as this progress may entail opportunities for manufacturers to protect and control environmental impacts using smart technology, which can be developed via ICT and IoT (Moktadir et al., 2018). But it would be reductive to think of sustainability constrained to as looking out for the environment. As Dyllick and Hockerts (2002) put it: “such a reduction misses several important criteria that firms have to satisfy if they want to become truly sustainable”. In its full sense, true sustainability refers to a concomitant avocation of efficient ecological, economic and social causes in a determined time frame. In reality, corporate sustainability is developed by satisfying 6 criteria: eco-efficiency, socio-efficiency, eco-effectiveness, socio-effectiveness, sufficiency and ecological equity. So, sustainable Companies do not just manage financial capital, they also manage natural, human, and social capital. (Dyllick & Hockerts, 2002). Nowadays, at a global level and from the business point of view, sustainable development means adopting business strategies and activities that meet the needs of the enterprise and its stakeholders’ while protecting, sustaining and enhancing the human and natural resources that will be needed in the future (IISD, 2019). The influence of an Organization’s culture on Safety outcomes can be as weighty as the Safety Management System. A positive culture includes mutual trust, shared perceptions and confidence. ‘Safety Culture’ is a subset of the overall Company culture. Even though many Companies refer to ‘Safety Culture’ as the tendency of their employees to comply or not with the rules or act (or not) safely, the culture and the style of management may be even more significant. Generalized, routine procedural violations, failure to comply with the Company’s own Safety Management System and Management decisions that consistently prioritize production or cost before Safety are symptoms of poor cultural factors. The key aspects of an effective culture are (HSE, 2019): Management commitment, which produces higher levels of motivation and concern for Health and Safety throughout the organization, being the active involvement of senior management in the Health and Safety system considered as very important; 1771

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Visible management, whereby managers need to be seen and to lead by example when it comes to Health and Safety, being open to talking about Health and Safety and visibly demonstrating their commitment through their actions (a great example of commitment is to show a willingness to stop production to resolve issues). Employees will generally assume that they are expected to put commercial interests first, causing Safety initiatives or programmes to be undermined by cynicism if Management is perceived as not sincerely committed to Safety; Good communication throughout all hierarchical levels of employees, with questions about Health and Safety being part of everyday work conversations, and having Management actively listening to what they are being told, and taking it seriously; Active employee involvement in workshops, risk assessments, risk analyses and other, because participation in Safety is important for workers to build ownership of Safety at all levels of the hierarchy and because the Company can truly benefit from the unique knowledge that each worker has of their work; Inspection, including non-threatening interviews with a suitable cross-section of the Company, in a sample size large enough to take account of differing views and experience. In 2017, the ILO, the Finnish Ministry of Social Affairs and Health, the Finnish Institute of Occupational Health, the Workplace Safety and Health Institute of Singapore, the International Commission on Health at Work and the European Agency for Safety and Health at Work (EU-OSHA) undertook a major project regarding the estimation of costs and benefits of OSH. Findings showed that work-related injuries and illnesses amounted to a loss of 3.3% of the European gross domestic product (GDP), the equivalent of € 476 billion every year, while worldwide, the societal costs totalled 3.9% of GDP, which corresponded to an annual cost of about € 2,680 billion. The percentage of the societal costs were shown to vary widely between countries, particularly between western and non-western countries, depending on the characteristics of the industry, the legislative context and the incentives for prevention. The report also showed that work-related diseases accounted for 86% of all work-related deaths worldwide and 98% of deaths in the European Union (EU). Work-related injuries and illnesses caused a total of 123.3 million ADLs (disability-adjusted life years) to be lost worldwide, 7.1 million of which totalled solely in the EU. Of this, 67.8 million (3.4 million in the EU) were deaths and 55.5 million (3.7 million in the EU) were disabilities. In most European countries, work-related cancer was responsible for the largest share of the costs (€ 119.5 billion or 0.81% of EU GDP), with musculoskeletal problems taking second place (Heuvel et al., 2017). Risk Management is part of the overall Management System. As a risk reduction and Risk Management tool, it always aims for optimization. There are several aspects that motivate a systematized Risk Management: a) the increased pressure on Companies (to perform better, to be more efficient, more sustainable); b) the need to preserve workers’ health (healthier workers are more productive, miss less working days, cost less and the absence of accidents has a good impact on the Company’s image; c) the need to increase protection of heritage; d) the reduction of insurance payment costs and; e) the assignment of Safety and trust to processes and procedures. According to EU-OSHA, poor occupational Safety and Health costs Companies’ money, but a good level of OSH has advantages. Companies with higher occupational Safety and Health standards are more successful and more sustainable. Recent estimates show that for every euro invested in OSH there is an average return of 2.2 euro (ranging between 1.29 euro and 2.895 euro) and that the cost-benefit of enhancing occupational Safety and Health is favourable (European Commission, 2014, citing BenOSH).

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Indeed, it may be an overwhelming task taking into account not only the variables that need to be considered but also the responsibility of the task, given the impact of the undesirable consequences. Hence the importance of standardized Management Systems. The gold standard for Occupational Health and Safety Management is ISO 45001. ISO 45001:2018 specifies the OSH Management systems requirements for Companies to proactively improve their OSH performance. It is intended as a tool to help establish and improve the working environment in Health and Safety, to prevent accidents and, in many cases, to exceed current legislation, through continual improvement, systems’ performance analysis and short term action plans and long term strategies for OSH. Developed by a committee of occupational Health and Safety experts, this standard follows other generic Management System approaches such as ISO 14001 and ISO 9001. It was based on earlier international standards in this area such as OHSAS 18001, the International Labour Organization’s ILO-OSH Guidelines, various national standards and the ILO’s international labour standards and conventions. As part of the ISO family, ISO 45001 will enable organizations to enjoy a more globally recognized technical standard (ISO, 2018). The main changes introduced by ISO 45001 are, as follows: • • •

• • •

Business Context: Chapter 4.1, External and Internal Issues, introduces new clauses for the systematic determination and monitoring of the business context; Workers and Other Stakeholders: Chapter 4.2 introduces a focus on the needs and expectations of workers and other stakeholders and worker involvement. Systematically identifies and understands the factors that need to be managed within the Management System; Risk and Opportunity Management: Chapters 6.1.1, 6.1.2.3, 6.1.4, companies should determine, consider and, where necessary, take action to address any risks or opportunities that may impact (positively or negatively) the ability Management System to deliver desired outcomes, including improved workplace Health and Safety; The commitment of leadership and Management: Chapter 5.1, ISO 45001 emphasizes the need for the active involvement of senior management and the assumption of responsibility for the effectiveness of the Management System; Objectives and Performance: Enhanced focus on objectives as drivers for improvement (Chapters 6.2.1, 6.2.2) and benchmarking (Chapter 9.1.1); Extended Requirements Related to: ◦◦ Participation and consultation (5.4); ◦◦ Communication (7.4): More prescriptive about “mechanisms” of communication, including determining what, when and how to communicate; ◦◦ Purchases, including outsourcing, and contractors (8.1.4).

Figure 1 shows all the ISO 45001:2018 requirements for establishing an occupational health and safety management system (OH&SMS). Compared to its predecessors, ISO 45001 presents a new level of sophistication, raising workplace Safety to a more strategic position. More importantly, it strengthens worker participation, by focusing on the needs and expectations of workers and stakeholders, as well as their involvement in the continual process of improvement. It also shows an increased emphasis on risk-based thinking, as it is closely related to the active commitment of top management and context analysis. Organizations should determine and take appropriate action to address any risks or opportunities that could positively or negatively impact 1773

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the Management System’s ability to deliver intended outcomes, including improving on-site OSH. Leadership is paramount for success: top management needs to be committed and actively involved and is responsible for the effectiveness of the Management System. Top management should be a leader, not a boss. There is also a special focus on monitoring outsourced activities and outside personnel concerning Safety and Health at work; they are considered part of the organization. Figure 1. ISO 45001:2018 requirements for an occupational health and safety (OH&S) management system

Surely, ISO 45001 allows users to systematically identify and understand the factors that need to be managed through the Management System. As part of the global Management System, OSH management systems provide a framework for managing OSH risks and opportunities, by setting management actions and procedures to be implemented by a Company, within a framework of definition of responsibilities, technical and financial means, aiming at the elimination or reduction of risks arising from the Company’s activities, products or services. The OSH Management System approach applied in ISO 45001 is founded on the concept of Plan-Do-Check-Act (PDCA). It can be applied to the Management System and each of its elements, as follows (ISO, 2018): a) Plan: determine and assess OSH risks, OSH opportunities and other risks and other opportunities, establish OSH objectives and processes necessary to deliver results following the organization’s OSH policy;

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b) Do: implement the processes as planned; c) Check: monitor and measure activities and processes concerning the OSH policy and OSH objectives, and report the results d) Act: take actions to continually improve the OSH performance to achieve the intended outcomes. PDCA highlights the iterative nature of Risk Management. The importance of leadership and worker participation is, once again, emphasized in ISO 45001, as shown by the incorporation of the PDCA concept into a novel framework. By implementing the standard, companies undertake to produce proof documents which evidentiate the procedures and processes that follow from the implementation of requirements and sub-requirements, as well as the necessary records that also validate their compliance with the latter and through which companies ensure the operation, maintenance and continual improvement of the management system. Figure 2 depicts a mapping of the mandatory documents (MD), mandatory reports (MR) and currently used but not mandatory documents (CUD), for a more comprehensive understanding of how these mandatory and recommended documents relate to the standard’s requirements and sub-requirements. The mandatory documents (MD) include the Scope of the OH&S management system, regarding subrequirement 4.3 - Determining the scope of the OH&S management system; the OH&S policy, relative to sub-requirement 5.2 - OH&S policy; the Responsibilities and authorities within the OH&SMS, referring to sub-requirement 5.3 - Organizational roles, responsibilities and authorities; the OH&S process for addressing risks and opportunities, which addresses sub-requirement - 6.1.1 Actions to address risks and opportunities: General; the Methodology and criteria for assessment of OH&S risks, concerning sub-requirement 6.1.2.2 - Assessment of OH&S risks and other risks to the OH&S management system; the OH&S Objectives and plans for achieving them, in respect of sub-requirement 6.2.2 - Planning to achieve OH&S objectives and; the Procedure for emergency preparedness and response, with regard to sub-requirement 8.2 - Emergency preparedness and response. Mandatory records include the OH&S risks and opportunities and actions for addressing them, in connection with sub-requirement 6.1.1 – Actions to address risks and opportunities: General; the Legal and other requirements, related to sub-requirement 6.1.3 - Determination of legal requirements and other requirements; the Evidence of competence, concerning sub-requirement 7.2 – Competence; the Evidence of communications, with regard to sub-requirement 7.4.1 – Communication – General; the List of external documents, referring to sub-requirement 7.5.3 - Control of documented information; the Plans for responding to potential emergency situations, relative to sub-requirement 8.2 - Emergency preparedness and response; the Results on monitoring, measurements, analysis and performance evaluation and the Maintenance, calibration or verification of monitoring equipment, both addressing sub-requirement 9.1.1 - Monitoring, measurement, analysis and performance evaluation: General; the Compliance evaluation results, in respect of sub-requirement 9.1.2 - Evaluation of compliance; the Internal audit program and Internal audit results, one and the other regarding sub-requirement 9.2.2 - Internal audit programme; the Results of management review, linked to sub-requirement 9.3 - Management review; the Nature of incidents or nonconformities and any subsequent action taken and the Results of any action and corrective action, including their effectiveness, the twain related to sub-requirement 10.2 - Incident, nonconformity and corrective action and; the Evidence of the results of continual improvement, which addresses sub-requirement 10.3 - Continual improvement.

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Figure 2. Mapping of the mandatory and common use documentation for the establishment, implementation and maintenance of an OH&S Management System according to ISO 45001:2018

In addition to the mandatory documentation, there are documents which refer to requirements that do not necessarily materialize in the documentation, but whose documentation may be useful for the company in establishing, implementing, and maintaining the OH&S management process. These documents are commonly used but not mandatory (CUD) and include: The OH&S Manual, addressing require-

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ment 4 – Context of the Organization; the Procedure for Determining Context of the Organization and Interested Parties, related to sub-requirement 4.1- Understanding the organization and its context; the Procedure for Consultation and Participation of Workers; referring to sub-requirement 5.4 - Consultation and participation of workers; the Procedure for Hazard Identification and Assessment, addressing sub-requirement 6.1.2.1 – Hazard Identification; the Procedure for Communication, in connection to sub-requirement 7.4.1 – Communication: General; the Procedure for Document and Record Control, linked to sub-requirement 7.5 – Documented Information; the Procedure for Operational Planning and Control, concerning sub-requirement 8.1 - Operational planning and control; the Procedure for Change Management, with respect to sub-requirement 8.1.3 - Management of change; the Procedure for Internal Audit, in conformity with sub-requirement 9.2 – Internal audit; the Procedure for Management Review, in accordance with sub-requirement 9.3 – Management review; the Procedure for Incident Investigation and the Procedure for Management of Nonconformities and Corrective Actions, the two aiming the sub-requirement 10.2 - Incident, nonconformity and corrective action. The concept of Safety Culture has received increasing attention from Companies. As defined by the ACSNI Study Group on Human Factors (1993), Safety Culture is “the product of individual and group values, attitudes, perceptions, competencies, and patterns of behaviour that determine the commitment to, and the style and proficiency of, an Organization’s Health and Safety Management” being Companies with a favourable Safety Culture characterized by “communications founded on mutual trust, by shared perceptions of the importance of Safety and by confidence in the efficacy of preventive measures”. Well, this definition seems to be one motivating factor for the new framework of the standards, which highlights the importance of the communication between leaders and workers, and by encouraging the participation of both parties in the decision-making process. It makes leaders commit to tangible Safety goals, all the while bringing the worker into the discussion. And this has a two-fold effect. One, because the worker took part in the decision process, the worker feels that his/her opinion matters. Simultaneously, the worker has inside knowledge of the effort that the Company is putting on his/her Safety, so he/she feels imbued with the Safety Culture. Two, because the worker took part in that decision process, he/ she is compelled to act righteously, respecting the Safety procedures and the instructions. So, this has a tremendous impact on workers and, hence, on the Company´s performance itself. And that is why a Company’s Safety Culture of a Company could also be greatly improved by resorting to standardization.

