Big Data Analytics in Supply Chain Management: Theory and Applications 2020036449, 2020036450, 9780367407179, 9780367816384

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Big Data Analytics in Supply Chain Management

Big Data Analytics in Supply Chain Management Theory and Applications

Edited by

Iman Rahimi, Amir H. Gandomi, Simon James Fong, and M. Ali Ülkü

First edition published 2021 by CRC Press 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742 and by CRC Press 2 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN © 2021 Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, LLC Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, access www.copyright. com or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. For works that are not available on CCC please contact mpkbookspermissions@ tandf.co.uk Trademark notice: Product or corporate names may be trademarks or registered trademarks and are used only for identification and explanation without intent to infringe. Library of Congress Cataloging-in-Publication Data Names: Rahimi, Iman, editor. | Gandomi, Amir Hossein, editor. | Fong, Simon, editor. | Ülkü, Muhammed Ali, editor. Title: Big data analytics in supply chain management : theory and applications / edited by Iman Rahimi, Amir H. Gandomi, Simon James Fong and M. Ali Ülkü. Description: First edition. | Boca Raton : CRC Press, 2021. | Includes bibliographical references and index. Identifiers: LCCN 2020036449 (print) | LCCN 2020036450 (ebook) | ISBN 9780367407179 (hbk) | ISBN 9780367816384 (ebk) Subjects: LCSH: Business logistics. | Big data. Classification: LCC HD38.5 .B53 2021 (print) | LCC HD38.5 (ebook) | DDC 658.70285/57–dc23 LC record available at https://lccn.loc.gov/2020036449 LC ebook record available at https://lccn.loc.gov/2020036450 ISBN: 978-0-367-40717-9 (hbk) ISBN: 978-0-367-81638-4 (ebk) Typeset in Times by codeMantra

This book is affectionately dedicated to all those in the front lines fighting against the COVID-19 pandemic and also, to my family – Dr. Rahimi to Elnaz – Dr. Gandomi to Mary-Helen Alexandra and Naomi Fatma-Anne – Dr. Ülkü

Contents Preface.......................................................................................................................ix Acknowledgments .....................................................................................................xi Editors..................................................................................................................... xiii Contributors.............................................................................................................. xv Chapter 1

Big Data Analytics in Supply Chain Management: A Scientometric Analysis ..................................................................... 1 Iman Rahimi, Amir H. Gandomi, M. Ali Ülkü, and Simon James Fong

Chapter 2

Supply Chain Analytics Technology for Big Data................................ 9 Sivagnanam Rajamanickam Mani Sekhar, Swathi Chandrashekar, and Siddesh Gaddadevara Matt

Chapter 3

Prioritizing the Barriers and Challenges of Big Data Analytics in Logistics and Supply Chain Management Using MCDM Method........................................................................ 29 Mehdi Keshavarz-Ghorabaee, Maghsoud Amiri, Mohammad Hashemi-Tabatabaei, and Mohammad Ghahremanloo

Chapter 4

Big Data in Procurement 4.0: Critical Success Factors and Solutions ...................................................................................... 45 Bernardo Nicoletti and Andrea Appolloni

Chapter 5

Recommendation Model Based on Expiry Date of Product Using Big Data Analytics ................................................................... 65 Abhisekh Kumar Singh, Maheswari Raja, and Azath Hussain

Chapter 6

Comparing Company’s Performance to Its Peers: A Data Envelopment Approach....................................................................... 79 Tihana Škrinjaric´

Chapter 7

Sustainability, Big Data, and Consumer Behavior: A Supply Chain Framework ............................................................................. 109 Brianna A. Currie, Alexandra D. French, and M. Ali Ülkü

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Chapter 8

Contents

A Soft Computing Techniques Application of an Inventory Model in Solving Two-Warehouses Using Cuckoo Search Algorithm ......................................................................................... 133 Ajay Singh Yadav, Anupam Swami, Navin Ahlawat, and Srishti Ahlawat

Chapter 9

An Overview of the Internet of Things Technologies Focusing on Disaster Response ....................................................................... 151 Reinaldo Padilha França, Ana Carolina Borges Monteiro, Rangel Arthur, and Yuzo Iano

Chapter 10 Closing the Big Data Talent Gap ...................................................... 169 Curtis Breville Index ...................................................................................................................... 189

Preface Ever evolving, the main characteristics of Big Data (BD) are now expanded into “5V” concept consisting of Volume, Velocity, Variety, Veracity, and Value. As BD has been transitioning from an emerging topic to now an immensely growing research field, it has become essential to classify the different types and examine the general trends of this research area. The continuous efforts to create more sophisticated technology to gather data at different steps of supply chain operations and management have engendered the new era of supply chain analytics. By utilizing BD, companies such as Amazon, UPS, and Wal-Mart are gaining unprecedented mastery and competitive advantage with their supply chains. Such optimized efficiencies and visibility of inventory levels, order fulfillments, material sourcing, and product delivery are achieved by predictive data analytics to adjust supply with demand; leveraging new planning strengths to optimize their sales channel strategies; optimizing supply chain strategy and competitive priorities; even launching powerful new ventures. The concurrence of events such as growth in the approval of supply chain technologies, data inundation, and a shift in management focus from heuristics to data-driven decision-making have collectively resulted in the rise of the Big Data Analytics (BDA) era. In spite of these opportunities, many supply chain operations are gaining little to no value from BD. From sourcing to consumer purchasing behavior to logistics services, if well-utilized, BD is poised to help management with more informed, timely, and robust supply chain decisions. In this edited book, the contributors discuss the outcomes of recent large-scale achievements on BDA topics in relation to Supply Chain Management (SCM). That is, this book aims to showcase a diversity of SCM issues that may benefit from BDA, both in theory and practice. This book shows a representative sampling of real-world problems as well as discussing some diverse features related to the use of Big Data in real-world applications. Big Data Analytics in Supply Chain Management provides a review of the stateof-the-art progress in developing the field of Big Data and reveals the applicability of Big Data approaches to tackle real-world supply chain problems. It is tailored to the audiences of engineers in industries, researchers, students, faculty members, and operations research and industrial engineering from academia, who work mainly on Big Data in supply chain problems. To facilitate this goal, Chapter 1 presents a scientometric analysis to analyze scientific literature in Big Data analytics in supply chain management. Chapter 2 focuses on supply chain analytics technologies for Big Data that are capable of assisting a firm, organization, or company in taking rapid, intelligent, cost-effective, and more efficient decisions. Chapter 3 addresses the barriers and challenges of Big Data analysis in supply chain management. Chapter 4 presents an exhaustive set of critical success factors for procurement 4.0 based on a summary of the actual results and of a survey in organizations undergoing digital transformation. Chapter 5 introduces product recommendations based on expiry date using collaborative filtering and content-based filtering and targets to minimize the unsold expired products using data analytics. ix

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Preface

Chapter 6 focuses on a nonparametric approach, Data Envelopment Analysis, for comparing a company’s business performance. Chapter 7 studies consumer choice factors such as price, utility, quality, service, and return policy as they pertain to sustainable products and a new supply chain framework is proposed to study how Big Data can be employed as an effective tool for influencing purchase behavior toward sustainable consumption, while improving the triple bottom line. In Chapter 8, a special case of a cost-minimizing inventory model is studied using a nature-inspired (cuckoo search) algorithm. Chapter 9 provides an updated overview of IoT technologies as well as focusing on disaster response and management, showing the approach and successful relationship, with a concise bibliographic background, categorizing and synthesizing the potential of technology. Finally, Chapter 10 provides the results of a qualitative exploratory case study conducted to understand where business leaders in the U.S. are looking to fill their BD specialist job openings. This book is essential for research students at all levels, and we hope it will be used as a supplement textbook for several types of courses, including operations research, computer science, statistics, and many fields of science and engineering related to supply chain management problems. Iman Rahimi Universiti Putra Malaysia, Malaysia Amir H. Gandomi Faculty of Engineering and IT, University of Technology, Sydney, Australia Simon James Fong University of Macau, Macau M. Ali Ülkü Rowe School of Business, Dalhousie University, Canada

Acknowledgments We, the editors, are grateful to each and every author who contributed to our edited book. We would also like to extend our appreciation to all reviewers for their time, critical review of the chapters, and their insightful comments and constructive suggestions provided in several rounds.

REVIEWER COMMITTEE Anju S Pillai, Amrita Vishwa Vidyapeetham Daniel Pacheco Lacerda, UNISINOS – Universidade do Vale do Rio dos Sinos Devi Mani, King Khalid University Dheeraj Malhotra, GGS IP University, Fabio Antonio Sartori Piran, Feevale University Francisco Gaudêncio Mendonça Freires, Federal University of Bahia Jiayi Liu, Wuhan University of Technology Jui-Long Hung, Boise State University Jayakrishna Kandasamy, Vellore Institute of Technology Massimo Merlino, University of the Republic of San Marino Mohammad Kabir Hassan, University of New Orleans Muhammad Sulaiman, Abdul Wali Khan University Mardan Nor Liyana Mohd Shuib, University of Malaya Nadia Al-Aboody, Southern Technical University Poulami Das, The Neotia University Raheleh Khanduzi, Gonbad Kavous University Sanjeev Swami, Dayalbagh Educational Institute

We also thank the project team at CRC Press, for supporting us in the development and completion of this project. We hope the readers will find this book a valuable contribution to the emerging body of knowledge in Big Data Analytics in Supply Chain Management. Iman Rahimi, PhD Amir H Gandomi, PhD Simon Fong, PhD M. Ali Ülkü, PhD

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Editors Iman Rahimi, PhD, earned his BSc (Applied Mathematics) in 2009, MSc (Applied Mathematics – Operations Research) in 2011 and his PhD in the Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, Universiti Putra Malaysia, Malaysia, in 2017. He will join as a research scholar to University of Technology, Sydney, Australia, in 2021. His research interests include machine learning, optimization, and supply chain management. He has edited a book entitled Evolutionary Computation in Scheduling with Wiley. He has served as an editor for the following journals: Computational Research Progress in Applied Science & Engineering (CRPASE), International Journal of Renewable Energy Technology (IJRET), and International Journal of Advanced Heuristic and Meta-Heuristic Algorithms. Also, Dr. Rahimi has been an editor and coeditor of books for some prestige publishers (Elsevier and Taylor & Francis). Amir H. Gandomi, PhD, is a Professor of Data Science at the Faculty of Engineering and Information Technology, University of Technology Sydney. Prior to joining UTS, Prof. Gandomi was an Assistant Professor at the School of Business, Stevens Institute of Technology, USA, and a distinguished research fellow in BEACON Center, Michigan State University, USA. Prof. Gandomi has published more than 200 journal papers and seven books which collectively have been cited more than 17,000 times (H-index = 60). He has been named as one of the most influential scientific minds and Highly Cited Researcher (top 1%) for four consecutive years, 2017 to 2020. He also ranked 18th in GP bibliography among more than 12,000 researchers. He has served as an associate editor, editor, and guest editor on several prestigious journals such as AE of SWEVO, IEEE TBD, and IEEE IoTJ. Prof. Gandomi is active in delivering keynotes and invited talks. His research interests are global optimization and (big) data analytics using machine learning and evolutionary computations, in particular.

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Editors

Simon James Fong, PhD, graduated from La Trobe University, Australia, with a First Class Honours BEng Computer Systems degree and a PhD Computer Science degree in 1993 and 1998, respectively. Dr. Fong is now working as an Associate Professor at the Computer and Information Science Department of the University of Macau. He is a cofounder of the Data Analytics and Collaborative Computing Research Group in the Faculty of Science and Technology. Prior to his academic career, Dr. Fong took up various managerial and technical posts, such as systems engineer, IT consultant, and e-commerce director in Australia and Asia. Dr. Fong has published more than 500 international conference and peer-reviewed journal papers, mostly in the areas of data mining, data stream mining, big data analytics, meta-heuristics optimization algorithms, and their applications. He serves on the editorial boards of the Journal of Network and Computer Applications of Elsevier, IEEE IT Professional Magazine, and various special issues of SCIE-indexed journals. Dr. Fong is also an active researcher with leading positions such as Vice Chair of IEEE Computational Intelligence Society (CIS) Task Force on Business Intelligence  & Knowledge Management and Vice Director of International Consortium for Optimization and Modeling in Science and Industry (iCOMSI). M. Ali Ülkü, PhD, is a Full Professor of Supply Chain and Decision Sciences, and the Director of the Centre for Research in Sustainable Supply Chain Analytics-CRSSCA, in the Rowe School of Business at Dalhousie University, Halifax, Nova Scotia, Canada. Dr. Ülkü is also crossappointed with the Department of Industrial Engineering and the School for Resource and Environmental Studies. He earned his PhD in Management Sciences from the University of Waterloo, MSc in Operations Research from Çukurova University, and BSc in Industrial Engineering from Bilkent University. Prior to his academic career, he worked as a productivity consultant in the largest international brewery in Turkey. Dr. Ülkü’s research interests include the theoretical modeling of sustainable supply chain and logistics systems, operations-marketing interface, and mathematical modeling of consumer behavior and societal problems. He published in such journals as Annals of Operations Research, European Journal of Operational Research, International Journal of Production Economics, Journal of Business Research, Journal of Cleaner Production, and Service Science. His research funding includes those from The Natural Sciences and Engineering Research Council of Canada, The Scientific and Technological Research Council of Turkey, and The United States National Science Foundation. A recipient of the Exceptional Teaching Award from the University of Waterloo, Dr. Ülkü has taught operations management, business analytics, logistics, and supply chain management courses at various universities in Canada, Turkey, and the USA. He served as the Program Chair for the 2018 Canadian Operational Research Society Conference. The IEOM Society International honored him with the 2019 Distinguished Professor Award.

Contributors Navin Ahlawat Department of Computer Science SRM Institute of Science and Technology Ghaziabad, India Srishti Ahlawat SRM Institute of Science and Technology Ghaziabad, India Maghsoud Amiri Department of Industrial Management, Faculty of Management and Accounting Allameh Tabataba’i University Tehran, Iran Andrea Appolloni Università di Tor Vergata Rome, Italy Rangel Arthur Faculty of Technology (FT) State University of Campinas (Unicamp) Campinas, Brazil Curtis Breville DM/IST, Dell Technologies Denver, Colorado, USA Swathi Chandrashekar Department of Information Science & Engineering Ramaiah Institute of Technology Bangalore, India

Brianna A. Currie Centre for Research in Sustainable Supply Chain Analytics Rowe School of Business Dalhousie University Halifax, Canada Simon James Fong Department of Computer and Information Science University of Macau Macau, China Reinaldo Padilha França State University of Campinas (Unicamp) Campinas, Brazil Alexandra D. French Centre for Research in Sustainable Supply Chain Analytics School for Resource and Environmental Studies Dalhousie University Halifax, Canada Amir H. Gandomi Faculty of Engineering & Information Technology University of Technology Sydney Ultimo, Australia Mohammad Ghahremanloo Department of Management Faculty of Industrial Engineering and Management Shahrood University of Technology Shahrood, Iran

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Mohammad Hashemi-Tabatabaei Department of Industrial Management Faculty of Management and Accounting Allameh Tabataba’i University Tehran, Iran Azath Hussain School of Computing Science and Engineering Vellore Institute of Technology (VIT) Bhopal University, India Yuzo Iano State University of Campinas (Unicamp) Campinas, Brazil Mehdi Keshavarz-Ghorabaee Gonbad Kavous University Gonbad Kavous, Iran Siddesh Gaddadevara Matt Department of Information Science & Engineering Ramaiah Institute of Technology Bangalore, India Ana Carolina Borges Monteiro State University of Campinas (Unicamp) Campinas, Brazil Bernardo Nicoletti Temple University Rome, Italy Iman Rahimi Department of Mechanical and Manufacturing Engineering Faculty of Engineering Universiti Putra, Malaysia Malaysia

Contributors

Maheswari Raja Vellore Institute of Technology Chennai, India Sivagnanam Rajamanickam Mani Sekhar Department of Information Science and Engineering Ramaiah Institute of Technology Bangalore, India Abhisekh Kumar Singh Vellore Institute of Technology (VIT) Chennai, India Tihana Škrinjariü Department of Mathematics Faculty of Economics and Business University of Zagreb Zagreb, Croatia Anupam Swami Department of Mathematics Government Post Graduate College Sambhal, India M. Ali Ülkü Centre for Research in Sustainable Supply Chain Analytics Rowe School of Business Dalhousie University Halifax, Canada Ajay Singh Yadav Department of Mathematics SRM Institute of Science and Technology Ghaziabad, India

1

Big Data Analytics in Supply Chain Management A Scientometric Analysis Iman Rahimi Universiti Putra Malaysia

Amir H. Gandomi University of Technology Sydney

M. Ali Ülkü Dalhousie University

Simon James Fong University of Macau

CONTENTS 1.1 1.2

Introduction ...................................................................................................... 1 Analysis ............................................................................................................ 2 1.2.1 Data Collection ..................................................................................... 2 1.3 Scientometric Analysis ..................................................................................... 2 1.3.1 An Analysis on Keywords .................................................................... 2 1.3.2 A Short Analysis on Countries and Affiliations ................................... 3 1.3.3 Co-author Analysis ............................................................................... 4 1.3.4 An Analysis on Sources ........................................................................ 4 1.3.5 Co-citation Analysis ............................................................................. 5 1.3 Discussion and Conclusion ............................................................................... 7 References .................................................................................................................. 7

1.1

INTRODUCTION

The study of big data is constantly expanding, and the main characteristics of big data are now subdivided into the “5V” concept, consisting of Volume, Velocity, Variety, Veracity, and Value (Emrouznejad 2016, Kitchin and McArdle 2016, Onay and Öztürk 2018). As big data have experienced a transition from being an emerging 1

2

Big Data Analytics in Supply Chain Management

topic to a growing research field, it has become essential to classify the different types of research and examine the general trends of this research area. Continuous efforts to create more sophisticated technology to gather data at different steps of the supply chain have led to a new era of supply chain analytics (Ülkü and Engau 2020). Using big data, developers such as Amazon, United Parcel Service (UPS), and Wal-Mart, are gaining unprecedented mastery over their supply chains. They are achieving greater oversight into inventory levels, order fulfillment rates, and material and product delivery using predictive data analytics to adjust supply with demand, leveraging new planning strengths to optimize their sales channel strategies, optimizing supply chain strategy and competitive priorities, and even launching powerful new ventures. The concurrence of events, such as growth in approval of supply chain technologies, data inundation, and a shift in management focus from heuristics to data-driven decision-making, has collectively resulted in the advent of the big data era. In spite of these opportunities, many supply chain operations are restricted or obtain no value from big data. Using these methods, we can overcome widespread difficulties by making the most of big data in the supply chain and increasing cost efficiencies from the already produced data. This process should allow recognition of potential research fields for future research. In this chapter, a short analysis on big data analytics in supply chain management was done. This chapter presents a general analysis of the current developments in the growing field of big data analytics in supply chain management by using scientometrics and charts.

1.2 1.2.1

ANALYSIS DATA COLLECTION

For this scientific analysis, a scientometric mapping technique was used to discover the most common keywords used among published articles. First, we searched for the topics “big data” and “supply chain management” in the SCOPUS database between 2000 and present. More than 700 research articles were found (as of June 14, 2020). Figure 1.1 shows the distribution of papers from 2000 onward. Most of the analysis in this chapter was done with VOSviewer, which is known as a powerful software for scientometric analysis (Van Eck and Waltman 2010, 2011, 2013), and some researchers have used VOSviewer for their analysis (Emrouznejad and Marra 2016, Rahimi et al. 2017, Gandomi et al. 2020).

1.3 SCIENTOMETRIC ANALYSIS 1.3.1

AN ANALYSIS ON KEYWORDS

Figure  1.2 presents a cognitive map on which the node size is comparable with a number of documents in the indicated scientific discipline, for example, the keywords “big data,” “Internet of things,” and “data analytics” possess large nodes. The top 10 keywords and the number of occurrences found in the analysis are shown in Table 1.1.

Big Data Analytics in Supply Chain Management

3

200 180 160 Documents

140 120 100 80 60 40 20 0 11995

2000

2005

2010 Year

2015

2020

2025

FIGURE 1.1 Number of documents on “Big Data Analytics in Supply Chain Management.”

FIGURE 1.2

1.3.2

Cognitive map (keywords analysis considering co-occurrences).