SOLUTIONS AND RECOMMENDATIONS Competitive Companies today are striving to become more efficient at various levels (quality, environment, Safety, sustainability) and use certification to support them in successful ventures, but also as an emblem that shows their stakeholders that they are doing so diligently, in accordance with the established parameters and standards, as a guarantee of their quality in the various areas. Most Companies started with Quality Management Systems certification (ISO 9001 series), then Environment Management Systems certification (ISO 14001 series), followed by Safety Management Systems certification. So, certification processes started to become too complex and laborious for Companies. Then, a homogenization of the certification systems aimed at making life easier for Companies by simplifying the certification paperwork arose, with the Integrated Management System of Quality, Environment and Safety (IMS-QES).

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The Integrated Management System of Quality, Environment and Safety (IMS-QES) benchmarks share the same dynamic cycle of continual improvement and the same fundamentals that contribute to the organization’s competitive positioning, namely: customer orientation (whether internal or external); leadership commitment; knowledge and practical application of the legal requirements (and other); prevention (of defects, incidents and accidents); involvement of all employees (including suppliers); ensuring proper training and education; performance regulation with procedures and work instructions; monitoring the systems to ensure their suitability; correction of deviations; considering suppliers in the internal processes; continuously improve performance. Furthermore, all three ISO 9001, ISO 14001 and ISO 45001 are based on a High-Level Structure, making them consistent with each other ISO Management System standards ISO 9001 (Quality) and ISO 14001 (Environment), both of which were updated in 2015. The high-level structure distributes clauses in 10 sections, aligned with the PDCA approach, to give logical sequence to management system requirements and proposes common text for very stable Management System requirements: scope, normative references, terms and definitions, context of the organization, leadership and worker participation, planning, support, operation, performance evaluation and improvement. The Plan-Do-Check-Act (PDCA) cycle, which is a cycle for improvement that was originated and made popular by two of modern quality control’s gurus, Walter Shewhart and Edward Deming (which is why PDCA is also called Deming cycle), is used when implementing a change to improve a process. All three: Quality, Environment and Safety Management Systems approach follow the PDCA, which facilitates the process, not only of the individual implementation of the systems but also their integration. And it seems reasonable to think that PDCA will, as well, be proven useful for upcoming Industry 4.0 management challenges. Quality, Environment and Safety Management System standards (ISO 9001, ISO 14001 and ISO 45001) are published following the standard of Annex SL, which facilitates the integration of the standards, reducing implementation costs and compliance. The high-level structure, terms and common text set out in Annex SL of the ISO Directives were the means found to ease the application complexity, providing a foundation that facilitates the development and adoption of Management System standards, making them easier to read and interpret by the users and facilitating the integration of Management Systems in Organizations. Even though the High-level structure cannot be changed, it begins with the generic requirements of Annex SL and becomes more specific as needed for applications in particular sectors. Thus, for different areas, it is possible to add sub-clauses and text specific to each area. And this is very advantageous! This is very valuable for companies that are striving to manage Industry 4.0 challenges because it provides them with an editable platform for creating an auditable reference, in line with one that has proven extremely useful and efficient and that fits the Company´s needs. Indeed, issues may differ (or rather evolve), but the way they are managed may be the same. The gold standard for Risk Management is ISO 31000 (last revised in 2018), a document designed to support leaders who create and protect value in organizations by managing risks, making decisions, defining and achieving objectives and improving performance. Organizations of all types and sizes face external and internal factors and influences that make it difficult to achieve their goals. Despite ISO 31000 cannot be used for certification purposes, it provides direction for internal or external audit programmes. Organizations using it can compare their Risk Management practices with an internationally recognized benchmark, providing sound principles for effective Management and Corporate Governance. Risk Management is, too, an iterative process, one that involves all activities associated with an Organization and includes interaction with stakeholders, and takes into account both the external and 1778

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internal context of the Organization, including human behaviour and cultural factors, helping Organizations define strategies, reach goals and make informed decisions. So, Risk Management is part of governance and leadership and is critical to how the Organization is managed at all levels, for it contributes to improving all Management Systems. According to ISO 31000, Risk Management shall be based on the principles, structure, and process described in it, which may already exist (fully or partially) in the Company, however, they may need to be adapted or enhanced to manage risk in an efficient, effective and consistent manner. The Risk Management process shall involve the systematic application of policies, procedures, practice in communication and consulting activities, setting the context and: assessing, treating, tracking, reviewing, registering and reporting risks. As principles, the standard establishes that the Risk Management structure and process are personalized and proportionate to the external and internal context of the organization, related to its objectives; and that risks may arise, change or disappear as changes in the external context and within an Organization occur; it also affirms that Risk Management must anticipate, detect, recognize and respond to these changes and events in an appropriate and timely manner and; reinforces that appropriate and timely stakeholder involvement allows their knowledge, viewpoints and perceptions to be considered, leading to better awareness and informed Risk Management itself. ISO 31000 recognizes the importance of human factors and Company Culture, stating that they significantly influence all aspects of Risk Management at every level and stage. Another important principle of this standard is that the inputs to Risk Management are based on historical and current information, but may also be based on future expectations. Risk Management is seen by the standard as transversal to all organizational activities and, because it establishes a structured and comprehensive approach to Risk Management, it contributes to consistent and comparable results. In short, ISO 31000 can (must!) be an integral part of management and decision making and integrate the structure, operations and processes of the Organization. Given its ability to be applied at several levels (strategic, operational, program or project levels) and its flexibility (there may be many applications of the Risk Management process within an Organization tailored to achieve objectives and to suit the external and internal context in which they are applied), this means that competitive Companies striving with the challenges that Industry 4.0 is bringing along should already be making use of this tool to manage Safety in an integrated manner, addressing the future challenges and dealing with the anticipated risks early on. If Risk Management is the common way of performing risk monitoring, whereby the installation, personnel, assets and outcomes are protected against adverse consequences of risks and the severity and uncertainty of loss is decreased through planning, coordination and control of activities, assets and resources to minimize the impact of uncertain events, the new industrial revolution imposed challenges may be seen as sources of novel hazards that have to be managed. Thus, big data, cyber-physical systems and other industrial automation and all other disruptive technology that make up Industry 4.0 have to be seen in the perspective of Risk Management and dealt accordingly, by taking advantage of the proven efficient and advantageous inherited structure of standardization, thus tackling Industry 4.0 upcoming challenges by leveraging industrial Occupational Safety with standardization. This approach will enable Companies to smoothly adjust to the new work paradigm brought by Industry 4.0. The document that assists in the implementation of ISO 31000:2009 by any public, private or community company, association, group or individual is ISO/TR 31004:2013. This standard provides a structure for organizations to follow and swiftly adapt their risk management framework to be consistent with ISO 31000 and coherent with the organization’s needs, the disambiguation and clarification of the essential concepts of ISO 31000 and orientation on the risk management principles and framework particularities 1779

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presented in ISO 31000:2009. Because it is not specific to any trade, industry or sector, nor is it specific to any type of risk in particular, ISO/TR 31004:2013 can be applied to all activities and to all parts of organizations. Public, private or community companies, associations, groups or individuals who are already consistent with ISO 31000:2009 but aim at the continual improvement of their framework and their management risk process as recommended in ISO 31000:2009, 4.6 and 5.6., may also benefit from the ISO 31004:2013 structured approach. The effective tools for risk analysis can be retrieved from ISO/IEC 31010 (last revised in 2019). Although this standard does not deal specifically with Safety, it provides guidance on the selection and enforcement of risk assessment techniques in a broad array of contexts, designed to assist in the decisionmaking processes where there is uncertainty, to convey information about particular risks and as part of a process for managing risk. For such purpose, the standard provides summaries of those techniques and also refers to other documents where they can be found more fully described. There are several ways of classifying different risk assessment techniques. This classification is useful for nimbly grasping each technique’s relative strengths and weaknesses, considering that the standard provides an overview of over 30 techniques. One shrewd classification of the risk assessment techniques is based on their applicability to each step of the risk assessment (strongly applicable, applicable and not applicable): • • • • • •

Risk identification; Risk analysis - consequence analysis; Risk analysis - qualitative, semi-quantitative or quantitative probability estimation; Risk analysis - assessing the effectiveness of any existing controls and; Risk analysis - estimation the level of risk or risk assessment techniques; Risk evaluation.

Indeed, the standard provides a comparative table for the different techniques. It immediately informs the user of the level and type of risk analysis that is possible to entail with that particular tool. The numerous methodologies and techniques available present different levels of robustness and fragility, due to their specificity. They should not be used indiscriminately. The choice of the risk assessment technique will, hence, depend on the complexity of the problem and the methods required to analyze it, the nature and degree of uncertainty of the risk assessment (which will depend on the information at disposal and on what is necessary to satisfy the objectives), the quality of the resources (time available, level of expertise, data needs or cost), and whether or not the method can provide a quantitative output. Considering that risk assessment is the global process of risk identification, risk analysis and risk evaluation, the methods and techniques used to perform risk assessment will, therefore, influence the risk assessment process, as will the context itself. The “risk identification” concept refers to the process of finding, acknowledging and recording risks, with the purpose of identifying weaknesses within the organization that may hinder the accomplishment of their proposed objectives (what is already happening that should not be happening and what may happen). By acknowledging their existence, the organizations are able to develop tools to tackle them. Whenever possible, the found risks should be eliminated. Otherwise, they should be mitigated, preferably, or at least, controlled. The existing controls may include design features, people, processes and systems. The risk identification process involves finding the cause(s) and origin(s) of the risk, which may be an event, a situation or a circumstance. Examples of risk identification methods include check-lists (data evidence based method) and Hazard and Operability studies (HAZOP, which is an inductive reasoning 1780

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technique). These methodologies may be aided by other supporting techniques such as Brainstorming and Delphi methodology, to improve the rigor of the risk identification process. The “risk analysis” process is about understanding the risk. It consists of determining the consequences and their probabilities for the identified risks, considering the presence (or absence) of the existing controls and their effectiveness. The level of risk is given by the combination between the severity of the consequences and their probabilities. There are several methods which can be applied in the risk analysis, but whenever there are more complex situations, more than one technique may be required. The “risk evaluation” concept stands for the understanding the significance of the estimated levels of risk (obtained during risk analysis) according to the risk criteria established. This evaluation, together with ethical, legal, financial and other considerations specific to the context will allow for deciding on future actions. The different methods can still be classified as qualitative, quantitative and semi-quantitative. Qualitative methods consist of systematic examinations carried out in the workplace to identify situations that can cause harm to people. Quantitative methods involve the objective quantification of the different elements of risk, namely Probability and Severity of Consequences. When the assessment carried out by the qualitative methods becomes insufficient to achieve adequate risk valuation and the underlying complexity of quantitative methods does not justify the cost associated with its application, semi-quantitative methods are resorted to. For qualitative methods, an assessment of individual scenarios is generally used, estimating the different risks based on answering questions such as “What if…?”, conducting qualitative assessments of Severity and Probability, without any associated numeric record. This type of methods is appropriate for assessing simple situations, whereby hazards can be easily identified by observation and compared with principles of good practice that exist for similar circumstances. Examples of qualitative risk assessment methods include HAZOP, Preliminary Hazard Analysis (PHA), Failure Mode and Effect Analysis (FMEA), Check-lists, Cause-and-Effect Analysis and Delphi technique. HAZOP is a general process of hazard identification where possible deviations from the expected or intended performance are identified. It should be noted that hazard is the term used for risk in the Safety context. Through a guideword based system, HAZOP allows for assessing the criticalities of the deviations. Originally developed as a more straightforward alternative to HAZOP, the Structured “What-if” Technique (SWIFT) is a systematic, teambased study, whereby a set of pre-made words or phrases is used to stimulate participants to identify risks. Resorting to standard ‘What-if’ phrase beginning, combined with the prompts, the participants investigate how a system, organization, process, worker (or any other element that makes up the specific working context) would be affected by deviations from normal operations and behaviour. The PHA is an inductive method of analysis aimed at identifying the hazards (and hazardous situations and circumstances) which may be prejudicial to an activity, facility or system. Failure modes and mechanisms, and their undesired effects may be identified by the FMEA technique. This technique derives some variants such as Design FMEA, System FMEA, Process FMEA, Service FMEA and Software FMEA. A Criticality Analysis may be performed after the FMEA, whereby the significance of each failure mode, qualitatively, semi-qualitatively, or quantitatively (FMECA) may be defined. Therefore, FMEA is technique that may, actually, provide a quantitative output. The Delphi technique is often mistaken by Brainstorming, but these are not synonyms. Brainstorming is a technique where well-informed participants discuss the identification of potential failure modes and related hazards, decision criteria and/or options for treatment, in a stimulating and encouraging free-flowing conversation environment with effective facilitation. Whereas the Delphi technique is a collaborative tool for building 1781

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consensus among experts, having experts express their opinions in an individual and anonymous manner all the while having access to the other experts’ considerations as the process evolves. Expert opinions may, hence, support the source and influence identification, probability and consequence estimation and risk evaluation. For performing a Cause-and-Effect analysis, a tree structure or fishbone diagram is built based on the effect that needs to be studied (e.g., an undesired event). The effect (head of the fish) may have a several contributory categories (larger fish bones) of causal factors (smaller fish bone ramifications). Contributory factors are oftentimes identified through Brainstorming. The Check-lists are, probably, the best-known, most ubiquitous risk identification tool. Their success may be due to their simplicity and ease of use; given that they consist of lists of typical uncertainties which need to be considered. There are several pre-developed check-lists available, which may also contribute to their favoritism. Quantitative methods assign a numerical value to the magnitude of the risk, using sophisticated calculation techniques that integrate data on the behaviour of the variables under analysis to determine the probability, by determining a pattern of regularity in the frequency of certain events and quantifying the severity, by resorting to mathematical models of consequences. These methods are particularly useful in cases of high risk or greater complexity (e.g., nuclear industry). Fault Tree Analysis (FTA), Event Tree Analysis (ETA) and Bow Tie are examples of quantitative methods. The FTA is a technique which focuses an undesired event (top event) and, beginning with that top event, determines all the events that can lead to it, displaying the relationship between the several events graphically in a logical tree diagram. Once the fault tree is built, measures have to be considered to mitigate or eliminate potential causes and sources of those events. Resorting to inductive reasoning, ETA takes the eventual initiating events and transforms them into probabilities of possible outcomes. The Cause/Consequence Analysis is a result of the combination of Fault Tree Analysis and Event Tree Analysis, which includes the time delays. The Bow Tie analysis consists on the elaboration of a diagram that displays the pathways from the risk causes to its consequences. It can be thought of as a combination of the FTA – in what concerns to the cause of an event (which is represented by the knot of a bow tie) – and the ETA, regarding the consequences. Indeed, Bow Tie diagrams can be built based on pre-existing FTA and ETA. The resulting Bow Tie diagram presents not only the several causes of one risk and the several consequences of that same risk, but also the barriers between the causes and the risk, and the barriers between risk and consequences, which are the major object of analysis of this tool. LOPA, which may also be called barrier analysis, is a semi-quantitative method for estimating the risks associated with an undesired event or scenario. It considers the existing controls and their effectiveness in the estimation, as it evaluates whether there are sufficient measures to control or mitigate the risk. In this technique, a cause-consequence pair is selected. Afterwards, the layers of protection that stand between one and the other are identified. Finally, the computation of the order of magnitude is performed to assess whether the protection is adequate to reduce risk to a tolerable level. For the application of several semi-quantitative methods, it is necessary to build hierarchy scales for Probability, Severity and Risk Index. The estimation of the numerical value of Occupational Risk Magnitude (R) is computed from the product between the estimation of the Probability of Risk (P) materialization and the expected Severity (S) of the injuries. Risk matrices are semi-quantitative risk assessment methods par excellence (e.g., William Fine). Check-lists and PHA are “Look-Up Methods”, whereas Delphi, Brainstorming and SWIFT are “Supporting Methods”. “Scenario Analysis” include FTA, ETA, Cause/Consequence Analysis and Causeand-Effect Analysis. FMEA, FMECA and HAZOP are “Function Analysis” tools, and LOPA and Bow 1782

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Tie analysis are “Controls Assessment” tools. More complex, “Statistical Methods” include Markov Analysis, Bayesian Analysis and Monte Carlo Analysis. To prevent ambiguity and misuse of concepts (which would make it difficult to read and compare risk assessments from different sources), ISO/IEC Guide 73:2009 (last revised and confirmed in 2016) provides the definitions of generic terms related to Risk Management.

FUTURE RESEARCH DIRECTIONS Industrial revolutions have been occurring at ever-smaller time steps. Although researchers are currently discussing all that Industry 4.0 brings and all the changes that it will encompass, it is necessary to think even further ahead, as changes are expected to be more and more frequent. It would be important in the future to create evolutionary tools that would adapt as needed, i.e., that keep up with the state-ofthe-art industrial technologies, enabling managers to deal systemically with industry challenges in an agile manner. The authors are working closely with several manufacturing companies and gathering knowledge regarding their intervention needs in what relates to the strategies for companies to evolve so as to keep up with this industrial revolution. In a potential risks anticipation approach, authors will focus on pre-establishing measures to be implemented so that the risks are avoided or, if not possible, widely mitigated. A multidisciplinary team of specialists from several areas of knowledge (Ergonomics and Human Factors Engineering, Electronics and Computer Engineering, Systems Engineering and Psychologists) is to be established so that the most effective path is taken, while accounting for all the requirements (including technological, ergonomic, productive, corporate and sustainability requirements). To this end, the authors will rely on the standard references for risk management, resulting in a systematic and comprehensive approach that is believed to leave little to chance.

CONCLUSION Industry 4.0 is an incipient concept, but major changes can be foreseen that will impact across the Industry and (also) services. From the way the work is executed to the way Companies will be managed at a macro level, the changes that this upcoming industrial revolution yields are significant. Thus, it makes sense that new ways of analyzing, assessing and managing the risks arising from this new paradigm be debated beforehand and implemented so that Companies are able, in the future, to maintain competitiveness in a sustained and sustainable manner, while benefiting from what the state-of-the-art technology brings to the environment. The Risk Management process shall involve the systematic application of policies, procedures, practice in communication and consulting activities, setting the context and: assessing, treating, tracking, reviewing, registering and reporting risks. In truth, Risk Management can be a daunting task, even in stabilized work paradigms. When, besides, one has to tackle an industrial revolution and remain competitive among one’s peers, this task takes on a whole new magnified dimension. One good strategy to swiftly respond to all the upcoming demands is to rely on standardization, taking advantage of proven efficient and advantageous inherited structures, thoroughly managing each of the systems (areas) that compose the management of the Organization individually, according to a proven quality model, while a concomitant macro-management of the Company is carried out, owing to the ease of

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integration allowed by the inherent flexibility of the standards here presented, which allow for creating and editing an auditable reference that fits the Company´s needs.

ACKNOWLEDGMENT This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.

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Health and Safety Executive (HSE). (2019). Common Topic 4: Safety Culture. Retrieved from http:// www.hse.gov.uk/humanfactors/topics/common4.pdf IISD. (2019). International Institute for Sustainable Development, Business Strategy for Sustainable Development. Retrieved from https://www.iisd.org/business/pdf/business_strategy.pdf ILO. (2019). International Labour Organization. Retrieved from https://www.ilo.org/global/lang--en/ index.htm INE. (2019). Instituto Nacional de Estatística, Statistics Portugal. Retrieved from https://www.ine.pt/ ISO. (2009). ISO/IEC 31010:2009. Risk Assessment Techniques. International Organization for Standardization. Geneva-Switzerland. ISO. (2009). ISO/IEC Guide 73:2009. Risk Management – Vocabulary. International Organization for Standardization. Geneva-Switzerland. ISO. (2018). 31000:2018. Risk Management – Guidelines. ISO/TC, 262. International Organization for Standardization. Geneva, Switzerland. ISO. (2018). 45001:2018. Occupational Health and Safety Management Systems – Requirements with Guidance for Use. International Organization for Standardization. ISO. (2019). International Organization for Standardization. Retrieved from https://www.iso.org/iso45001-occupational-health-and-safety.html Lampropoulos, G., Siakas, K., & Anastasiadis, T. (2019). Internet of Things in the Context of Industry 4.0: An Overview. International Journal of Entrepreneurial Knowledge, 7(1), 4–19. Leão, C. P., Costa, S., Costa, N., & Arezes, P. (2018, October). Capturing the ups and downs of accidents’ figures–the Portuguese case study. In Proceedings of the International Conference on Human Systems Engineering and Design: Future Trends and Applications (pp. 675-681). Springer. Lima, T. M. (2004). Trabalho e Risco no Sector da Construção Civil em Portugal: Desafios a uma cultura de prevenção. Oficina do CES, 211, 1–13. Miguel, A. S. (2014). Manual de Higiene e Segurança no Trabalho. Porto, Portugal: Porto Editora. Moktadir, M. A., Ali, S. M., Kusi-Sarpong, S., & Shaikh, M. A. A. (2018). Assessing challenges for implementing Industry 4.0: Implications for process safety and environmental protection. Process Safety and Environmental Protection, 117, 730–741. doi:10.1016/j.psep.2018.04.020 Rodgers, G., Lee, E., Swepston, L., & Van Daele, J. (2009). The International Labour Organization and the quest for social justice, 1919-2009. Book Samples, 53. Retrieved from https://digitalcommons.ilr. cornell.edu/books/53 Rodrigues, C. (2006). Higiene e Segurança do Trabalho – Manual Técnico do Formador. Braga, Portugal: Nufec - Núcleo de Formação, Estudos e Consultoria. Salim, R., & Johansson, J. (2018). Automation decisions in investment projects: A study in Swedish wood products industry. Procedia Manufacturing, 25, 255–262. doi:10.1016/j.promfg.2018.06.081

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ADDITIONAL READING Aquilani, B., Piccarozzi, M., Silvestri, C., & Gatti, C. (2020). Achieving Environmental Sustainability Through Industry 4.0 Tools: The Case of the “Symbiosis” Digital Platform. In Customer Satisfaction and Sustainability Initiatives in the Fourth Industrial Revolution (pp. 37-62). Hershey, PA: IGI Global. Avogaro, M. (2019). The Highest Skilled Workers of Industry 4.0: New Forms of Work Organization for New Professions. A Comparative Study. E-Journal of International and Comparative Labour Studies, 8(1). Beard-Gunter, A., Ellis, D. G., & Found, P. A. (2019). TQM, games design and the implications of integration in Industry 4.0 systems. International Journal of Quality and Service Sciences, 11(2), 235–247. doi:10.1108/IJQSS-09-2018-0084 Chovancova, B., Dorocakova, M., & Malacka, V. (2018). Changes in industrial structure of GDP and stock indices also with regard to the Industry 4.0. Business and Economic Horizons (BEH), 14, 402-414. Galati, F., & Bigliardi, B. (2019). Industry 4.0: Emerging themes and future research avenues using a text mining approach. Computers in Industry, 109, 100–113. doi:10.1016/j.compind.2019.04.018 Ivanov, D., Sethi, S., Dolgui, A., & Sokolov, B. (2018). A survey on control theory applications to operational systems, supply chain management, and Industry 4.0. Annual Reviews in Control, 46, 134–147. doi:10.1016/j.arcontrol.2018.10.014 Liao, Y., Deschamps, F., Loures, E. D. F. R., & Ramos, L. F. P. (2017). Past, present and future of Industry 4.0-a systematic literature review and research agenda proposal. International Journal of Production Research, 55(12), 3609–3629. doi:10.1080/00207543.2017.1308576 Liboni, L. B., Cezarino, L. O., Jabbour, C. J. C., Oliveira, B. G., & Stefanelli, N. O. (2019). Smart industry and the pathways to HRM 4.0: Implications for SCM. Supply Chain Management, 24(1), 124–146. doi:10.1108/SCM-03-2018-0150 Mishra, D., Roy, R. B., Dutta, S., Pal, S. K., & Chakravarty, D. (2018). A review on sensor based monitoring and control of friction stir welding process and a roadmap to Industry 4.0. Journal of Manufacturing Processes, 36, 373–397. doi:10.1016/j.jmapro.2018.10.016 Nascimento, D. L. M., Alencastro, V., Quelhas, O. L. G., Caiado, R. G. G., Garza-Reyes, J. A., RochaLona, L., & Tortorella, G. (2019). Exploring Industry 4.0 technologies to enable circular economy practices in a manufacturing context: A business model proposal. Journal of Manufacturing Technology Management, 30(3), 607–627. doi:10.1108/JMTM-03-2018-0071

KEY TERMS AND DEFINITIONS Human Factors: A science field that aims at understanding the interactions between humans and other elements of a system, not only by addressing the most current research challenges with a multidisciplinary approach, but also by applying theory, principles, data, modelling, and other methods to design, in order to optimize both human well-being and overall system performance.