A SHORT ANALYSIS ON COUNTRIES AND AFFILIATIONS

Figure  1.3 shows top organizations that contribute to rankings in the field. Hong Kong Polytechnic University has the first rank (13%), University of Kent possesses the second rank (9%), and California State University and Montpellier Business Schools are in third place (8%). Figure 1.4 presents the countries ranked by the number of published articles. As is shown, the United States possesses the first rank followed by China, India, the United Kingdom, Germany, France, Australia, Hong Kong, Italy, and Malaysia.

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TABLE 1.1 Top 10 Keywords No.

Keywords

1 2 3 4 5 6 7 8 9 10 11

Big data Supply chain management Supply chains Information management Decision-making Supply chain Big data analytics Data analytics Internet of things Sustainable development Competition

Occurrences 394 372 205 145 87 .62 .56 55 52 44 44

Hong Kong Polytechnic University University of Kent 4% 4% 4% 4%

4% 4%

13%

Califorrnia State University, Bakersfield 9%

4%

8%

4% 5% 5%

8% 6%

7%

Kent Business School Montpellier Business School University of Plymouth

8% TBS Bu usiness School University of Massach husetts Dartm mouth

FIGURE 1.3 Top organizations ranking by number of documents.

1.3.3

CO-AUTHOR ANALYSIS

Figure 1.5 depicts the analysis of coauthors that networks use to present the robust and fruitful connections among collaborating researchers. The links through the networks’ present channels of knowledge and networks that highlight the scientific communities engaged in research on the big data analytics in supply chain management are shown.

1.3.4 AN ANALYSIS ON SOURCES Figure 1.6 illustrates the density map of title of sources. There are many sources, including Lecture Notes in Computer Science, Journal of Business Logistics, Computers and Industrial Engineering, International Journal of Production Research, International

Big Data Analytics in Supply Chain Management

H Hong Ko

United States

It Malaaysia, 20 Unitted States, 148

Austrralia, 33

China India

5

France,, 46

United Kingdom Germany

Germany,, 50

France Australia Hong Kong

China, 144

United Kingdom, 78

Italy Malaysia

FIGURE 1.4

India, 78

Top countries ranking by number of documents.

FIGURE 1.5 The scientific community (coauthor) working on “Big data Analytics in Supply Chain Management.”

Journal of Production Economics, Production and Operations Management Journal contributing in the field. The density of the nodes (journal title) is shown by color, and the high density belongs to some reputed sources such as Lecture Notes in Computer Science while IEEE International Conference possesses the low density.

1.3.5

CO-CITATION ANALYSIS

Co-citation analysis is another metric that has been presented in this chapter. The co-citation analysis of cited authors has been demonstrated in Figure  1.7. The

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FIGURE 1.6 Density map (Title).

FIGURE 1.7

Co-citation analysis (Cited authors).

Big Data Analytics in Supply Chain Management

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parameter settings have a minimum of one citation for each author, resulting in 38,602 authors with strength co-citation links. The top‐cited authors in the field are A. Gunasekaran, R. Dubey, S.J. Childe, and T. Papadopoulos.

1.3 DISCUSSION AND CONCLUSION In this chapter, scientometric analysis of “Big Data Analytics in Supply Chain Management” for the time period between 2000 and 2020 was explored. Keywords analysis and citation analysis with VOSviewer software were used, and the most commonly used keywords were identified. Keyword, organization, country coauthor, and co-citation analyses were investigated in this chapter. The analysis of keywords indicates that information management, Internet of Things, sustainable development, and competition are among well-described topics in the field. The United States, China, India, the United Kingdom, Germany, France, Australia, Hong Kong, Italy, and Malaysia are most active countries in the field of big data analytics in supply chain management. A short analysis on the sources indicates that Lecture Notes in Computer Science, Journal of Business Logistics, International Journal of Production Research, and Production and Operations Management Journal are well-known journals that have the most contributions to big data analytics in supply chain management. A comprehensive and systematic review as a future direction is highly recommended. Applicability of evolutionary computations and interdisciplinary works in the case of big data are important matters for practical problems.

REFERENCES Emrouznejad, A. (2016). Big Data Optimization: Recent Developments and Challenges. Springer, Berlin. Emrouznejad, A. and M. Marra (2016). Big data: Who, what and where? Social, cognitive and journals map of big data publications with focus on optimization. In: Emrouznejad, A. (ed.) Big Data Optimization: Recent Developments and Challenges, pp. 1–16. Springer, Cham. Gandomi, A. H., Emrouznejad, A., Rahimi, I. (2020). Evolutionary computation in scheduling: A scientometric analysis. In: Gandomi, A.H., Emrouznejad, A., Jamshidi, M.M., Deb, K., Rahimi, I., (eds.) Evolutionary Computation in Scheduling, pp. 1–10. Wiley, Hoboken, NJ. Kitchin, R. and G. McArdle (2016). What makes big data, big data? Exploring the ontological characteristics of 26 datasets. Big Data & Society, 3(1). doi:10.1177/2053951716631130. Onay, C. and E. Öztürk (2018). A review of credit scoring research in the age of big data. Journal of Financial Regulation and Compliance, 26(3), 382–405. Rahimi, I., Ahmadi, A, Zobaa, A. F., Emrouznejad, A., Abdel Aleem, S. H. E. (2017). Big Data Optimization in Electric Power Systems: A Review, CRC Press, Boca Raton, FL. Ülkü, M. A. and A. Engau (2020). Sustainable supply chain analytics. In: W. Leal Filho Encyclopedia of the UN Sustainable Development Goals. Springer (forthcoming). Van Eck, N. and L. Waltman (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. Van Eck, N. J. and L. Waltman (2011). Text mining and visualization using VOSviewer. arXiv preprint arXiv:1109.2058. Van Eck, N. J. and L. Waltman (2013). VOSviewer manual. Leiden: Univeristeit Leiden 1(1): 1–53.

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Supply Chain Analytics Technology for Big Data Sivagnanam Rajamanickam Mani Sekhar, Swathi Chandrashekar, and Siddesh Gaddadevara Matt Ramaiah Institute of Technology, Bangalore, India

CONTENTS 2.1

Introduction.......................................................................................................9 2.1.1 Introduction to Supply Chain Analytics Technology.......................... 11 2.1.2 Necessity for Supply Chain Analytics for Big Data............................ 11 2.2 Features of Supply Chain Analytics................................................................ 11 2.3 Opportunities and Applications for Supply Chain Analytics ......................... 12 2.3.1 Opportunities for Supply Chain Analytics.......................................... 12 2.3.2 Process Specific Applications of Big Data Analytics.......................... 12 2.4 Tools for Supply Chain Analytics ................................................................... 15 2.5 Supply Chain Analytics Methods ................................................................... 15 2.5.1 Descriptive Analytics.......................................................................... 17 2.5.2 Predictive Analytics............................................................................ 18 2.5.3 Prescriptive Analytics ......................................................................... 19 2.6 Supply Chain Challenges in Adopting Big Data Analytics ............................ 22 2.7 Future of Supply Chain Analytics...................................................................24 2.8 Conclusion.......................................................................................................24 References ................................................................................................................25

2.1 INTRODUCTION Big data more often than not incorporates data and information with sizes over and above the potential of software tools that are regularly used to apprehend, select, organise, and process information enclosed with the bounds of an adequate elapsed clock [1]. Although it emphasises on unstructured data, its philosophy embraces structured and semi-structured data too. Structured data embodies data – often numeric in nature, that is already organised by the institution through data sets and spread sheets. Unstructured data are cluttered and do not belong to any preordained model. It involves data obtained from social media sources, which assist institutions to muster information on customer requirements. Nevertheless, it is not the amount of data that is vital. The main concern, however, is how that data are utilised in the organisations [2]. It is possible to examine the big data for perceptions that make way 9

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Big Data Analytics in Supply Chain Management

FIGURE 2.1

Big data process.

for enhanced judgements and tactical organisations advancements [3]. Figure  2.1 portrays the all-inclusive technique involved in deducing information from big data. Big data process can be further divided into management and analytics. The methodologies involved in acquiring and storing data and information are associated with data management. It is also involved in preparing and retrieving data for further analysis. Data analytics represents the approaches required to perform analysis and gather information intelligence [4]. Henceforward, big data define the large volume of data, necessitates a group of methodologies, procedures, and automation along with innovative structures of integration to expose perceptions obtained from data and information which are known to be varied, intricate, and are of enormous in measure [5]. The definition of big data can be summarised as the three V’s [6] mentioned below: • Volume: It is the quantum of produced and stocked data. The magnitude of information gathered decides the usefulness, significance, and prospect insight. Organisations gather information from a humongous mixture of sources, together with IoT devices, business transactions, equipment from industries, audios, videos, social media, and many more [6]. The pressure on the storage load has been relieved in present scenarios by using platforms like data lakes and Hadoop. • Velocity: Data gush into businesses at a strikingly exceptional momentum due to the broadening of IoT and require to be grasped in an appropriate method. Popular RFID labels, automated meters, detectors and sensors, propel prerequisites to deal with the inundations of such data and information instantaneously. • Variety: Information is known to be in a heterogeneous format. Structured data – obtained from traditional databases in numeric form to unstructured data – word presentations and documents, emails, visuals, sounds [7]. Supply Chain: Big data analytics models to unveil intricate details in the voluminous data and acquire valuable insights from it. And, supply chain is an exceptionally powerful contributor to big data [8]. Supply chain is a complex web of all the people, agencies, assets, activities, and technologies demanded in the development to the purchase of a product, including all the functions that emerge with receiving an order to fulfilling the end user’s request, through developing a product, operations and marketing, distribution networks, business and finance, and end-user or customer service [9]. The entities connected in the supply chain include manufacturers, vendors, repositories or warehouses, travel agencies for transportation, distribution centres, and retail

Supply Chain Analytics Technology

11

agents. Activities of the supply chain comprise converting raw products and apparatuses into a fully furnished and completed product that is produced to the users [10]. The inputs gathered by Supply Chain contain data from important entities like manufacturing and production, development, logistics, and retail [11]. The application of Big Data Analytics on a stream of such abundant data in the supply chain can nurture a dynamic phenomenon of making decisions to predict prospects and perils.