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Integrated Management Systems: An organization-wide system that integrates all the systems and processes of an Organization into a complete, efficient and effective structure, enabling the Organization to function as a single unit with unified objectives, optimizing resources for implementation, maintenance and audits to each Management system. Manufacturing: The economic activity that uses a technique, generally dominated by the presence of machinery or machinery, to transform raw materials into production and consumption products. Occupational Safety: The discipline that focuses on the study, assessment and control of the risks of operation (e.g., obstacles, sharp edges, safety protection-lacking machinery, falling objects) to prevent the occurrence of accidents and other anomalous situations at work. Risk Management: The overall process of risk avoidance, mitigation and / or control, encompassing risk assessment, which in turn encompasses risk analysis. Safety Culture: A subset of the overall Company culture, refers to “the product of individual and group values, attitudes, perceptions, competencies, and patterns of behaviour that determine the commitment to, and the style and proficiency of, an Organization’s health and safety management” (ACSNI Study Group on Human Factors, 1993). Security: The vigilance and protection against possible attacks or hackings on an institution or personality. Sustainability: The quality of being able to exist over some time, enabled by the satisfaction of 6 criteria: eco-efficiency, socio-efficiency, eco-effectiveness, socio-effectiveness, sufficiency and ecological equity; concomitant avocation of efficient ecological, economic and social causes in a determined time frame. Standards: Documents that provide requirements, specifications, guidelines or characteristics that can be used consistently to ensure that materials, products, processes and services are fit for their purpose (ISO). Transnationalization (or transnationalism): explains how interconnected networks of social organization (individually or in society) can influence each other, either in the political or economic sphere or otherwise.

This research was previously published in Safety and Security Issues in Technical Infrastructures; pages 152-172, copyright year 2020 by Information Science Reference (an imprint of IGI Global).

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Technodata and the Need of a Responsible Industry 4.0 Raúl Tabarés Gutiérrez https://orcid.org/0000-0002-8149-3534 Fundación Tecnalia Research and Innovation, Spain Javier Echeverría Ezponda Ikerbasque, Spain

ABSTRACT The great transformation that will face European industry is driven by the need of digitizing the entire value chain around manufacturing for creating competitive advantages to maintain a dominant position in the global economy. This new paradigm is commonly known as Industry 4.0, and it has a significant policy support from the European Commission as well as different member states. However, this transition is full of uncertainties as the digitization of industry creates different concerns about employment, privacy, labor rights, and other issues related with this technological revolution. In this chapter, the authors trace back the origins of Industry 4.0 to the Web 2.0 phenomenon as well as they reflect upon the role of technodata and technofactories in a postindustrial society. Finally, they stress the need to reflect about developing a responsible digitization of industry that will consider societal concerns.

INTRODUCTION Technological revolutions have been historical sources of great wealth as well as producers of deep societal transformations. New radical technologies like Robotics, Cyber-Physical Systems, Internet of Things (IoT), Artificial Intelligence and many others are favoring a transition to a new industrial revolution that will redefine the role of factories in the economy all over the next coming years. The introduction of digital technologies for automating and monitoring production process will create new routes for innovation, new services and new competitive advantages in manufacturing companies. The European Commission (EC) is promoting this new paradigm throughout what it has been originally coined as “Industry 4.0” in Germany some years ago, for embracing digitization and favoring the transition in factories to business DOI: 10.4018/978-1-7998-8548-1.ch090

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 Technodata and the Need of a Responsible Industry 4.0

models based in high-added value services. This push for digitization in the whole value chain of the manufacturing sector lies in the inability of Europe to compete with U.S. Internet companies and as a way to strength the current sector for assuring the international importance of it in the global economy. But this transition to a digitized industry is full of challenges as the main role of industry in the economy will be completely redefined, and this will create deep transformations in society. Loss of medium and low skilled jobs, the need of huge investments to acquire technology and maintain it, conflicts between workforce and managers, as well as issues related with privacy, security and new forms of alienation due to the introduction of autonomous technologies in factories are envisioned as non-side effects of this technological revolution. New tensions will emerge in the policy arena as well as in society as the factory is still the place where class struggle happens and already existing power relations will be affected by new forms of control. These societal and ethical consequences of the digitization of manufacturing processes will demand a more collaborative approach to technology design and development, for assuring that the outcomes of this new revolution can be beneficial to a larger part of society and preventing possible non-desired impacts. In this contribution the authors pay attention at the historical roots of the Industry 4.0, that can be traced back to the origins of Web 2.0 and the platform economy, to understand the value of data in this digitized form of economy to create new innovations based on this new fuel. The text explores how this new type of industry is being promoted by policy makers and the role of technodata as the basis of this new kind of technofactories in the Third Environment is stressed. In addition, the authors underline the need of developing a Responsible Industry 4.0 that will consider and reflect on the views of the different stakeholders that will be affected by these great transformations that are envisioned in the manufacturing business. Moreover, it is argued that the introduction of these new digital technologies in the factory must be aligned with societal expectations, for assuring the diffusion of the innovations as well as promoting their competitiveness, avoiding further modifications. If this kind of co-responsibility approaches are not considered, the authors predicts that the embracement of digitization in factories can face significant barriers that will deter its implementation. New problems can be raised in the factory in relation with the introduction of these new technologies as well as the experimentation of difficulties in this transition can lead to a lack of competitiveness in the manufacturing sector. Finally, the authors conclude with a set of a policy recommendations for accommodating the introduction of technological innovations to the social concerns that can be risen up as main stoppers of the Industry 4.0 paradigm. Attention is also posed in the need of developing a much more diverse industry that can prevent biases, inequalities and negative externalities from the adoption of disruptive innovations by different companies. The authors stress the need of a developing a Responsible Industry 4.0 that can be beneficial to society and can pave the way for the digitization of manufacturing, avoiding at the same time, dramatic transformations in economy, employment & welfare.

WEB 2.0 AS THE ONSET OF PLATFORM ECONOMY In 2004 Tim O´Reilly wrote a famous article describing the new features that were sharing a certain group of Internet companies that rose at this time in Silicon Valley after the dot com crash. These companies, according to O´Reilly, were creating the “Web as platform” paradigm (O´Reilly, 2005b) because they were developing new business models and new technologies that created a “perpetual beta” in software 1789

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and making the Web more important than ever. O´Reilly also identified in his proposal a set of 7 principles to speak about a Web 2.0 version of the Web (O´Reilly, 2005b); 1. 2. 3. 4. 5. 6. 7.

The Web as Platform Harnessing Collective intelligence Data is the next “Intel Inside” End of the software release cycle Lightweight programming models Software above the level of single device Rich user experiences

Despite the fact that the term Web 2.0 was a buzzword for identifying several aspects and implications of a new approach to develop websites that was not focused only on technological assets (Anderson, 2007), several technologies like Ajax, XML, Flash and API´s can be recognized in this momentum that implied a breakout with the previous Web (Tabarés-Gutiérrez, 2015, 2016), and introduced more fragmentation in the platform (DiNucci, 1999). According to O´Reilly Web 2.0 is the network as platform, spanning all connected devices; Web 2.0 applications are those that make the most of the intrinsic advantages of that platform: delivering software as a continually-updated service that gets better the more people use it, consuming and remixing data from multiple sources, including individual users, while providing their own data and services in a form that allows remixing by others, creating network effects through an “architecture of participation,” and going beyond the page metaphor of Web 1.0 to deliver rich user experiences (O´Reilly, 2005a) One of the fathers of the Web, Tim Berners-Lee, has also expressed in several occasions his disconformity with this interpretation of Web 2.0 as a contrary idea to the previous Web as he claims that it was designed from the very beginning to connect people (Berners-Lee, 2000; Laningham, 2006). Despite this statement, it can be observed how after the appearance of Web 2.0 new platforms like Wikis (Anderson, 2007), Blogs (Doctorow, 2002; Nardi, Schiano, Gumbrecht, & Swartz, 2004) and Social Network Sites (Boyd & Ellison, 2007) clearly contributed to a growing user adoption of the Web due to the easy generation content tools that these new websites introduced on the Internet. Non-technical users without skills of HTML coding were attracted to the Web as well as other kinds of users that were already present. This was possible due to the emphasis of the Web 2.0 period for generating new multimedia contents that attracted much more attention than other previously present text-based contents (Tabarés-Gutiérrez, 2015, 2016). It is in this moment when social media (Helmond, 2015; Kietzmann, Hermkens, McCarthy, & Silvestre, 2011; Mangold & Faulds, 2009) platforms like Facebook, Twitter or YouTube emerge and become the digital references where different users start to create, edit, remix, comment and upload different kind of contents. Pictures and videos will become the new drivers to attract attention (Goldhaber, 1997) and User Generated Content (UGC) (J Van Dijck, 2009; Ritzer & Jurgenson, 2010) sites will start to gain prominence and reconfigure the nature of the Internet. As time has gone by and digital business models based on user data have demonstrated their profitability, new platforms have risen in the Web. This is the case of Netflix, AirBnB, Uber or Spotify or other companies that have positioned themselves as intermediaries in our daily routines for watching movies in streaming, renting a flat, hiring transportation services or listening to music throughout streaming services. These companies and others that have popped up after the Web 2.0 paradigm and the appearance of mobile devices (Vogelstein, 2013) belong to 1790

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what it has been called recently as “platform economy” (Kenney & Zysman, 2016), “platform capitalism” (Srnicek, 2017), “sharing economy” (Sundararajan, 2016) or “surveillance capitalism” (Zuboff, 2015). The disparity of terms used for referring to a new reality give us an idea about the important change that have happened in the business models that host the Web and their importance in global economy. This new platform economy is characterized by an accumulation of power, audiences and resources in digital platforms populated by users, producers and prosumers. Platform economy is defined as a term that encompasses a growing number of digitally enabled activities in business, politics, and social interaction (Kenney & Zysman, 2016). Due to the growing digitalization of sociality (José Van Dijck, 2013) and the decentralization effect that Internet culture provokes on society (Castells, 1997, 2001) a transition to digital services that are promoted by nascent start up´s has been possible and new business have succeed in an increasing technology-mediated environment. These new companies are totally dependent on the contribution of human beings and the digitization of value-creating human activities (Kenney & Zysman, 2016) for creating new services and remaining competitive. The availability of large datasets that have been generated since the consolidation of UGC websites (J Van Dijck, 2009) and the pervasive use of mobile devices has made possible that the establishment of data driven business models in several areas of society has become the new norm. This exploitation of data introduced by users in a voluntary basis (pictures, videos, texts, etc.) or in an involuntary basis (data coming from web or mobile tracking) by platform owners on the Internet has led to the establishment of concepts like “digital labor” (Scholz, 2012) or “free labor” (Terranova, 2000) that reflect upon the new forms of labor exploitation that have introduced by digital capitalism. Nowadays it can be noticed how this business paradigm is currently being extended to other fields like industry as the costs for manufacturing are decreasing and new forms of value must be build up for maintaining profits and developing new services that can generate more benefits in a post-industrial scenario. As Martin Zenney and John Zysman argue If the industrial revolution was organized around the factory, today´s changes are organized around these digital platforms, loosely defined. Indeed, we are in the midst of a reorganization of our economy in which the platform owners are seemingly developing power that may be even more formidable than was that of the factory owners in the early industrial revolution (Kenney & Zysman, 2016). The evolution of these digital platforms that have emerged on the Internet have been focused in the development of Big Data tools (Boellstorff & Maurer, 2015) that allow them to analyze the increasing volumes of data that are generated but also for decreasing the costs of collection and storage of it. At the same time, these technologies have permitted to generate more sophisticated techniques that are a continuous source of innovation for these companies (Boyd & Crawford, 2011; Gray, 2014). In this sense, the irruption of Artificial Intelligence (AI) has been made possible due to the great advances of computing in data treatment but also for the large quantities of available data to train computers in specific tasks. The next frontier in this expanding paradigm about the role of data in business is the factory as it remains as an unexplored territory where there is not sufficient knowledge about production processes and there are no digital layers of information that can be used for providing a deeper understanding of what is happening at the sweatshop. In this sense, is of outmost importance for industry leaders to create new innovations that can aid to the inner working of the plant or developing new services that can be aligned to the sector where they work. This is one of the next steps in the digital transformation of society towards the post-industrial scenario where the classical manufacturing of goods is not likely to be as lucrative as it was in previous industrial revolutions with a continuous decreasing of manufacturing costs and profitability. 1791

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INDUSTRY 4.0: TOWARDS A NEW PARADIGM FOR MANUFACTURING IN EUROPE From 2010 onwards, it has been observed the emergence of a new paradigm in the current situation of European industry. Despite the fact that Europe has also boasted of a strong manufacturing sector that employs around 30 million people, represents around of the 16% of the GDP and contributes to more than 80% of total exports, the financial crisis that took place in 2008 has led to the loss of jobs, mainly among the unskilled or low-skilled workers (European Commission, 2013). Globalization and the rising of emerging economies in the south east of Asia has also favored the delocalization of factories and industrial plants due to the low labor costs that these countries can offer to multinational companies. In addition to, there is a shared consensus about the rising of a new industrial revolution that is on the way and it will redefine manufacturing and the classical conception of what a factory is due to emergence of several disruptive technologies coming from the digital transformation (Brynjolfsson & McAfee, 2014; Schwab, 2016). Despite all of these factors, manufacturing is envisioned to be a major provider of employment in Europe in 2025 (EU Skills Panorama, 2014) and in this sense the rising of “Industry 4.0” paradigm is critical for achieving this objective. The digitization of European industry is one of the highlighted milestones that the European Commission is pushing forward throughout different policies aimed to modernize European factories and improving their capabilities for facing a more complex and challenging production ecosystem. Industry 4.0 is the term that is being used for describing a much more automatized, digitized and flexible production ecosystem. Despite the fact that the term was first used in 2011 at the Hannover Messe by a working group, the German government has used this concept for supporting their national industries in the digitization of their production process (Verniere, Van der Straeten, Torfs, Venderlinden, & Van den Kerkhof, 2017) and as a way to confront the growing competition overseas. The name has been widely accepted by other countries at the European level and it also counts with the backing of the EC. One of the most well knowns policy briefings delivered by the European Parliament defines Industry 4.0 as a term applied to a group of rapid transformations in the design, manufacture, operation and service of manufacturing systems and products (Davies, 2015). Other terms have been also used to referring to this new paradigm such as “Smart factories”, “Industrial Internet of Things”, “Smart industry” or “Advanced manufacturing” but these terms have lost popularity in favor of Industry 4.0. Although the term has been recognized worldwide, in countries like USA and UK is known as “Connected Enterprise” or “Fourth Industrial Revolution” (Morrar, Arman, & Mousa, 2017). Basically, the potentialities of Industry 4.0 are related with the possibilities that digitization of physical world can bring to industrial plants as well as how decentralized decision making can be introduced throughout the connectivity between machines and humans in real time. This new paradigm is also considered as an evolution of the current factories towards the acquisition of automation capabilities and the merger of machinery and digital systems (see Figure 2). This new industrial revolution aim to digitally connect everything in and around a manufacturing operation (suppliers, the plant, distributors, even the product itself) for providing a highly integrated value chain (Davies, 2015). This major reconfiguration of production processes in factories needs of a significant policy support and that is one of the reasons behind the support of the EC to different research and innovation actions like “Factories of the Future” Public-Private Partnership program1, where different stakeholders from academia, industry and government are collaborating. Heavy investment2 is being fueled throughout this program and more than 2.000 organizations have benefited from this initiative 1792

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thanks to the 240 projects that have been funded during the last years (Pazin, 2017). This effort by the EC is also aligned with other national strategies that different Member States have implemented in their countries to support their local industries. As it is shown in Figure 1, almost every member state has a specific Industry 4.0 program to support this technological transition of businesses and it is expected that in the near future new programs and policies will be planned and delivered at national and European levels to enable this transition. Figure 1. Map of different European initiatives around Industry 4.0

(European Commission, 2017a)

The digitalization of traditional industry is a great challenge as it requires the collaboration of different actors that have not been present in the manufacturing sector till recent years. That is why a major policy support is expected to favor the engagement of new agents. Fabrication is of paramount importance in relation to employment in Europe with around 30 million direct jobs and 60 million indirect jobs (Pazin, 2017) and what´s more important; 59% of these jobs are distributed in SME´s (European Commission, 2013). This is one of the reasons that helps to understand the political strategy of manufacturing in the Eurozone and why it is highly interconnected with different institutions, companies, universities, research centers and other stakeholders that it makes this sector of outmost importance in every policy agenda for their political and social implications3. The EC is also committed with this significance and some parliamentarians like Andrus Ansip, Vice-President of the EC for the Digital Single Market has remarked this need for policy alignment in Europe around the idea of supporting the digitization of industry; I congratulate the Member States which have already started their national political initiatives, committing significant financial and organizational resources to digitizing European industry. I warmly welcome

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the newcomers and encourage other countries to join. The European platform of national initiatives is an example of a collaborative and cohesive European Union. This is a strong effort at European level, but it will produce results only if Member States do their part and support industry and innovation communities in their regions which drive digital transformation (European Commission, 2017a). The framing of this call to join forces between Member States for supporting this new technological paradigm lies in the need of upgrading the European industry and creating a more competitive one that can guarantee well-paid jobs, exports and wealth. This modernization of the industry is also narrowed in the increasing competitiveness of emerging economies in the south-east of Asia and the predominance of the Chinese industry in the global market. Another big reason is that Germany has a more manufacturing-based economy that the USA and the UK and at the same time, the power achieved by US Internet companies in the last years makes so difficult for Europe to compete in these novel markets. This explains why Germany and Europe have adopted this strategy towards the digitization of industry for becoming world leaders and innovators in manufacturing (Fuchs, 2018). Nevertheless, it must be stressed that the digitization of industry is also a never-ending process as the introduction of software to monitor industrial processes requires of people and technologies that can update and maintain software services upon request. The values of the Web 2.0 paradigm are also embedded in this industrial revolution offering a “perpetual beta” (O´Reilly, 2005b) in order to upgrade technologies for monitoring manufacturing. In this sense, the rising of disruptive technologies like Big Data, Robotics, Cybersecurity, Internet of Things (IoT), artificial intelligence (AI), Cyber-physical systems or Additive Manufacturing will redefine the operational processes of factories creating new opportunities for maximizing efficiency, agility and flexibility in manufacturing (Keller, Rosenberg, Brettel, & Friederichsen, 2014). However, this transition must face several challenges that have been identified to embrace a new technological paradigm. The integration of ICT in manufacturing processes does not only implies the acquisition of particular innovative technologies. It really means a more complex process that demands to rethink about the role of infrastructures, resources, processes and organizations themselves. Another of the representatives of the EC, Željko Pazin, which is the President of European Factories of the Future Research Association (EFFRA), stress how important is to reflect about what it means to get on board on this industrial revolution; digitization means more than the installation of new ICT or high-speed connectivity. It is a complete transformation of where, how and why we manufacture. It is shaping the factory floor, products, the skills of workers and integrating services and supply chains. (Pazin, 2017)

Challenges for Industry Digitization Several challenges lie ahead in the implementation of the Industry 4.0 paradigm in the manufacturing European landscape, as the integration of digital technologies in factories does not merely depend on technology acquisition and technology transfer processes. Of course, it demands a major reconsideration of facilities, structures, resources and processes but it also stress several points of action that can hardly being tackled by organizations that do not have plenty of resources. In this sense, one of the major challenges that imposes Industry 4.0 is related with the need of large investments to upgrade and maintaining technological equipment. This barrier could deter the onboarding of SME´s as they do not usually have the kind of access to funding that is needed to accomplish these kinds of large investments. In this sense it´s very likely that minor companies will have to cooperate in order to share resources and create open value chains (IEC, 2015). Big companies will be also forced to contribute to these collaborative environments as the nature of manufacturing production processes requires a high grade of 1794

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interconnection between different organizations. Current trends in customization and personalization are imposing flexibility, agility and resilience as core components of production systems as well as shorter product lifecycles have become the new norm. These trends will demand new collaborative approaches to be developed in production ecosystems that can meet the increasing high-end specifications. Another important challenge that may arise soon are possible asymmetries in digitization processes between regions. This is a problem that will draw a lot of attention from policy makers as inequalities between Member States can hinder the potential of an European data economy (European Commission, 2017b). Digital technologies have demonstrated their potential to create different kinds of divides (DiMaggio & Hargittai, 2001) in the past and legislators will have to take a closer look to the evolution of the different countries for assuring an inclusive industry for all Member States. Assuring high speed bandwidth connections, cybersecurity infrastructures and resources, as well as other kinds of digital equipment’s and resources will be of outmost importance for paving the way to a smooth transition to a digitized economy. Figure 2. Industrial Revolutions and Future View Source: Wikipedia

Deskilling and reskilling phenomena are also envisioned as future problems in the current workforce as this modernization of industry is driven by automatization as well as other industrial revolutions have suffered too. Despite the fact there is a growing literature about the future impact of the digitization of factories in employment, there is no consensus at all about what it will be the indicative amount of jobs that can be lost due to this transition (Winick, 2018). It seems that many medium-income jobs will be affected by this revolution as it has already happened in the XIX century (Frey & Osborne, 2013) and it can be inferred that low-skill and medium-skill jobs could be at risk due to the impact of automatization in factories. However, there is at the same time a major challenge in the need of promoting a high-skilled workforce that can lead this technological revolution as Robotics, AI and Cyber-Physical Systems will improve the autonomy of employees but will demand advanced capabilities. This is one of the biggest problems that have been identified as some reports have stressed that around 900.000 jobs with specific digital skills could be demanded in 2020 in Europe (Gareis, K., Hüsing, T., Birov, S., Bludova, I., Schulz,

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C., & Korte, 2014). In fact, it is expected that in coming years 9 of 10 jobs will require digital skills and in this sense Europe is not satisfactorily prepared as 44% of Europeans do not have these basic abilities4. All of these challenges remain unsolved and can act as stoppers of digitization processes in European industry if they are not properly tackled. The importance of manufacturing for Europe is essential and in this sense, there are several dark clouds that are appearing on the horizon, questioning the viability of this transition to a digitized industry.

TECHNODATA AS THE FUEL FOR INDUSTRY 4.0 As it has been exposed previously, the main driver of this new technological revolution lies in the need of digitizing the manufacturing network and infrastructures for creating virtual realities that can thrive new innovations. The role of data and the generation of digital layers of information that can store the functioning of real processes at the factory is one of the backbones where it lies the next industrial revolution. Innovations like Digital Twins are of paramount importance in this new industry as they comprise particular artifacts and their computer models that reflects its performance in a carefully way (Bruynseels, Santoni de Sio, & van den Hoven, 2018). These novel assets allow to develop new techniques such as predictive maintenance for maximizing efficiency as well as optimizing resources in production processes. Then; the unexpected can be envisioned with a percentage of error and providing some insights about what can it be done about it. This rediscovering of the real world throughout virtual realities and digital artifacts makes room for the emergence of technodata; data that is treated throughout different emergent technologies like Big Data, Machine Learning or AI and it is of outmost importance for the new industry as it constitutes the new fuel for a growing digitized industry. This emergence of technodata must be understood as a byproduct of the Third Environment and the technosciences (Echeverría, 1999). This is also a direct consequence of the development of Information and Communication Technologies (ICT), and its embedding in societal processes of every kind (business, social life, administrative, legal, etc.). Technosciences emerge in society in the U.S.A. after the World War II thanks to the recommendations of several policy makers that started to lure attention to the important role of Science & Technology and its contribution to economic and societal development (Bush, 1945). Macro research projects commonly known as “Big Science” are funded by the federal government and other agencies like the NASA or the NSF combining huge investments, top-notch facilities and high-skilled researchers. Afterwards, other technoscientific initiatives are deployed in other countries, especially in Europe, like the CERN or the ESA (Echeverría, 2018). Nowadays, it can be observed how these initiatives are still on the edge with popular collaborative research and innovation programs in the old continent such as “Horizon 2020”5 that are supported by the EC. As well as with technoscience, it is needed to explain the hypothesis of the Third Environment as the authors consider this concept of great importance to frame the technological revolution that is being conducted. To do so, it is required to previously clarify the notion of technological systems as systems of human actions and not only as a set of artifacts, mediums or instruments (Quintanilla, 2005). A concept that is also influenced by the Orteguian idea of “supernature” that reflects on the transformative role of certain kind of technologies. The famous Spanish philosopher of technology also argued that thanks to techniques, human beings have generated “supernatures”; man, thanks to his technical gift, manages for everything that he requires to be found in his surroundings – he creates, thus, a new, more favorable

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circumstance, segregating, we could say, a supernature by adapting nature to his needs (p 65, Ortega y Gasset, 1965) Technologies and innovations have transformed nature and societies in a great extent creating new possibilities for human development. Inhabitants of urban areas are also a good example of this because they do not live in nature but in an artificial ecosystem developed by human technologies. Not only in a physical way but also in an intellectual way due to a different set of norms, values and laws that have been superimposed by human technologies of different kind. These two environments have been transformed by a third one, the Third Environment, which has been originated thanks to the development of the diverse ICT technological systems that have permitted the emergence of a new social space–time, the third environment, that superimposes itself on the two great space–times that human beings have experienced and that we continue to experience: the physis and the polis (J Echeverría, 1999). It is in this Third Environment where technosocieties (Echeverría & Tabarés-Gutiérrez, 2016) have emerged due to ICT´s. Digital technologies now allow to create, maintain or blocking social relationships and interactions between citizens (and also with other techno-objects and technopersons) throughout technological mediation. ICT´s have created new digital infrastructures where information and data are the new valuable resources that can generate profits and economic activity. Of course, data treatment is nothing new as it has been one of the core activities in many industries, businesses and governments since long time ago (Boellstorff & Maurer, 2015) but the great advances in computing technologies (particularly in cloud computing) in the last years have allowed to generate new techno-objects that can sustain new innovations in this Third Environment. As it has been argued previously, the main goal of technosciences is not only conceptualizing the world but also to transform it (Echeverría, 2018) and this has been the case in the progressively digitization that has occurred in society. ICT´s not only create new objects but also mediates ways of life as well as they create new forms of living. Other authors have also spoken about the transformational nature of the ICT and they have coined other terms to this phenomenon like the infosphere (Floridi, 2002) or the informationalism (Castells, 1997). The emergence of Youtubers and other “digital personalities” in the Web that are able to attract attention to the content they produce as well as to monetize these audiences are examples of it (Tabarés-Gutiérrez, 2017). These new actors are also living in the Third Environment as technopersons but they have also a profound impact in the other two environments as they are superimposed into them. In this sense, the emergence of technodata is the vital raw material that technofactories will have to gather, store, use, share and reuse for developing new services that will add a major value to their products and services. Nevertheless, the digitization of factories will provoke major transformations as ICT´s have provoked previous disruptions in business like media, tourism or music. This is of particular importance as the factory is one of the major providers of employment in Europe and the modernization that is on the way is creating several shadows of mass unemployment all over the continent. At the same time, the investment of capital that is needed to acquire digital technologies (and maintaining them) can create inequalities between regions, businesses and companies. The accumulation of capital and power in particular companies is expected to follow similar patterns to the Internet industry, where the rising of the platform economy has showed how a bunch of organizations have a dominant position in sectors like digital advertising, e-commerce or cloud computing (Gillespie, 2010; Kenney & Zysman, 2016; Srnicek, 2017; Sundararajan, 2016). But at the same time, it is still unclear that these transformations will happen in a smooth way as it has (almost) happened in the Internet industry. The factory is still the place where power relations and class struggles happens and due to the weight of European manufacturing in the total amount of jobs in the continent, it is very optimistic to think that these transformations will be 1797