2.1.1

INTRODUCTION TO SUPPLY CHAIN ANALYTICS TECHNOLOGY

It exhibits the potentiality to take information-based judgements, with the grounds of the outline and overview obtained of the vast information, with the help of visual tools such as charts, diagrams, graphs, tables, and more. Supply chains produce enormous quantities of information. It provides assistance by discovering patterns and providing insights. Supply Chain Analytics enhances the process of making decisions for all tasks by making use of the data and quantitative and analytical methods [12]. Supply Chain Analytics lays down the foundation for businesses to accomplish the desired challenging growth, improve their profits, and increase their market shares by exploiting the derived insights from the gathered data.

2.1.2

NECESSITY FOR SUPPLY CHAIN ANALYTICS FOR BIG DATA

Businesses, Organisations, Companies dealing with enormous amounts of data require Supply Chain Analytics to assist them in making faster, smarter, and more efficient decisions [13]. With Supply Chain Analytics, organisations can improve their forecasting, identify their drawbacks and inefficiencies, react better to user’s requirements and needs, drive innovation, and follow innovative ideas. Supply Chain Analytics is required to: • execute the precise solutions to specifically analyse, predict. and interpret data big • identify the risk indicators • responding in a punctual manner to the insights obtained It is also known to be the fundamental foundation for involving cognitive technologies like artificial intelligence (AI). Cognitive technologies like humans perform understanding, reasoning, learning, and interacting but at aggressive speed and capacity. Such advancement with the supply chain has turned over a new leaf for the optimisation of big data [14].

2.2

FEATURES OF SUPPLY CHAIN ANALYTICS

With advances in supply chain analytics, several organisations can safeguard their business reputation and ensure long-term sustainability [15]. • Collaborative: A cloud-based network is used to collaborate with all the entities of the supply chain allowing collaboration and involvement of several organisations.

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• Connected: Ability to make use of all sorts of information and data – structured and organised, unstructured, and traditional data. • Cyber aware: Connectivity and collaborative features make it highly essential for the supply chain to be particularly aware of intrusions from cyber and cyber hacks. • Cognitively enabled: Supply chain is automated and self-learning. The AI platform renders itself to be the foreground for the future by gathering and collecting, coordinating, and organising, and managing actions and decisions all through the chain [16]. • Comprehensive: Insights are required to be spontaneous and comprehensive. Delay and latency are undesirable in the future of the supply chain.

2.3

OPPORTUNITIES AND APPLICATIONS FOR SUPPLY CHAIN ANALYTICS

This section illustrates the different opportunities and applications for supply chain analytics in big data.

2.3.1

OPPORTUNITIES FOR SUPPLY CHAIN ANALYTICS [17]

With analytics, Supply Chain performs with high targets to enhance in several aspects, including user requirement prediction, Supply Chain assessment, the overall efficiency of Supply Chain, time taken to react, risk assessment. • Enhancing predictions of user requirement: Understanding customer and user requirements plays a very vital role. Meeting the user’s requirements with the precise product and to the precise user at the precise time and place is key to earning and perpetuating customer satisfaction and loyalty. • Enhancing efficiency: Incorporating analytics to approximately calculate and make decisions that are cost-efficient enhances the efficiency all-round the supply chain [18]. Reduction of cost and spend analytics has persistently been the top priority in Supply Chain. • Refining Assessment of Risk in the Supply Chain: Better prediction and assessment of risk and its possible impact by evaluating an enormous amount of historical data and risk mapping techniques using predictive analysis to reduce the impact are highly essential. • Upgrading traceability: For improved tracking of products from production to retail, enhancing traceability is essential. Upgraded tracking abilities provide much better control over several supply chain processes. The primary purpose is to guarantee a better flow of products.

2.3.2

PROCESS SPECIFIC APPLICATIONS OF BIG DATA ANALYTICS

The steps involved in the flow of the supply chain for a process-specific application are illustrated in Figure 2.2 and Table 2.1. Supply Chain is considered to be a web of

Supply Chain Analytics Technology

FIGURE 2.2

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Process of supply chain for process specific application.

TABLE 2.1 Summary of Process Specific Applications Sl No.

Process

Application

1

Planification

• Risk evaluation and resilience planning • Minimise the risk of investments on groundwork and external agreements and contracts • Constant supervising of performance

2

Supplying

• Reduce storage capacity and distribution

3

Production

• Market intelligence for all-sized enterprises Information on parameters of production, such as the forces that are required in assembly operations or differences of dimensions between parts, can be recorded and analysed • support the analysis of root-cause of future defects

4

Distribution

• Optimal routing • Real-time optimisation of routes • environmental domain intelligence and awareness • verification of addresses • pick-up and delivery spots • Improve Supply Chain Traceability • Delivery Routes Real-time optimisation

numerous trades and associations [19]. It unfolds the diverse tasks implicated with fabricating a product and services beginning from the idea stage till distribution to the users. i. Plan • Major data-driven operation in the supply chain is planned.

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• There is a significant possibility to reformulate the procedure of planning, with the use of fresh data from sources including the internal origin and extrinsic origin to derive real-time requirement and delivery model. Several organisations make use of the latest information sources to upgrade planning and their demand understanding abilities [20]. • With clarity and visibility on all sorts of data, the magnitude for production is possible to be evaluated instantaneously with the intention to recognise inconsistency between requirement and delivery [21]. This makes it possible to initiate activities, such as changes in rates, promotion timings or to make necessary realignments. • For example, several online enterprises utilise analytics, data, and forecasting to modify recommendations of products to users. There is an increase in requirement with such compellingness towards products that are available in stock. ii. Source • Based on the transactional ground, several processes associated with the supply chain can be recognised and identified in real-time to mark any variations from the routine fashion of delivery [22]. • Data Analytics is also known to drive strategic decisions. Organisations are also discovering risk management possibilities through predictive analytics by means of mapping their supply chains and using worldwide information and data from social networks about fire mishaps, strikes, or bankruptcies, the organisation can supervise supply disruptions and take necessary decisions before their competitors. iii. Manufacturing • Manufacturing can be improved by Big Data Analytics manufacturing. • Information on parameters of manufacturing, such as the forces that are required in assembly operations or differences of dimensions between parts, can be recorded and analysed to hold up the analysis of reasons of blemishes, although it has the slightest possibility of its occurrence in the future. iv. Warehousing • Logistics has been very economic. Organisations have always invested in techniques and technologies that create a competitive advantage. • There are many improvements and advances seen in warehousing. • New innovative technologies, data and information sources, predictive and modern analytical methods are constituting novel opportunities in warehouses [23]. • Examples – Chaotic Storage mechanism that capacitates the effectual warehouse space utilisation and reduces the distance for travel for personnel. – Warehouses of High-rack bay have the ability to automatically rearrange pallets overnight to rearrange schedules for the next day optimally.

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v. Transportation • Several Organisations are already making use of analytics to enhance their operations. For example, truck companies are making use of analytics on their fuel consumption to intensify their driving efficiency and GPS techniques to minimise waiting periods by taking into consideration assigning warehouse inlets on time. • Several Delivery Organisations are making use of real-time routing for deliveries to end-users in dependence on the traffic information and truck’s location on the road [24]. vi. Point of Sale • Data and information-driven optimisation can always provide a competitive advantage. • Advanced analytics can assist organisations in making judgements regarding the products to be placed in positions that are in high value, like the end of the aisle, and duration of their retention in that location. It can also enable them with the opportunity to explore the benefits of sales attained by congregating interconnected products together. • Retailers are monitoring a challenging activity on sales [25] for the detection and prevention of out-of-stock by using indicators to indicate when the products are out-of-stock. If a product that is purchased every few minutes does not appear at the tills, a notification is triggered to have someone check if the product on the shelf is out-of-stock. More such creative and innovative technologies are being processed; one such includes the fitting of sensors that are light-weighted on shelves as well as using in-store cameras in order to monitor and detect levels of stock on the shelves.

2.4

TOOLS FOR SUPPLY CHAIN ANALYTICS

Big data tools that are accessible for the supply chain are used to explore and integrate data, perform mathematical analysis, use accurate visual techniques for presentation, and comprehend the data storage system. Table 2.2 lists down some of such tools. In order to develop a system for making decisions, the summarisation of all the tools is a vital task in Supply Chain Analytics [26].

2.5

SUPPLY CHAIN ANALYTICS METHODS

The Methodology can be categorised into descriptive, predictive, and perspective analytics [28,29]. Interpretation of the past data for better perception and comprehension of the changes occurring in any organisation are descriptive analytics. It is a scope of statistics that determines on collecting and condensing of information for better interpretation [30]. Predictive Analytics, at its elemental ground, pursues to identify and discover patterns and represents the interconnection between data. To make predictions, numerous different statistical techniques such as machine learning, data mining, predictive modelling are incorporated to analyse the present and past data [31]. Prescriptive Analytics goes well past predictive analytics by describing the activity

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Big Data Analytics in Supply Chain Management

TABLE 2.2 Tools for Supply Chain Analytics Tool

Explanation

R programming

An open-source, free tool used for statistical analysis. Built on command-line interfaces. For processing and performing analysis, and developing statistical software, this tool is used by data miners and statisticians. It is a tool for optimisation. It is used to optimise linear, non-linear, integer programming problems. The tool can be used as an assistance for documentation. It is known for modelling expressions easily. This tool is associated with both structured and unstructured data. It performs analysis and generates reports for the two types of data. It provides accurate visualisation techniques, easy access to information. Also performs exploring and integration of data. A database program that is used for documentation. It is categorised as a NoSQL database program. It provides assistance in warehouse and storage, numbering and indexing, aggregating and collecting data. This tool controls and handles the maintenance and support for customers on the foundation of past sales, reports, and issues faced. The DSM tool is one of the best tools in the category of drop shipping arbitrage. This tool is associated with extracting data from a diverse group of data and information sources, apprehends, and condenses the information gathered for precise presentation in a standard format with the intention of easy processing. It centralises on tasks scheduling and monitoring. And, it performs re-execution of the tasks in the situation of a failure. The map-reduce methodology is used to operate on the humongous quantity of data. The technique is divided into two activities: mapping and reducing. Input data are split into several individualistic blocks that are processed by the mapping technique. And, the entire input data are divided and sorted to reduce the amount of work [27].

LINGO

Pentaho

MongoDB

Drop Shipping Management Tool (DSM) Informatica

Map Reduce

FIGURE 2.3 Making decisions and taking actions with the help of descriptive, predictive, and perspective analytics.

plan required to reach the anticipated results and understand the interconnected consequence of every decision. The relationship between the three models of analytics is illustrated in Figure 2.3 [32]. Big data analysed using the descriptive and predictive model of analysis is further proceeded with the perspective model of analysis, and the acquired and gathered information is used to make decisions.

Supply Chain Analytics Technology

2.5.1

DESCRIPTIVE ANALYTICS

• The primary stage of analytics in any decision-making scenario is descriptive analytics [33]. It provides solutions and explanations to the question of “What has happened” and apprehends and recapitulates the source data to a human-understandable format. • It engages with particulars of what took place in the past, what is happening currently, and why, illustrated in Figure 2.4. Also, it provides a clear and universal source of details throughout the supply chain. Descriptive Analytics identifies the possibilities and opportunities in the Supply Chain. • They are beneficial to exemplify the overall stock, the average amount consumed per user, and changes in sales per year. Some examples are intimation that furnishes information regarding past actions about the organisation’s manufacturing and presentation, economics and finances, operation and sales, and end-users. • Techniques and approaches such as Regression, Modelling and Visualisation, and OLAP (online analytical processing) are used in descriptive analytics • Descriptive Analytics functions can be divided into five categories: • Determining organisation goals and metrics: Performance is evaluated with respect to the goals and that makes determining the critical metrics very important. Enhancing the returns, lowering the expenses, increasing efficiency are some of the common goals. • Identification of the required data: There exist several sources from which the data can be extracted, such as desktops, warehouses, manual records. Understanding the requirements ensures proper planning to acquire the data and accurate extraction of the data [34]. • Data Extraction and Preparation: This step although consumes the maximum quantity of time duration is essential to ensure high precision. Removal of duplicate entries, transforming data to a standard format, and cleansing are a few steps involved in data preparation. • Data Analysis and Processing: With the intention of identifying patterns and calculating performances, analysis models are created. Performance is evaluated for the initially mentioned goal by comparing it with past results [35]. R Programming and Python, open-source tools, are used to perform analysis.

FIGURE 2.4 Descriptive analytics.

17

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Big Data Analytics in Supply Chain Management

• Data Presentation: Visual methods such as charts and graphs are commonly used to present the results obtained from the data processing and analysis. Data visualisation plays a vital role in this scenario. Several visualisation tools such as Business Intelligence provide the ability to present the data in a visual format.

2.5.2

PREDICTIVE ANALYTICS

• Predictive Analytics as shown in Figure 2.5 engages with the possibilities of what could happen. It strives to make accurate predictions about the future and explore the reasons. It assists an enterprise in understanding the probable results in the future and its implications on the business [36]. • It can be applied during the business, from speculating the user behaviour and understanding designs to recognise inclinations in marketing and sales [37]. • Quantitative and qualitative methodologies are both used to process and analyse the past and present data to make predictions. Its goal is to project what is the possibility of events occurring in the future and the reason behind its occurrence [38]. • Predictive Analytics is associated with some techniques [39] and algorithms such as: 1. some methods used for estimating the sales in the supply chain are Advanced Forecasting and Time-series method 2. K-NN (Nearest Neighbour), Naive Bayes (NB), Discriminant Analysis are known Statistical algorithms being used for prediction 3. for the hierarchical consecutive structure, Random Forests and Decision trees are used 4. Identical or similar items in the humongous gathered data are grouped together accordingly using Clustering algorithms and Pattern-mining algorithms • Understanding predictive analysis by developing a predictive model using regression analysis [40]. • Characterising the functioning of a random variable using a cluster of data mining or modelling approach with one or more numeric variables is known as regression. • The straight-line technique, linear regression, is used to determine the relationship between the predictor and response variable.

FIGURE 2.5

Predictive analytics.

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• Straight-line represented by Equation 2.1 y = mx + c

(2.1)

– Regression is used to interpret the value of the variable y through finding the suitable values for the parameters m and c, using the value of x as the foundation that is known. – Here, the dependent variable is y and the other variables become independent variables or predictor variables. • This methodology is preferred to learn the values of parameters associated with a function that has the capability of leading the function to its best-fit Y = F ( x ,) + e

(2.2)

– In Equation 2.2, the value of Y, which is a continuous target, is to be estimated or predicted using regression function F. – (θ1, θ2…θn) is a set of parameters and the error is denoted by e • The result obtained or the output of the analysis is represented by R, known as the coefficient of determination. The value of R varies from 0 to 1. – When R = 0, the independent parameter is not useful in predicting the dependent parameter. – When 0 ≥ R ≤ 1, it indicates that the range to which the depending parameter can be determined or predictable. – Example: When R = 0.60, it specifies that 60% of the variance in the parameter y can be predictable from the parameter x.

2.5.3

PRESCRIPTIVE ANALYTICS

• It is, as seen in Figure 2.6, concerned with what should occur in the future and what steps to be taken at the present to influence its occurrence – owning various judgements on the grounds of descriptive and predictive analytics, simulation, or mathematical optimisation, mainly building the knack of making decisions with several perspectives in mind [41]. • What and when aspects are being dealt by descriptive and predictive analytics, while Prescriptive Analytics contemplates on the reason behind its occurrence (“why it occurred”) [42]. • Data and information are collected continuously to trace back the events that provide the decision-makers with the opportunity to increase their prediction accuracy to make better choices. • This model is associated with optimisation and simulation. Its main aim is to enhance business performance by unravelling the reason for the occurrence of certain events.

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Big Data Analytics in Supply Chain Management

FIGURE 2.6 Perspective analytics.

• Although this analytic technology is comparatively complicated to apply [43]. But when applied appropriately, they have the strongest influence on how organisations make judgement calls and decisions. • The perspective analytic method uses the below mentioned two classes of algorithms • Decision trees – A tree-like graph structure or a model of decisions and their likely consequences that include the possibility of event outcomes, resource expense, and utility [44]. It is a technique to display an algorithm that encompasses only conditional control statements. – In the decision tree structure, each inner node marks a “test” on an attribute (such as, if a flipped coin lands on its head or tails), all the branches present represent the result of the trial or test, and leaf node represents the decisions taken after analysing. The route from the root to leaf represents classification rules. – For example: Critical Path Method (CPM) is used for project modelling, an algorithm for scheduling numerous activities and tasks related to projects. Another available method is known to be the Program Evaluation and Review Technique (PERT). Table  2.3 highlights the contrasting features of CPM and PERT. And having an understanding of contrasting characteristics of the two types of models, decision trees can be used to decide on which model can be used for implementation as shown in Figure 2.7. • Fuzzy Rule-Based System – The logic where more than true or false values are incorporated is known as fuzzy logic. This system comes into play when the situation cannot yield a direct true or false solution [45]. A continual range of truth values in the interval of [0, 1] is used rather than just the direct true or false values. – Fuzzy Rule-Based Systems are systems that embody an extension of traditional rule-based systems. Using fuzzy statements as the principal components of the rules permits gathering, apprehending, and managing the potential uncertainty of the represented knowledge. And, its structure is illustrated in Figure 2.8. – Inputs: A crisp numerical value. The inputs within the input subsets are combined wither with logical “AND” or logical “OR.”

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TABLE 2.3 Difference in the Characteristic Between CPM and PERT CPM

PERT

Deterministic Uses historical or past data to make estimations Focuses on Expense and Time Can be extended to small projects

Probabilistic Uses Probabilistic approach; hence there exists an opportunity for an activity to fail at any point. Therefore, the estimates are uncertain. Focuses on planning Suitable for R&D projects

FIGURE 2.7

Decision tree for selecting a model.

– Rules base: “IF….THEN…” statements are incorporated in the fuzzy rules [46]. Each rule is broken into two parts. The first part begins with an “IF” and terminates before the “THEN” is known as a predicate of combined inputs. The following consequent part that comes after ‘‘THEN” includes the subset of the output. – Applying the Implication Method: This phase is known to shape the consequence part.

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Big Data Analytics in Supply Chain Management

FIGURE 2.8 Structure of fuzzy rule-based system.

– Aggregating the outputs: It is necessary to combine all the rules, as the decisions are taken established on evaluation of all the conditional rules. In this step, output or result of each individual rule is integrated into one individual fuzzy set. The output obtained from the implication phase is used as the input to the aggregation process. – Defuzzification: This is the final phase where the obtained result in the fuzzy format is required to be converted to a crisp output using defuzzification techniques such as bisector, middle of maximum, that can be directly used. – Benefits of using Fuzzy Rule-Based Systems [47]: – Capable of providing an accurate solution to reason and rationalise with variability and uncertainty. – It is constructed from the experience of experts. – Liberal and tolerant towards ambiguous information and does not require historical data – Fuzzy rules can be developed easily using data from a survey without the need for in-depth pre-processing

2.6 SUPPLY CHAIN CHALLENGES IN ADOPTING BIG DATA ANALYTICS Analytics techniques offer pronounced advantages in the transformation of the economy. But it also raises several challenges with capturing the data, storage of that vast information, and analysis and visualisation of the data. It also incorporates issues with data inconsistency and information incompleteness, timeliness and security, and safety of the data information [48]. Challenges faced can be categorised into two categories such as Organisational Challenges and Technical Challenges [49]:

Supply Chain Analytics Technology

i. Organisational challenges: • Time delay: Big data are featured for its humongous volume of data. The complex intricacy of Supply Chain with the goals for the interpretation of the data sets in a company with the external components like the timely absence of access to the data that constitute in making the process of analytics more time-consuming. • Insufficiency of resources: The accessibility of real-time information is very pivotal for the betterment of results. Supply Chain is a platform that produces complex multi-functional data for inter-related entities, requires collection, and storage of multi-functional information to be well-organised and streamlined. • Privacy and security concerns: Sharing of Data through Supply Chain Network is a major component in data and information collection from numerous different sources, analysing it and providing insights. But due to several laws related to Security and Privacy constraints towards the data sharing process, the accuracy of the insights gathered through Big Data Analytics is affected. • Behavioural issues: If the decision-makers act on all the unsubstantial changes, the supply chain and inventory cost can be at an increased risk. As big data are associated with a variety and huge volume, there exists an elevated risk of recognising insignificant associations. • Issues on return on investment (ROI): Big data as we are aware are associated with a huge variety and volume of data and information. This aspect of big data makes it very enigmatic to evaluate the value of the data that are collected. Executing analytics on big data necessitates a considerable quantity of expenditure for constructing the infrastructure that is required. There might exist an elevated risk on the ROI because of scepticism on the value of the data. • Lack of skills: The data produced by Supply Chain Sources are very complex. And, it recommends an amalgamation of good analytical skills on domain knowledge and the potential to comprehend the usability of the information. Finding such conjunction along with experience is strenuous. ii. Technical challenges [50]: • Scalability of data: In the course of making use of analytic technology in any organisation, scalability of information and data is observed as a prime technical problem. The inefficacy of several systems to transit from conventional limited databases to distributed or cloud-based databases negatively influence the insights received from Big Data Analytics as the quality and quantity of relative data obtained are jeopardised. • Quality of data: The implementation and outcome of the analytics techniques determine the data and information quality that is stored and utilised. Data are featured as intangible, complex, and multi-dimensional on the grounds of its origin and implementation. The data quality is required to be consistent to obtain harmonious and dependable results for the intention of decision making. The quality of data and information gathered is determined by the diversity and origin of data.