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materialized without social and political convulsions. Manufacturing still relies in a great significant portion of human labor and Germany specially has significant labor costs coming from the workforce. This emphasis in the digitization of industry can be seen as a capitalist need for reducing labor costs and maximizing profits but it is still not clear how human labor complexity will be reduced by automation in a substantial way (Fuchs, 2018). Machines have proven to be excellent with simple tasks in different production lines but there is still a long way ahead to create a robot that can deal with the physical barriers of an industrial plant and help humans at the same time in their daily routines. Other challenges that will create the growing digitization of the industry are related with privacy, cybersecurity and surveillance. All of them will create new needs in terms of regulations and security as goods will be most of them transformed in services but also because of technodata will become the major asset of technofactories. This technodata is not a single raw material but a set of different aggregated data that will be built up throughout the combination of different sources and treated with different techniques and technologies like Big Data, AI or Machine Learning for obtaining valuable outputs that can generate new services or innovations. Data such as machine durability or employee performance in certain tasks can be stored, analyzed or aggregated to create new technodata that will constitute a valuable output. The emergence of data brokers in the industrial realm will be also another issue that will provoke privacy and security concerns as this technodata constitutes a commodity itself that can be sold for further treatments. Just like data brokers are also operating with current data provided by Social Media platforms and Internet activities that are monitored (Zuboff, 2015). All in all, there is a clear transformation of the business models and value proposals on the way regarding the manufacturing sector as digital technologies imply a new conception of production processes. A major reconfiguration that needs to rethink traditional roles of workforce but also needs to rethink the entire role of industry in the European economy and society. The growing role of technology in manufacturing will create tensions in different areas of society and it will also create new (but at the same time old) conflicts as the displacement of workforce in production processes will be favored by industry digitization.

THE NEED FOR A RESPONSIBLE INDUSTRY 4.0 In previous sections there have been exposed the difficulties that will appear in coming years due to the transformation of the European industry and how this reconfiguration will impose several challenges in the foreseen path of digitization. The shadows of mass unemployment that can introduce the automatization of factories (Frey & Osborne, 2013) as well as the growing need for massive investments for incorporating technology in industrial plants (Davies, 2015) will create several tensions in the policy arena. Other emerging challenges that will appear due to the introduction of surveillance and monitoring techniques into the factory will affect regulatory frameworks and social consensus as it can be expected new conflicts between workers, trade unions and corporate managers in a classical class struggle basis. In this sense, the authors would like to draw attention to and to reflect upon the idea of developing a “Responsible Industry 4.0”as the role of it in the European economy and society is of outmost importance. The need of developing and introducing technological innovations in the factory that will go hand in hand with societal expectations can produce better outputs and alleviate the strains that the new industrial revolution can bring into society. The EC has also adopted Responsible Research & Innovation (RRI) as a pillar in his strategy of Research & Development in the Horizon 2020 program (European 1798

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Commission, 2012) but it must advance in his strategy to include RRI as a core value of Industry 4.0. This emerging paradigm will create a profound disruption of the classical understanding of industry as a major provider of manufactured goods, long-term jobs and supplier economic activities if it is finally fully embraced and RRI could help to mitigate the negative externalities that technological innovation can introduce in manufacturing. The EC provides the following definition for RRI; Responsible research and innovation is an approach that anticipates and assesses potential implications and societal expectations with regard to research and innovation, with the aim to foster the design of inclusive and sustainable research and innovation (European Commission, 2017c). RRI can be a useful element in order to foresee the challenges and threats that digital tech could bring to factories as reframes responsibility in innovation contexts (Owen, Macnaghten, & Stilgoe, 2012) as well as confers new responsibilities (Douglas, 2003) not only for scientists but for other different stakeholders likewise universities, business, policy-makers and research funders, in order to prevent from technological “lock-in” (Collingridge, 1982) and “historical accidents” (David, 1985). In this sense, the Collingridge dilemma imposes several challenges for the digitization of industry as ethical issues can be early addressed during the technology design phase but at the same time, it is tough to envision which social and ethical consequences will appear after the design is implemented (Collingridge, 1982). Dual uses of technology as well as not envisioned problems that can appear with the introduction of particular technologies in the factory might appear at a stage where the change of their trajectories have not enough room. In addition, the non-envisioned ethical and social consequences of different technologies are usually pointed out by stakeholders that are not usually part of the communities that take part in their design and development, as engineers and designers have usually positive attitudes to their creations and are not aware about its future recoils. Innovation is also a collective process that requires a collective approach to responsibility (Mitcham, 2003) and that many times is supported by public debate (Von Schomberg, 2013) with different stakeholders that contribute through their visions to the process of research, technological development and innovation. Therefore, involving actors and users into these processes to introduce new ideas and values is also encouraged for envisioning not socially acceptable or potentially dangerous products of technological innovation (Stilgoe et al., 2013) and at the same creating the “right impacts” (Owen et al., 2012). This emphasis on promoting a collective responsibility between different stakeholders can help to reduce uncertainty and creating trust in different collectivities that are not usually included in Research & Innovation processes. Different non-side effects and trade-offs that can create technological innovations are important matters that must be discussed not only by technology makers. That is why incorporating different stakeholders to the loop can be mutually beneficial for aligning societal expectations and reducing uncertainty in complex systems that certainly will have a deep impact in economy and society. Involving different stakeholders and diverse collectivities can help to visualize future scenarios to avoid or to embrace, that the introduction of these technological innovations in factories can provoke. RRI also can provide of economic benefits as the involvement of different stakeholders in research and innovation processes can lead to a greater diverse ecosystem that can imply a better quality of the technologies introduced in the factory. This is of special interest in relation with digitization processes as the growing importance of algorithms and other autonomous technologies in our lives are revealing how technology possess several biases that reflects the values of its developers (Quirós et al., 2018). Digital sector is a predominant white male industry and creating a diverse workforce will help to meet

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the needs of increasing and complex fragmented markets as well as saving costs in potential business loses that will not take care of this (Quirós et al., 2018). Pursuing a more collaborative and diverse approach for this Industry 4.0 will also prevent future modifications of technologies that will be demanded by regulators to compliance with laws. Moreover, the inclusion of different agents in these R&D processes is a cost-effective strategy to obtain new ideas, procedures and outcomes that can help to create innovations and services aligned with the societal expectations and needs. These new agents can bridge research centers and companies with other domains that have been previously unexplored or remain inaccessible. Sustainability strategies can be introduced into technology roadmaps with low cost due to the collaboration with other outsiders of industry that are affected by technological innovations. Some examples that can lead to this kind of interdisciplinarity collaborations with industry are initiatives such as the recommendations that a group of experts has proposed to the EC on Civil Law Rules on Robotics (https://bit.ly/2Pmqj1z) or the “Ethics in Action” initiative driven by the IEEE (https://ethicsinaction.ieee.org/). These kinds of initiatives are umbrella actions that aim to inspire concrete actions by markets, sectors and companies which are surrounded by different stakeholders in their value chains. These specific actions can help to mitigate the ethical and social non- side effects of the introduction of Industry 4.0 paradigm in industrial landscapes. To conclude with this section, the authors would like to encourage industry leaders to adopt a comprehensive approach to this new technological revolution as previous episodes in industrial history have shown how dramatic these transformations can be. Particularly, the authors argue that there are several major lines of action that policy makers will have to safeguard for promoting a Responsible Industry 4.0; • •

•

•

•

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Protecting employee labor rights in front up of technological innovation. The introduction of disruptive innovations like Robotics and AI will create new threats to employee labor rights as these autonomous technologies will diminish the influence zone of employees. Guaranteeing privacy, security and confidentiality of data, specially from employees. Technodata will become the major competitive asset in technofactories and these technodata will comprise different datasets that will aggregate not only data produced by machines but also from employee performance, habits, behaviors, etc. These data will be of specially complexity. Promoting digital skills and reskilling of workforce. New disruptive technologies will require new competences and knowledge as they will be embedded in traditional production processes. These episodes of technology transfer will create specific demands of skills as well as they will create skills obsolescence in different areas of production. Assuring meaningful participation of different stakeholders of value chains in digitization processes. Digital transformation of industry has a critical importance in the future and prosperity of Europe and therefore it shouldn´t be only at the hands of a bunch of European manufacturers. It is needed that a dialogue between different stakeholders that can be affected by these conversions must be enabled for envisioning non-side effects and anticipating them. Including marginal collectivities that can be excluded from advanced manufacturing. Industry 4.0 will need of a highly educated workforce and specialized profiles that can navigate in these new technofactories, and that is why the danger of exclusion of different collectivities, is greater than anytime at history. Women, migrants, elderly people and other marginal collectivities that face discrimination in the Internet industry could be also excluded in this new industrial paradigm.

 Technodata and the Need of a Responsible Industry 4.0

CONCLUSION During this text the authors have explained the historical roots of the interest to adapt business models that rely on data due to emergence of the Web 2.0 after the dot.com crash and afterwards, the consolidation of the platform economy on the Internet. Digitization of physical and human activities present a clear need for adopting data-driven business models that are considered to be more profitable and innovative in post-industrial scenarios. It is in this growing digitization and monetization of human activities where the interest for an Industry 4.0 in Europe has been rised. The digitalization of European industry and its value chain is focused in maintaining its important role in the global economy but also for creating new competitive advantages that can flourish by the generation of new services associated to the introduction of digital technologies to the manufacturing sector. This strategy has been pushed forward by Germany and the EC after the failing of the Lisbon Strategy and the inability of Europe to compete with US Internet giants. To strengthen a secondary strategic sector of the European economy, a highly interconnected one between the different Member States and at the same time, favoring the transition to a data economy, has becoming a new imperative in the policy arena for maintaining its leverage in the global economy. Nevertheless, this strategy of Industry 4.0 raises different questions about its financial sustainability, positive impacts on employment, new conflicts in factories and shadows of accumulation of power due to the digitization asymmetries that can occur in this transition. To prevent these negative externalities that technological revolutions can bring to European societies the authors argue that is of outmost importance to implement a Responsible Industry 4.0 approach in the current strategy of digitization. The adoption of a collective and diverse approach that can include other visions and expectations that are not currently being held by technology leaders can result in a more suitable introduction of autonomous technologies in factories and therefore, alleviating tensions and strains that can be risen with these transformations. The fourth industrial revolution should be also a major reconfiguration of what is the role of technology in society. The development and introduction of these digital innovations must be aligned to societal expectations for not provoking exclusions and displacements of stakeholders in industry, economy & society. A true industrial revolution will take all these factors into account for delivering a much more responsible way of developing and introducing technology that can provide prosperity and wellness for most of society and not for a minority, as well as avoiding dramatic transformations in economy, employment & welfare.

ACKNOWLEDGMENT This research was supported by the NewHoRRIzon Horizon 2020 project under grant agreement No 7414402.

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KEY TERMS AND DEFINITIONS Digitization or Digitalization: Application of different ICT technologies to design, control, monitor, evaluate, assess, and improve processes, products, services, or strategies. Industry 4.0: Term used to describe a much more automatized, digitized, lean, and flexible approach to production processes in factories. Platform Economy: A business paradigm that has been pushed by the accumulation of power and capital on the internet throughout the development of digital platforms that gathered a significant number of users. Responsible Research and Innovation: RRI is an approach that tries to provide anticipation and assessment to potential implications of research and innovation. Technodata: Data treated by advanced technologies in order to find patterns and insights that can boost business innovation. Technofactories: Factories that rely on digitization and data for acquiring competitive advantages and high-value added services associated to manufacturing. Web 2.0: Period of the web characterized by the irruption of several participatory platforms like wikis, blogs, and social networks as well as the introduction of new dynamic technologies like Ajax, APIs, and Flash.

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More info about this initiative at this link https://ec.europa.eu/research/industrial_technologies/ factories-of-the-future_en.html. Total amount is around 1,15 billion euros for the period 2104-2020. More info at http://ec.europa. eu/research/press/2013/pdf/ppp/fof_factsheet.pdf. Major European manufacturers like Volkswagen have a large list of suppliers (more than 40.000) that are already coordinated in digital platforms. See for instance https://www.volkswagen-mediaservices.com/en/detailpage/-/detail/Volkswagen-Group-expands-digital-supply-chain/view/49405 53/4277f85fa0fe74e68f860d037e02125e?p_p_auth=fqbWuvt1. Digital Single Market has published a factsheet stressing this issue. See https://ec.europa.eu/digitalsingle-market/en/news/digital-skills-gap-europe. More information about this collaborative program of the European Commission can be founded at this link https://ec.europa.eu/programmes/horizon2020/.