23

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Big Data Analytics in Supply Chain Management

• Deficit of techniques: impotence and inability of systems and organisations to exploit the information gathered badly control the robustness of the insights developed after evaluating the data sets. The mechanisms and methodology that are used to explore, evaluate, assess, predict, and conceptualise data and information demand to be modified and enhanced with respect to the magnitude and intricacy of data.

2.7 FUTURE OF SUPPLY CHAIN ANALYTICS The emerging need to manage a voluminous amount of data and information and utilise the insights derived from it is establishing the pressure of the need for supply chain analytics. The heightening popularity among several organisations of the advantages of big data in the supply chain analytics is playing a significant role in escalating the demand for analytic solutions to enhance the perceptibility all over the supply chain enablers. Big data in Supply chain analytics is capable of assisting a firm, organisation, or company in taking rapid, intelligent, cost-effective, and more efficient decisions. The benefits include the following: • Better apprehension of risks: By recognising the known risks, the supply chain analytics has the ability to predict the upcoming future risks by observing and perceiving trends and patterns all around the supply chain. • Enhance precision in planning: Big data guides and assists an organisation in predicting the future demand better. It evaluates and analyses the customer data to ensure precise prediction. It provides several insights that help many organisations determine which products must be pruned when they are less beneficial and which products are in demand. • All-around supply chain: Supply Chain technologies are used by organisations to gather customer requirements, perform warehouse tracking and supervising, and collect responses of users and customers to make knowledgeable judgements and decisions. • Achieve a profitable ROI: With a better understanding of the data in the organisation throughout the supply chain, organisations can make smarter investments and receive higher returns [51]. Artificial intelligence is seen as the next step in supply chain analytics. It has been built with the ability to process the enormous volume of data and information (structured as well as unstructured) and afford insights in real-time [52]. Along with data retention and process automation, systems with AI are built with the potentiality to think, cogitate, reason, rationalise, learn, and improve like humans. Advances of blockchain technologies along with AI [53] integration provide organisations with the ability to actively analyse and make predictions.

2.8 CONCLUSION Several key factors such as shrinking of product life cycles, reduced supply chain visibility, unproductive supplier networks, increased warehousing expenses, a surplus of

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unrequired forecasts, and oscillating customer demands necessitate the optimisation of the supply chain. In conclusion, Supply chain analytics technology for big data assists several enterprises and organisations in attaining growth [54], improving profitability and gain, and increasing market shares by making use of the derived insights for taking strategic decisions. With the help of the case studies mentioned above in the paper, we can conclude that solutions offered by supply chain analytics provide a holistic approach to supply chain and enhances sustainability, decreases the inventory cost, and accelerates the time taken to market the products. Enhanced outcomes and improved cost-effectiveness obtained as a consequence of adopting supply chain analytics encourages the application of solutions in various applications, such as retail and consumer goods, healthcare, and production.

REFERENCES 1. Snijders, C., Matzat, U., Reips, U.-D. (2012). ‘Big Data’: Big gaps of knowledge in the field of Internet. International Journal of Internet Science, 7: 1–5. 2. Benabdellah, A.C., Benghabrit, A., & Bouhaddou, I. (2016) Big Data for Supply Chain Management: Opportunities and Challenges. In 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA) (pp. 1–6). IEEE : Agadir, Morocco. 3. Christopher, M. (2016). Logistics & supply chain management. Pearson UK. 4. Gandomi, A., Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2): 137–144. doi:10.1016/j. ijinfomgt.2014.10.007. 5. Targio Hashem, I.A., Yaqoob, I., Badrul Anuar, N., Mokhtar, S., Gani, A., Ullah Khan, S. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47: 98–115. doi:10.1016/j.is.2014.07.006. 6. Addo-Tenkorang, R., Helo, P.T. (2016). Big data applications in operations/supply-chain management: A literature review. Computers & Industrial Engineering, 101: 528–543. 7. Kanaujia, P.K.M., Pandey, M., Rautaray, S.S. (2017). A framework for development of recommender system for financial data analysis. International Journal of Information Engineering and Electronic Business (IJIEEB), 9(5): 18–27. doi:10.5815/ijieeb.2017.05.03. 8. Feki, M., Boughzala, I., Wamba, S.F. (2016). Big data analytics-enabled supply chain transformation: A literature review. In 2016 49th Hawaii International Conference on System Sciences (HICSS). IEEE: Koloa, USA. 9. Leveling, J., Edelbrock, M., Otto, B. (2014, December). Big data analytics for supply chain management. In 2014 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), (pp.  918–922). IEEE: Bandar Sunway, Malaysia 10. Kozlenkova, I., et al. (2015). The role of marketing channels in supply chain management. Journal of Retailing, 91(4): 586–609. doi:10.1016/j.jretai.2015.03.003. Retrieved 28 September 2016. 11. Zhong, R.Y., Newman, S.T., Huang, G.Q., Lan, S. (2016). Big data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives. Computers & Industrial Engineering, 101: 572–591. 12. Damiani, E., Frati, F., Tchokpon, R. (2011). The role of information sharing in supply chain management: The securescm approach. International Journal of Innovation and Technology Management, 8(3): 455–467.

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13. Fan, Y., Heilig, L, Voß, S. (2015). Supply chain risk management in the era of big data. In: International Conference of Design, User Experience, and Usability. Cham: Springer; pp. 283–294. 14. https://www.ibm.com/supply-chain/supply-chain-analytics. 15. Wu, L., Yue, X., Jin, A., Yen, D.C. (2016). Smart supply chain management: A review and implications for future research. The International Journal of Logistics Management, 27(2): 395–417. doi:10.1108/ijlm-02-2014-0035. 16. Prajogo, D., Olhager, J. (2012). Supply chain integration and performance: The effects of longterm relationships, information technology and sharing, and logistics integration. International Journal of Production Economics, 135: 514–522. 17. Kache, F., Seuring, S. (2017). Challenges and opportunities of digital information at the intersection of big data analytics and supply chain management. International Journal of Operations & Production Management, 37(1): 10–36. doi:10.1108/ijopm-02-2015-0078. 18. Fabble-Costes, N., Jahre, M. (2008). Supply chain integration and performance: A review of the evidence. The International Journal of Logistics Management, 19(2): 130–154. 19. Lambert, D.M., Cooper, M.C. (2000). Issues in supply chain management. Industrial Marketing Management, 29(1): 65–83. 20. Wang, L., Alexander, C.A. (2015). Big data driven supply chain management and business administration. American Journal of Economics and Business Administration, 7(2): 60–67. doi:10.3844/ajebasp.2015.60.67. 21. Amiri, A. (2006). Designing a distribution network in a supply chain system: Formulation and efficient solution procedure. European Journal of Operational Research, 171(2): 567–576. 22. Lee, C.K.M., Lau, H.C.W., Ho, G.T.S., Ho, W. (2009) Design and development of agent-based procurement system to enhance business intelligence. Expert Systems with Applications, 36(1): 877–884 23. Abai, N.H.Z., Yahaya, J.H., Deraman, A. (2013). User requirement analysis in data warehouse design: A review. Procedia Technology, 11: 801–806. 24. Bhattacharya, S., Mukhopadhyay, D., Giri, S. (2014). Supply chain management in Indian automotive industry: Complexities, challenges and way ahead. International Journal of Managing Value Supply Chains, 5: 49–62. doi:10.5121/ijmvsc.2014.5206. 25. Waller, M.A., Fawcett, S.E. (2013). Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2): 77–78. 26. Anitha, P., Patil, M.M. (2018). A review on data analytics for supply chain management: A case study. International Journal of Information Engineering and Electronic Business(IJIEEB), 10(5): 30–39. doi:10.5815/ijieeb.2018.05.05. 27. Dean, J. (2006). Experiences with MapReduce, an abstraction for large-scale computation. In: Proceedings of the 15th International Conference on Parallel Architectures and Compilation Techniques, PACT’06. Seattle, Washington, USA. 28. Tiwari, S., Wee, H.M., Daryanto, Y. (2018). Big data analytics in supply chain management between 2010 and 2016: Insights to industries. Computers  & Industrial Engineering, 115: 319–330. doi:10.1016/j.cie.2017.11.017. 29. Wang, G., Gunasekaran, A., Ngai, E.W., Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176: 98–110. 30. Pyne, S., Rao, B.L.S.P., Rao, S.B. (eds.) (2016). Big Data Analytics. doi:10.1007/ 978-81-322-3628-3. 31. Lamba, K., Singh, S.P. (2017). Big data in operations and supply chain management: Current trends and future perspectives. Production Planning and Control, 28(11–12): 877–890.

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32. Darvazeh, S.S., Vanani, I.R., Musolu, F.M. (2020). Big Data Analytics and Its Applications in Supply Chain Management, New Trends in the Use of Artificial Intelligence for the Industry 4.0, Luis Romeral Martínez, Roque A. Osornio Rios and Miguel Delgado Prieto, IntechOpen. doi:10.5772/intechopen.89426. 33. Trkman, P., McCormack, K., De Oliveira, M.P., Ladeira, M.B. (2010) The impact of business analytics on supply chain performance. Decision Support Systems, 49(3): 318–327. 34. Demirkan, H., Delen, D.: Leveraging the capabilities of service-oriented decision support systems: putting analytics and big data in cloud. Decision Support Systems, 55(1): 412–421. 35. Souza, G.C. (2014). Supply chain analytics. Business Horizons, 57(5): 595–605. 36. Schlegel, G.L. (2014). Utilizing big data and predictive analytics to manage supply chain risk. The Journal of Business Forecasting, 33(4): 11. 37. Sarprasatham, M. (2016). Big data in social media environment: A business perspective. doi:10.4018/978-1-5225-0846-5.ch004. 38. Barbosa, M.W., Vicente, A.D., Ladeira, M.B., Oliveira, M.P. (2018) Managing supply chain resources with big data analytics: A systematic review. International Journal of Logistics Research and Applications, 21(3):177–200. 39. Waller, M.A., Fawcett, S.E. (2013). Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2): 77–84. 40. Srimani, P.K., Patil, M.M. (2014). Regression model for edu-data in technical education system: A linear approach. In: Satapathy, S., Avadhani, P., Udgata, S., Lakshminarayana, S. (eds.) ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India- Vol II. Advances in Intelligent Systems and Computing, vol. 249. Cham: Springer. 41. Lepenioti, K., Bousdekis, A., Apostolou, D., Mentzas, G. (2020). Prescriptive analytics: Literature review and research challenges. International Journal of Information Management, 50, 57–70. doi:10.1016/j.ijinfomgt.2019.04.003. 42. Rozados, I.V., and Tjahjono, B. (2014). Big data analytics in supply chain management: Trends and related research. In: 6th International Conference on Operafions and Supply Chain Management. Bali, Indonesia. 43. den Hertog, D., Postek, K. (2016). Bridging the gap between predictive and prescriptive analytics-new optimization methodology needed. Technical report, Tilburg University, Netherlands. 44. Gröger, C., Schwarz, H., Mitschang, B. (2014). Prescriptive analytics for recommendationbased business process optimization. In International Conference on Business Information Systems (pp. 25–37). Cham: Springer. 45. Magdalena, L. (2015). Fuzzy rule-based systems. In: Springer Handbook of Computational Intelligence, pp. 203–218. doi:10.1007/978-3-662–43505-2_13. 46. Zadeh, L.A. (1975). The concept of a linguistic variable and its applications to approximate reasoning – Part I. Information Sciences, 8(3): 199–249. 47. Agami, N., Saleh, M., El-Shishiny, H. (2010) A fuzzy logic based tend impact analysis method. Technological Forecasting and Social Change Journal, 77(7), 1051–1060. 48. Rahman, M., Tahiduzzaman, Md., Rahman, Md.S. (2017). Big data and its impact on digitized supply chain management. 3. ISSN: 2455-6661 49. Awwad, M., Kulkarni, P., Bapna, R., Marathe, A. (2018). Big data analytics in supply chain: A literature review. 50. Bhattacharya, S., Mukhopadhyay, D., and Giri, S. (2014). Supply chain management in Indian automotive industry: Complexities, challenges and way ahead. International Journal of Managing Value Supply Chains, 5: 49–62. doi:10.5121/ijmvsc.2014.5206. 51. “Why supply chain analytics is a must have,” Christy Pettey, Gartner, 14 May 2015.

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52. “Creating a thinking supply chain for the cognitive era,” Matt McGovern, Watson Customer Engagement, 27 Mar 2017. 53. Jordan, M.I., Mitchell, T.M. (2015) Machine learning: Trends, perspectives and prospects. Science, 349(6245), 255–260. 54. Pusala, M.K., Amini Salehi, M., Katukuri, J.R., Xie, Y., Raghavan, V. (2016). Massive data analysis: Tasks, tools, applications, and challenges. Big Data Analytics, 11–40. doi: 10.1007/978-81-322-3628-3_2.

3

Prioritizing the Barriers and Challenges of Big Data Analytics in Logistics and Supply Chain Management Using MCDM Method Mehdi Keshavarz-Ghorabaee Gonbad Kavous University

Maghsoud Amiri Allameh Tabataba’i University

Mohammad Hashemi-Tabatabaei Allameh Tabataba’i University

Mohammad Ghahremanloo Shahrood University of Technology

CONTENTS 3.1 3.2 3.3

Introduction to Big Data Analytics................................................................. 30 Barriers to BDA: Background ........................................................................ 31 Methodology ................................................................................................... 33 3.3.1 The Steps of HBWM .......................................................................... 35 3.3.2 Determining the Consistency Rate ..................................................... 38 3.4 Results and Discussion ................................................................................... 39 3.5 Conclusion ...................................................................................................... 41 References ................................................................................................................ 42

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3.1 INTRODUCTION TO BIG DATA ANALYTICS Big Data Analytics (BDA) is a term that refers to the processes of investigating large amounts of data with diverse variables to uncover hidden patterns, unknown correlations, and other useful information or knowledge (Zhong et al. 2016). BDA has many advantages over traditional database management. Lack of information in various manufacturing and service areas has been one of the major challenges for various organizations over the years. By using BDA, organizations can improve their performance and ultimately gain competitive advantages. In the management of the supply chain of large organizations and companies, the flow of information is also large. Information of transactions and activities that are performed throughout the supply chain is invaluable for business owners. This information can help them in future decisions on issues such as demand forecasting, production planning, and so on. The main purpose of BDA is to help companies make better business decisions, enabling users to analyze large volumes of data from various sources such as databases, the Internet, mobile and location records, as well as information recorded by sensors. To analyze such enormous data which have different formats, there is a wide range of technologies for analyzing Big Data (BD), enormous data that form a set of open-source software frameworks capable of supporting analyzing large amounts of data sets in cluster systems (Zakir, Seymour, and Berg 2015). Today, the effective and efficient use of BDA is a key factor for the success of manufacturing companies in the global market (Y. Wang and Hajli 2017). The widespread use of digital technologies has led to the emergence of Big Data Business Analytics (BDBA) as an important business capability to give companies a better means to derive value from the increasing volume of data and achieve a powerful competitive advantage. BA is the study of the skills, technologies, and practices used to assess strategies and operations at the organization level to gain insight into and guide the career planning of an organization. These evaluations include evaluation of strategic management, product development, customer service delivery through evidence-based data, statistical analysis and operations, predictive modeling, forecasting, and optimization techniques (H. Chen, Chiang, and Storey 2012). The BDBA comprises two dimensions: BD and Business Analysis (BA). BA refers to the ability to process data with the following characteristics (3V): high velocity, high variety, and high volume. It has more processing capabilities than traditional data management approaches (C. L. P. Chen and Zhang 2014). In recent years, BDBA has become a fast-growing and effective way for business organizations to keep their competitive advantages in a dynamic business environment. One of the important applications of BDBA is its use in strategic management: formulating the strategies and aligning the organizational strategies and supply chain. Organizational strategy is very important because it presents the overall direction of the organization and guides the operations and strategies of an organization’s supply chain. Therefore, supply chain operations and strategies must be consistent with organizational strategies. BDBA can complement strategic management by adding advanced forecasting insights to strategy implementation processes. BDBA can complement strategic management by introducing advanced forecasting insights into strategy implementation processes. On the other hand, the amount

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of data generated and communications over the Internet is increasing dramatically; thus, some challenges arise for organizations that wish to use this vast volume of data. Organizations wish to use this data because BD can provide unique insights about market trends, customer purchase patterns, maintenance cycles, cost-cutting approaches, as well as more targeted business decisions (G. Wang et al. 2016). The purpose of BDBA is to gain insights from data using statistics, mathematics, econometrics, simulation, optimization, or other methods to support business organizations to make better decisions (Accenture Global Operations Megatrends Study 2014). In the strategic stage of supply chain planning, BDBA plays a key role and it is used to support firms to make strategic decisions in terms of resources, supply chain network design, and product design and development. In the operational planning phase, BDBA is applied to assist management in supply chain performance decisions, which often include planning of demands, inventory, and logistics activities (C. L. P. Chen and Zhang 2014). Many companies around the world use BDA to identify and predict their customers’ behavior and achieve flexibility in relationships, logistics, and support. They can also effectively manage demand or cost fluctuations. BDA has been used in Logistics and Supply Chain Management (LSCM) due to the sensitivity and importance of historical data and information. LSCM faces significant challenges that can potentially lead to inefficiency and wasting of resources in supply chains, including delayed deliveries, increased fuel costs, inappropriate suppliers, and increased unsatisfied customer expectations (Barnaghi, Sheth, and Henson 2013). To achieve the goal of this chapter, which is to prioritize the barriers to BDA in the supply chain and logistics, the hierarchical best-worst method (HBWM) is used. This method has been introduced in Tabatabaei et al. (2019) and performs well for hierarchical decision-making. The reason for choosing HBWM in this study is that it requires less data for decision-making and offers more consistent comparisons. This method will be described in Section 3.3. In this chapter, we examine the barriers and challenges of BDA in the supply chain management. These barriers and limitations are identified using experts’ opinions and reviewing the literature. Then, we determine their significances using the MCDM method. The results of the study reveal the problems and issues that organizations and companies face in analyzing BD and identify the most important factors. The study also shows that organizations can achieve competitive advantage by employing appropriate strategies and removing barriers. The rest of the chapter is organized as follows: Section 3.2 reviews the literature on the barriers to BDA; Section 3.3 describes the methods used to prioritize the barriers; in Section 3.4, the barriers are prioritized based on experts’ opinions and hierarchical decision-making; conclusions of the chapter are provided in Section 3.5.