This research was previously published in the Handbook of Research on Industrial Advancement in Scientific Knowledge; pages 1-19, copyright year 2019 by Information Science Reference (an imprint of IGI Global).

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Chapter 91

Sustainable Manufacturing in the Era of Industry 4.0: A DEMATEL Analysis of Challenges Ravinder Kumar Amity University, Noida, India

ABSTRACT This is an era of information technology and Industry 4.0 in the manufacturing sector. Globalization and spread of technology have leveled the field of competition among all economies. With aforementioned development, there is a need for sustainable manufacturing practices to justify the use of natural resources all over the globe. Both developed and developing economies should adopt the sustainable practices of manufacturing. On other hand, managing challenges of sustainable manufacturing is an uphill task for manufacturing organizations for several reasons. In this chapter, the author has analyzed the challenges of sustainable manufacturing by using DEMATEL technique to differentiate them in cause and effect challenges. This differentiation can further help in effective analysis of these challenges. From practical and managerial viewpoints, this study can help the policymakers and strategy planners of manufacturing organizations in better understanding of sustainability and its aspects. Further, it can help in developing policies on sustainable manufacturing on national and international level both in developed and developing economies.

INTRODUCTION Sustainability is a concept that is accepted widely these days. But common assumptions relate sustainability to only environmental issues. Sustainable manufacturing means ability to sustain, less wastage or carbon-less manufacturing. It has three aspects related to environment, economic and social. Sustainable manufacturing focuses on such strategies which work in present without sacrificing and polluting the resources of future. The process of production and product manufacturing should be environment friendly, sustainable in order to meet the criteria of recycle, remanufactured and reusable products till the end of their tenure. DOI: 10.4018/978-1-7998-8548-1.ch091

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 Sustainable Manufacturing in the Era of Industry 4.0

The science of winning organizations deals with the Green strategies that also combines the spotting and disposing of the various types of wastes that are frequently disregarded. As of now definition of Green has included “helping to sustain the environment for future generations”. Sustainable has been defined these days as “The elimination of waste everywhere while adding value for customers”. With environment, economy, and society, modern manufacturing is highly depends on technology and the influential role of technology on sustainable manufacturing practices cannot be ignored. Molamohamadi and Ismail (2013) observed that very few authors have studied the scope of technology in resolving the environmental and social menaces. The information technology and practices of Industry 4.0 has contributed significantly on sustainability front. With digitalization of other process, manufacturing practices have also evolved significantly in this era (Kayikci, 2018). Digital practices can contribute significantly for sustainability by reducing carbon foot prints, maximizing renewable energy usage and can generate efficient and economical viable technology solutions suitable for both individual and society. Haapala et al. (2013) stated that Sustainable manufacturing calls for coincidental thoughtfulness of economic, environmental, and social significances colligated with the production and delivery of goods.

SUSTAINABLE MANUFACTURING IN ERA OF INFORMATION TECHNOLOGY (IT) AND INDUSTRY 4.0 Practices of sustainable manufacturing can improve production facilities while reducing poor environmental and social effects. Nambiar (2010) observed that sustainable practices can enhance product quality, market shares and overall profits for organizations. Sriyogi et al. (2013) stated that there is knowledge gap in the existing supply chain management literature, especially on sustainable practices followed by emerging-markets. Dutta et al. (2020) studied the digital transformation priority of Indian discrete manufacturing Industries. Bhanot et al. (2015) focused on reducing high dependency on non-renewable natural resources(SM) leading to environmental pollution. Molamohamadi & Ismail (2013) stated that sustainable practices of manufacturing prove to be more relevant in modern time. Mittal and Sangwan (2014) studied the barriers affecting the implementation of green manufacturing, on all three aspects of sustainability. Fairfield et al. (2012) illuminates the factors influencing companies to implement sustainability practices. Luthra et al. (2011) observed that green supply chain management have attracted focus in recent time. Keivanpour et al. (2013) stated that for sustainable development, strategies focusing on environment, economical and social issues should be planned. Non-sustainable industrial development in emerging economies is creating grievous environmental and social threats (Mittal et al., 2013). Ghazilla et al. (2015) observed that depleting natural resources, carbon foot prints and other waste management issues are forcing manufacturing industries to stick to regulations related to environment. Mohanty et al. (2002) stated that in modern time modifications in materials, products, and processes is highly influenced by the sustainability. Growing population all over the globe is putting extra pressure on natural resources. New development in technologies needs to focus on efficient utilization of resources and sustainable practices (Davidson et al., 2010). Bhamu et al. (2011) stated that manufacturing practices like lean and green should focus on improving production efficiency while derogating their environmental and social effects. Zubir et al. (2012) studied the issues of sustainability in automotive industries of Malaysia. Bhanot et al. (2015) focused on use of renewable energy for sustainable indus1808

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trial development. Gupta et al. (2015) observed that sustainable practices are important for success of manufacturing business in modern time. Kulatunga et al. (2013) describes & investigated the challenges faced in the adaptation of flexible & green concept so as to solve the current issues faced by industries in manufacturing sector. Lakhan et al. (2020) studied the challenges of sustainable manufacturing in Indian manufacturing organization.

CHALLENGES OF SUSTAINABLE MANUFACTURING Sustainability covers all three environmental, economic and social aspects. With spread of Information technology (IT) through Industry 4.0 (I4.0) in manufacturing, adoption of sustainable practices have become more relevant. In developing economies, focusing of these practices is utmost important. Luthra et al. (2011) stated that for developing economies such as India sustainable Industrial development is required. Kumar (2020a) observed that sustainable supply chain management in digital era is a challenging task. Kumar (2020b) analyzed the enablers of Industry 4.0 in Indian scenario. From the literature reviews and expert opinions, Author has identified fifteen challenges to implement sustainable manufacturing in Indian industries. A survey has been conducted, among academia and industries experts to collect response on challenges of sustainable manufacturing. An opinion form or questionnaire according to DEMATEL technique has been made for collecting response from experts. The questionnaire was designed in the matrix format with a rating of 0-4 scales (such as: No influence, 0; Very low influence, 1; Low influence, 2; High influence, 3; Very High influence, 4). Challenges identified for sustainable manufacturing have been discussed in tables shown below. Table 1. Environmental Challenges S.No.

Environmental Challenges(EN)

Code

1.

Lack of awareness about green practices

EN1

2.

Lack of service orientation manufacturing & End-to-end engineering

EN2

3.

Lack of standards or benchmarks on green practices

EN3

4.

Low enforcement & Lack of support from top management on green practices

EN4

5.

Negative attitudes towards green practices

EN5

6.

Weak legislation on green practices

EN6

7.

Lack of technological supports on Green practices

EN7

Table 2. Economical Challenges S. No.

Economical Challenges (EC)

Code

1.

Neglected approach & Lack of dedicated funds for sustainable projects

EC1

2.

High initial cost of sustainable practices

EC2

3.

Uncertain benefits & Trade-Offs

EC3

4.

Less supportive existing organizational resources

EC4

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Table 3. Social Challenges S. No.

Social Challenges (SC)

Code

1.

Lack of awareness among customers about sustainable product.

SC1

2.

Lack of guidelines and rules on sustainability

SC2

3.

Employee resistance for new practices

SC3

4.

Lack of training for workers about sustainable practices

SC4

DEMATEL ANALYSIS OF CHALLENGES DEMATEL approach analyses the cause and effect relationship between factors. The DEMATEL gives mutuality between factors and help in showing relationship through a map. This technique commutes the mutuality relationships as cause and effect group relationship via matrixes and also helps in identifying the critical factors with the help of an affect relation diagram. Results obtained from DEMATEL methodology are shown in table 4, 5, 6 and 7. The steps for DEMATEL method are discussed in Figure 1. Figure 1. A DEMATEL methodology flow chart

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Table 4. Average Direct relationship matrix (A) Code

EN1

EN2

EN3

EN4

EN5

EN6

EN7

EC1

EC2

EC3

EC4

SC1

SC2

SC3

SC4

SUM

EN1

0

2.5

3

2.5

4

3.5

3.5

4

3

3.5

3.5

3.5

3

3.5

3.5

46.5

EN2

2.5

0

2.5

3

2.5

3

4

3.5

3

3

4

2

1.5

3

3

40.5

EN3

2.5

2.5

0

1

2.5

4

2.5

1.5

2.5

2.5

2.5

2.5

1

1.5

2.5

31.5

EN4

2.5

1

1.5

0

3

2.5

2.5

4

1

3

3

3

3.5

3.5

2

36

EN5

3.5

2.5

2.5

3.5

0

3

3.5

4

2

2.5

3.5

3.5

4

3

3.5

44.5

EN6

4

1.5

3.5

2.5

2.5

0

4

3

2

4

2.5

3

3

2.5

4

42

EN7

3

3.5

2.5

3

4

3.5

0

3.5

3

3.5

3

2.5

2

1

2.5

40.5

EC1

3.5

2.5

1

3.5

3

3.5

3.5

0

3

2.5

3

3

3.5

1.5

3.5

40.5

EC2

2.5

3.5

1.5

3

4

3

3

2.5

0

4

3.5

3

3

0.5

2.5

39.5

EC3

3

1

1.5

2

3

3.5

4

3

3

0

2

1.5

2

2.5

1

33

EC4

3.5

2.5

1

2.5

3.5

3

3

3.5

3.5

3

0

2.5

2.5

3

1.5

38.5

SC1

3.5

1.5

1

1.5

3

2.5

2

3.5

3.5

2.5

2.5

0

3.5

1

3.5

35

SC2

4

1

1.5

2

3.5

3

2.5

3

2.5

2

3

3.5

0

1.5

4

37

SC3

3

1.5

2.5

3.5

4

2.5

1

3

3.5

2

3.5

1

2

0

2.5

35.5

SC4

3.5

2

1

1

2

2

3

4

3.5

2

3

2

3

1.5

0

33.5

SUM

44.5

29

26.5

34.5

44.5

42.5

42

46

39

40

42.5

36.5

37.5

29.5

39.5

Table 5. The sum of influences given and received on challenges of Sustainable Manufacturing Challenge Code

D

R

D+R

D-R

EN1

5.808397

5.622495

11.43089

0.185902

EN2

5.101368

3.748258

8.849626

1.35311

EN3

4.040698

3.39561

7.436308

0.645088

EN4

4.560193

4.42124

8.981433

0.138953

EN5

5.574572

5.589128

11.1637

-0.14556

EN6

5.247856

5.333949

10.58181

-0.08609

EN7

5.139554

5.34841

10.48796

-0.20886

EC1

5.141533

5.804251

10.94578

-0.66272

EC2

5.010082

4.91266

9.922742

0.097422

EC3

4.26884

4.805995

9.074835

-0.53716

EC4

4.927861

5.314835

10.2427

-0.38697

SC1

4.48835

4.691826

9.180176

-0.20348

SC2

4.724573

4.847378

9.571951

-0.12281

SC3

4.530964

4.847378

9.378342

-0.31641

SC4

4.058821

5.009055

9.067876

-0.95023

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Table 6. The prominence vector (D+R) & ranking

Table 7. The Cause and Effect division of challenges

Rank

Code

D+R

1

EN1

11.43089

Cause

Cause Group- criteria

D-R

EN2

1.35311

2

EN5

11.1637

1

3

EC1

10.94578

2

EN3

0.645088

4

EN6

10.58181

3

EN1

0.185902

5

EN7

10.48796

4

EN4

0.138953

EC2

0.097422

6

EC4

10.2427

5

7

EC2

9.922742

Effect

Effect Group- criteria

D-R

8

SC2

9.571951

1

SC4

-0.95023

9

SC3

9.378342

2

EC1

-0.66272

EC3

-0.53716

10

SC1

9.180176

3

11

EC3

9.074835

4

EC4

-0.38697

12

SC4

9.067876

5

SC3

-0.31641

13

EN4

8.981433

6

EN7

-0.20886

SC1

-0.20348

14

EN2

8.849626

7

15

EN3

7.436308

8

EN5

-0.14556

9

SC2

-0.12281

10

EN6

-0.08609

OBSERVATIONS AND CONCLUSION The results of DEMATEL analysis are represented in Figure 2. Normally all challenges are divided in two groups of cause and effect. Figure 2. The casual diagram for sustainable manufacturing challenges

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Five challenges obtained in the cause group are lack of awareness about green practices (EN1), lack of service orientation manufacturing & End-to-end engineering (EN2) and lack of standards or benchmarks on green practices(EN3), Low enforcement & Lack of support from top management on green practices(EN4) and High initial cost of sustainable practices(EC2). Similarly, other ten challenges like lack of training for workers about sustainable practices (SC4), neglected approach & lack of dedicated funds for sustainable projects (EC1), Uncertain benefits & Trade-Offs(EC3), Less supportive existing organizational resources(EC4), Employee resistance for new practices(SC3), lack of technological supports on Green practices(EN7), Lack of awareness among customers about sustainable product(SC1), Negative attitudes towards green practices(EN5), Weak legislation on green practices(EN6) and Lack of guidelines and rules on sustainability(SC2) are shown in the effect group. Results show that (D-R) values are positives; this implies that their degree of impact (D) is greater than the degree of influence impact (R). From cause group, lack of service orientation manufacturing & End-to-end engineering (EN2) challenge ranks first based on a high DEMATEL score (1.35311). From the casual diagram (Figure 2), lack of training for workers about sustainable practices (SC4) challenge has the lowest priority with fewer points (-0.95023). From the manufacturing industries point of view, these challenges are not given much importance as compared to neglected approach & lack of dedicated funds for sustainable projects (EC1), uncertain benefits & trade-offs (EC3) challenges is the next precedence. Observation from this study can help the policymakers and strategy planners of manufacturing organizations in better understanding of sustainability and its aspects. Further it can help in developing policies on sustainable manufacturing on national and international level both in developed and developing economies.

DISCUSSION QUESTIONS IN CLASS ROOMS Q.1.What are different elements of industry4.0? Q.2.What are different aspects of sustainability? Q.3.What are different challenges to sustainable manufacturing? Q.4.Why sustainable manufacturing is need of hour?

REFERENCES Bhamu, J. P., Bhakar, V., & Sangwan, K. S. (2011).Integrated Lean Management System for Sustainable Development: A Conceptual Model. International Conference on Sustainable Manufacturing: Issues, Trends and Practices, 235-238. Bhanot, N., Rao, P. V., & Deshmukh, S. G. (2015). Sustainable manufacturing: An interaction analysis for machining parameters using graph theory. Procedia: Social and Behavioral Sciences, 189, 57–63. doi:10.1016/j.sbspro.2015.03.192 Davidson, C. I., Hendrickson, C. T., Matthews, H. S., Bridges, M. W., Allen, D. T., Murphy, C. F., & Austin, S. (2010). Preparing future engineers for challenges of the 21st century: Sustainable engineering. Journal of Cleaner Production, 18(7), 698–701. doi:10.1016/j.jclepro.2009.12.021

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Dutta, G., Kumar, R., Sindhwani, R., & Singh, R. (2020). Digital transformation priorities of India’s discrete manufacturing SMEs – a conceptual study in perspective of Industry 4.0. Competitiveness Review, 30(3), 289–314. Advance online publication. doi:10.1108/CR-03-2019-0031 Ghazilla, R. A. R., Sakundarini, N., Abdul-Rashid, S. H., Ayub, N. S., Olugu, E. U., & Musa, S. N. (2015). Drivers and barriers analysis for green manufacturing practices in Malaysian SMEs: A preliminary findings. Procedia CIRP, 26, 658–663. doi:10.1016/j.procir.2015.02.085 Gupta, S., Dangayach, G. S., & Singh, A. K. (2015). Key determinants of sustainable product design and manufacturing. Procedia CIRP, 26, 99–102. doi:10.1016/j.procir.2014.07.166 Haapala, K. R., Zhao, F., Camelio, J., Sutherland, J. W., Skerlos, S. J., Dornfeld, D. A., Jawahir, I. S., Clarens, A. F., & Rickli, J. L. (2013). A review of engineering research in sustainable manufacturing. Journal of Manufacturing Science and Engineering, 135(4), 041013. doi:10.1115/1.4024040 Kayikci, Y. (2018). Sustainability impact of digitization in logistics. Procedia Manufacturing, 21, 782–789. doi:10.1016/j.promfg.2018.02.184 Kulatunga, A. K., Jayatilaka, P. R., & Jayawickrama, M. (2013). Drivers and barriers to implement sustainable manufacturing concepts in Sri Lankan manufacturing sector. doi:10.14279/depositonce-3753 Kumar, R. (2020a). Sustainable Supply Chain Management in the Era of Digitalization: Issues and Challenges. In Handbook of Research on Social and Organizational Dynamics in the Digital Era. IGI Global. Kumar, R. (2020b). Espousal of Industry 4.0 in Indian manufacturing organizations: Analysis of enablers. In Handbook of Research on Engineering Innovations and Technology Management in Organizations. IGI Global. |Doi:10.4018/978-1-7998-2772-6 Lakhan, K. R., Tyagi, P., Nagar, L., & Gaur, D. (2020). Challenges of Sustainable Manufacturing for Indian Organization: A Study. In Recent Advances in Mechanical Infrastructure (pp. 33-39). Springer. Luthra, S., Kumar, V., Kumar, S., & Haleem, A. (2011). Barriers to implement green supply chain management in automobile industry using interpretive structural modeling technique: An Indian perspective. Journal of Industrial Engineering and Management, 4(2), 231–257. doi:10.3926/jiem.2011.v4n2.p231-257 Mittal, V. K., Egede, P., Herrmann, C., & Sangwan, K. S. (2013). Comparison of drivers and barriers to green manufacturing: a case of India and Germany. In Re-engineering Manufacturing for Sustainability (pp. 723–728). Springer. doi:10.1007/978-981-4451-48-2_118 Mittal, V. K., & Sangwan, K. S. (2014). Prioritizing barriers of green manufacturing: Environmental, Social and Economic perspectives. Procedia CIRP, 17, 559–564. doi:10.1016/j.procir.2014.01.075 Mohanty, A. K., Misra, M., & Drzal, L. T. (2002). Sustainable bio-composites from renewable resources: Opportunities and challenges in the green materials world. Journal of Polymers and the Environment, 10(1-2), 19–26. doi:10.1023/A:1021013921916 Molamohamadi, Z., & Ismail, N. (2013). Developing a new scheme for sustainable manufacturing. International Journal of Materials Mechanics and Manufacturing, 1(1), 1–5.

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Nambiar, A. N. (2010, January). Challenges in sustainable manufacturing. In Proceedings of the 2010 international conference on industrial engineering and operations management (pp. 9-10). Academic Press. Sriyogi, K., Agrawal, R., & Sharma, V. (2013). Sustainable Supply Chain Management Practices in Indian Manufacturing Firms a Case Based Research. Academic Press. Zubir, A. F. M., Habidin, N. F., Conding, J., Jaya, N. A. S. L., & Hashim, S. (2012). The development of sustainable manufacturing practices and sustainable performance in Malaysian automotive industry. Journal of Economics and Sustainable Development, 3(7), 130–138.

This research was previously published in the Handbook of Research on IT Applications for Strategic Competitive Advantage and Decision Making; pages 241-249, copyright year 2020 by Business Science Reference (an imprint of IGI Global).

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Chapter 92

Investigation of Operational Characteristics of Mechatronic Systems in Industry 4.0 Raul Turmanidze Georgian Technical University (GTU), Georgia Predrag V. Dašić https://orcid.org/0000-0002-9242-274X High Technical Mechanical School, Trstenik, Serbia Giorgi Popkhadze Georgian Technical University (GTU), Georgia

ABSTRACT This work presents the results of an analysis of the main expected potential problems that may occur in the implementation of the Industry 4.0 reform. It is proved that the pace and level of development of this reform will largely be determined by the effectiveness of the used mechatronic systems. It has also been established that as a result of systematic miniaturization of the nodes of radio-electronic equipment and microelectronic equipment and microelectronic technology, the main problem of these reforms and the implementation of complex technological processes is instrumental support, especially cutting micro-tools. Therefore, the examples of these micro-tools show methods for improving their performance characteristics.

INTRODUCTION To date, almost every scientist in any country knows and is unequivocally recognized that at the beginning of the XXI century the whole world is at the turn of the fourth scientific and technological revolution, which fundamentally should change the style and level of thinking, the rules of life for every person and especially the young generation in all countries of the world. This is due to the fact that according to DOI: 10.4018/978-1-7998-8548-1.ch092

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 Investigation of Operational Characteristics of Mechatronic Systems in Industry 4.0

many scholars and authors of large-profiled studies on the state of the necessary conditions for a worthy meeting of major reforms impending change is evaluated as the most comprehensive and ambitious in the history of mankind. It will be held under the abbreviated name “Industry - 4” (Turmanidze, Bachanadze & Popkhadze, 2017; Turmanidze, Bachanadze & Popkhadze, 2018; Turmanidze, Dašić & Popkhadze, 2018a; Turmanidze, Dašić & Popkhadze, 2018b; Turmanidze, Dašić, Popkhadze & Borodavko, 2018). During the first industrial revolution, which lasted for more than two centuries for the mechanization of certain operations of industry water and steam were used. As a result, the second revolution based on electricity were created mass production of many products in different areas of the economy. During the third revolution using electronic and information technology production processes have become automated. Now, based on the results of the third revolution is developing the fourth revolution, which is based on digital technologies, the development of which was started in the second half of the last century. It involves a merger of several modern technologies and the disappearance of all boundaries between physical, digital and biological spheres, is the creation of a cyber-physical systems. In other words, the final goal of the “Industry 4.0” reform is full automation and remote control of complex technological processes and administrative and financial operations by using super modern mechatronic systems (Turmanidze & Gviniashvili, 2011; Tzou, 1998; Van Beek, Erden & Tomiyama, 2010; Wang et al., 2005; Yu et al., 2008; Zhang et al., 2009). Results of the first three revolutions were general and applicable to all countries, for each enterprise and, in practice, for each person. However, the process of the fourth degree of the revolution and the consistent use of the results of its separate stages in practice will have a peculiar character for various industries. Of course, the basic principles are common, but since each individual branch has its own special modern multicenter and multivariable technology to their design and management will need special knowledge and individual approach. To create the above-mentioned mechatronic systems, that determine the level and pace of the development of the “Industry 4.0” reform it Requires high-precision technological equipment and special micro-tools for different purposes. We still in the 90s of the last century made a classification of all the basic micro-tools used in microelectronics and microelectronic technology, which are divided into three main groups. Cutting, mounting and assembly. Each group includes subgroups with different tool sizes and specific areas of their use. For a significant increase in the reliability of microcircuits and, accordingly, the final product, one more group of micro-tools must be marked - control tools - devices that enable us to check before boarding on the PCB. We can check all the operational characteristics of the microchip that is already installed on electrical, mechanical and thermal changes. We have designed, manufactured, tested and patented several options for such devices for different sizes of microcircuits and their housings. Even the technological equipment-stamps and molds for their production have been created. After testing and selection of microcircuits for such methods, it is possible to exclude early premature failures of complex microcircuits and expensive equipment and devices during operation, which provides a great economic effect. The design and operation of the above-mentioned micro-tools will be similarly reported in the presentation. All these tools are used quite a lot, since a significant part of modern technology, from everyday to space equipment, Is a set of mechanical nodes, hydro and pneumatic equipment and microelectronic blocks or entire control systems, that is, a complex mechatronic system. For their manufacture requires the implementation of many technological operations of different profiles.

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 Investigation of Operational Characteristics of Mechatronic Systems in Industry 4.0

However, it should be noted that among them, especially in large numbers, cutting micro-instruments are used, especially spiral drills. This is due to the fact that in the process of producing nodes of mechatronic systems, It is often necessary to treat holes of small diameters (about 1 mm or less),especially on parts of hydro and pneumatic equipment. As for the production of microelectronic nodes, in the technological processes for the production of basic parts - printed circuit boards, a significant part of the work comes at the drilling operations (Adamia, Gviniashvili & Bachanadze, 2009) of a huge number of holes of small diameter. To obtain holes in printed circuit boards, different methods are used, but practice has shown that the most acceptable method, especially when processing multilayered PCBs with subsequent metallization of hole surfaces, is drilling to this day. Carry out drilling of micro carbide drill geometry, which has multiple experiments an experiences relevant production. In particular: the optimum cutting angle and spiral angle grooves respectively is 300, and the rear angle 180. They are refaced through each hole and 1000 are designed for 3-4 regrinding costs. Production of printed circuit boards is mass production, where performance is carried out with the aim of increasing the drilling package, composed of several plates, it has a place of deep-hole drilling, where the drill depth exceeds the diameter of 8-10 times. Downtimes of expensive technological equipment, especially in mass production are associated with significant economic losses. In the production of printed circuit boards easy connected not only with the replacement of the tool with the aim of reshaping, but unexpected, caused by fragile destruction even before the first reshaping. Probability of brittle fracture grows significantly during deep drilling package of printed circuit boards. When this zone is located in the near destruction of the end of the spiral grooves.

LITERATURE REVIEW Mechatronics (Mechanisms and Electronics or Mechanics and Electronics) is the field of technology in which the multidisciplinary approach is applied, ie. knowledge, skills and concepts in modern mechanical engineering, electronics, electrical engineering, automatic control and software engineering in the development, design and production of new products and production systems. The term mechatronics has become a general synonym for the integration of these technologies and new technological trends in various fields. Examples of mechatronic systems are, for example: various types of automata and hybrid systems, numerically controlled (NC) machines, automatic production lines, flexible manufacturing systems (FMS), integrated and complex technical systems, mechanical products with microprocessor and similar electronic components, and everyday equipment such as such as autofocus cameras, video devices, hard drives, CD players, washing machines, etc. The word “mechatronics” was first used by a senior engineer at Japan’s company Yaskawa in 1969. First article for the field of “mechatronic system” which is indexed in Scopus citation database is published in 1984, while the following article from 1990. The total number of articles published for the field of “mechatronic system” in the Scopus for the period