3.2 BARRIERS TO BDA: BACKGROUND In this section, we will examine the barriers to implementing and using BDA. Many studies have been conducted to identify these barriers and various criteria and subcriteria have been defined for this purpose. Here, we intend to identify and analyze

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some of these barriers that are more relevant to supply chain management and logistics. Given the importance of BD and BDA, the purpose of this section is to identify and examine the major barriers to BDA adoption in supply chains and logistics. A study examining organization and technology management practices at 330 state-owned companies in North America revealed that many organizations were not ready to use BD to improve organizational performance (McAfee and Brynjolfsson 2012), and they needed to overcome several barriers (or challenges) in this regard. These barriers include the need to acquire new skills by employees and upgrade IT infrastructure as well as instill new managerial practices or new organizational culture across the organization (Manyika 2011). In order to examine the barriers to BDA, we searched for research literature using a variety of keywords, such as the “barriers to BDA,” “challenges of using BDA,” and the like. Few researchers have investigated and identified the barriers to BDA in the supply chain management and logistics. For example, a study was conducted to examine and identify the major barriers to the use and adoption of BDA in manufacturing supply chains in Bangladesh, and these barriers were prioritized based on the literature and experts’ opinions (Moktadir et al. 2019). In another study, techniques for removing barriers to acceptance and use of BDA were also discussed using qualitative analysis (Alharthi, Krotov, and Bowman 2017). A qualitative framework was also used to examine the challenges of using BDA in the telecommunications industry in South Africa (Malaka and Brown 2015). A conceptual framework was used to review articles related to the threats and opportunities of using BDA for international development (Hilbert 2016). An in-depth review process was carried out according to the literature review to accurately examine and identify the barriers to the acceptance and use of BD, and the transient and permanent barriers were categorized and discussed (Brohi, Bamiah, and Brohi 2016). Twenty-six factors influencing BDA acceptance and utilization were identified and evaluated and integrated into a conceptual framework that encompasses technology, organization, and environment. The factors were identified by reviewing the literature during the period 2009–2014, and the results of the article enriched the literature on BD (Sun et al. 2018). Factors and challenges affecting the acceptance of BD in Korean companies were identified. Importance of factors was determined using the analytic hierarchy process and opinions of experts in Korea. The results showed that understanding the benefits of BD and technological capabilities are the most important factors influencing BD use in this country (Park, Kim, and Paik 2015). In order to identify and rank important factors in BD acceptance and predict the impact of BD acceptance on the performance of manufacturing companies, important factors were identified: DEMATEL-ANFIS hybrid approach and then these factors were categorized based on technological, Organizational, and environmental dimensions. Data were collected from 234 industry executives involved in decision-making about IT provision in the Malaysian manufacturing companies. The results of the research showed that technological factors have the most influence on BD adoption and corporate performance (Yadegaridehkordi et al. 2018). In this chapter, based on the research literature and opinions of various supply chain experts and managers, the barriers to the acceptance and use of BDA in supply chain and logistics management were selected. Therefore, it was found that given the

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FIGURE 3.1 Barriers of BD and BDA.

specific international conditions and relations between countries, political barriers should also be considered as one of the important factors influencing the acceptance and usage of BDA. It should be noted, however, that the barriers to using and accepting BDA in different countries can vary depending on different cultures, different laws and policies, and so on. The barriers examined in this study have been selected through extensive discussions with supply chain managers and business owners in Iran and may be subject to change for use in other countries. These barriers are categorized into five main categories: technological barriers, specialized and human-centered barriers, data-based barriers, organizational barriers, and political barriers, which we briefly refer to as TEDOP. Each category contains several barriers in a specific area (Figure 3.1). A brief description and references for each factor can be seen in Table 3.1.

3.3

METHODOLOGY

The HBWM proposed by (Tabatabaei et al. 2019) was used to evaluate and calculate the weights of the barriers and sub-barriers. This method was used because it needs fewer data and has more consistent pairwise comparisons needed for decision making. HBWM is very useful when the decision-making problem has different criteria

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TABLE 3.1 Barriers to BDA in LSCM Main Barriers

Sub-barriers

Technological barriers (BR1)

Lack of accessibility of specific BDA tools (BR11)

Expertise and human-related barriers (BR2)

Data-related barriers (BR3)

A Brief Description

In various supply chains, choosing the appropriate BDA tools will improve performance. Lack of infrastructural The implementation of new facility (BR12) technologies in the organization requires proper infrastructure. Lack of interest in using Existing technology for BDA in novel technology (BR13) supply chains is costly. High cost of implementation The implement of BDA tools in (BR14) organizations requires high costs. New Technology The characteristics of BD are Compatibility (BR15) perceived as being consistent with the existing IT architecture in an organization Lack of IT experts (BR21) Lack of IT experts may increase data processing errors, data damage, or confound data analysis and interpretation. Ease of reception (BR22) The development of BDA tools in the organization requires special attention to the conditions of the employees. Lack of appropriate Lack of appropriate facilities in facilities to improve BDA educational organizations to research tools (BR23) existing difficulty and improve BDA tools. The complexity of data A variety of data formats from (BR31) distinct sources may create complexity in data storage and integration. Data quality (BR32) Data quality is sensitive due to a variety of data sources, storage tools, firms, etc. Data security and privacy Lack of data security and privacy are (BR33) the significant barriers to the adoption of BDA, as data must be secure if they are to compete in the international market. Rate of data growth (BR34) Increasing the rate of data generation can continually cause problems for the BDA. Many organizations simply opt to delete old data instead of trying to accommodate data growth.

Key Reference (Moktadir et al. 2019) (Trelles et al. 2011) (Moktadir et al. 2019) (Sun et al. 2018) (Sun et al. 2018)

(Alharthi, Krotov, and Bowman 2017) This study

(Moktadir et al. 2019)

(Fallik 2014)

(Malaka and Brown 2015) (Alharthi, Krotov, and Bowman 2017) (Alharthi, Krotov, and Bowman 2017) (Continued)

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TABLE 3.1 (Continued) Barriers to BDA in LSCM Main Barriers

Sub-barriers

Organizational barriers (BR4)

Lack of training facilities (BR41)

Political barriers (BR5)

A Brief Description

Adaptation of BDA inside companies may perhaps be obstructed by the absence of suitable training facilities for employees. Time constraints (BR42) Time constraints are one of the biggest issues in handling new technologies or tools in industries. Organizational culture The system of assumptions, values, (BR43) and beliefs of employees in dealing with new technology and how to accept changes and mutations. Share data between Lack of data sharing policies between organizations (BR44) organizations. Management support (BR45) Managers are willing to allocate sufficient resources and encourage the initial adoption of BD. Organizational structure The organization has a well-organized (BR46) structure that is well-suited to the reception of BD. Sanctions imposed by Existence of international sanctions governments (BR51) over disputes by governments as a barrier to access to modern information and technology and tools. The international restrictive International laws that apply to laws (BR52) technology transfer may affect the use of new technology or tools. Competitive pressure (BR54) The degree to which firms influence the decision-making of BD adoption according to the competitor’s behavior.

Key Reference (Malaka and Brown 2015)

(Zhong et al. 2016) This study

(Moktadir et al. 2019) (Sun et al. 2018) (Sun et al. 2018) This study

This study

This study

and subcriteria, and here, it calculates the importance of barriers and sub-barriers based on pairwise comparisons made by experts. In this study, HBWM is used to calculate the importance of barriers and sub-barriers. The hierarchical structure of the decision-making problem in this study is shown in Figure 3.2.

3.3.1

THE STEPS OF HBWM

The notations and their descriptions for the HBWM technique are provided in Table 3.2.

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FIGURE 3.2

Hierarchical structure of barriers to BDA in the supply chain management.

TABLE 3.2 Notations and Their Descriptions Sets Parameters

Notation

Definition

j ∈ C = {1,2,…,n} k ∈Ck = {1,2,…,m} aBj a jW

Barriers Sub-barriers

wBj

Preference of the most important barrier over j-th barrier Preference of j-th barrier over the least important barrier Preference of the most important sub-barrier over k-th sub-barrier for j-th barrier Preference of k-th sub-barrier over the least important sub-barrier for j-th barrier Weight of the most important barrier Weight of j-th barrier Weight of the least important barrier Weight of the most important sub-barrier for j-th barrier

wkj

Weight of k-th sub-barrier for j-th barrier

wWj

Weight of least important sub-barrier for j-th barrier

Gwkj

Global weight of k-th sub-barrier for j-th barrier

j aBk

j akW

Variables

wB wj wW

According to Figure 3.2, the steps of this technique are shown as follows: Step 1. Determining the set of problem barriers and sub-barriers: At this stage, the problem barriers and sub-barriers are determined as {c1 , c2 ,…,cn } and {c1k , c2 k ,…,cnk }, respectively. Step 2. Determining the most important (best) and the least important (worst) barrier and sub-barriers: the most important and the least important barrier and sub-barriers are identified. Step 3. At this stage, the preference of the most important barrier over each of other barriers is defined as a number between 1 and 9, which is shown

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as AB = ( aB1 , aB 2 ,…,aBn ), where aBi is the preference of the most important barrier over ith barrier and aBB = 1. Step 4. At this stage, the preference of each barrier over the least important barrier is defined as a number between 1 and 9, which is shown as AW = ( a1W , a2W ,…,anW ), where a jW is the preference of jth barrier over the least important barrier and aWW = 1. Step 5. At this stage, the preference of the most important sub-barrier over each of other sub-barriers is defined as a number between 1 and 9, which j j j , where aBk is the preference of the most is shown as AB = aB1 , aBj 2 ,…,aBk j important sub-barrier over jth sub-barrier and aBB = 1. Step 6. At this stage, the preference of each sub-barrier over the least important sub-barrier is defined for each barrier as a number between 1 and 9, which j j is shown as AB = a1jW , a2jW ,…,akW , where akW is the preference of kth j sub-barrier over the least important sub-barrier for jth barrier and aWW =1. * * * Step 7. Find the weights of the barriers w1 , w2 ,…, wn and sub-barriers w1j* , w2j* ,…, wkj* .

(

)

(

(

)

(

)

)

Given that the non-negativity of the weights, we can formulate the programming model of HBWM as follows (Equations 3.1–3.8): min ξ L +

∑ξ

L j

(3.1)

j

wB − aBj w j ≤ ξ L , for all j

(3.2)

wB − aBj w j ≤ ξ L , for all j

(3.3)

j wBj − aBk wkj ≤ ξ jL , for all j & all k

(3.4)

j wkj − akW wWj ≤ ξ jL , for all j & all k

(3.5)

Gwkj = w j × wkj , for all k

(3.6)

∑w

j

= 1,  w j ≥ 0

(3.7)

j k

= 1,  wkj ≥ 0

(3.8)

j

∑w k

Figure 3.3 represents the framework of the research methodology.

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FIGURE 3.3

3.3.2

Framework of the research methodology.

DETERMINING THE CONSISTENCY RATE

L According to the proposed approach, ξ of reference comparisons determined for L the barriers and ξ j of reference comparisons determined for the sub-barriers of each barrier are calculated separately by the HBWM technique, and the results are solved in a similar way to the original best-worst method (BWM), the consistency index (CI) presented for the original BWM can be used to calculate the consistency rate (CR) of the reference comparisons performed for the barriers and sub-barriers. The CR in the BWM is determined concerning the value of the preference of the most important barrier over the least important barrier and the preference of the most important sub-barrier over the least important sub-barrier of jth barrier. The CI value is specified in Table 3.3 (Rezaei 2015).

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TABLE 3.3 CI in BWM j aBW , aBW

1

2

3

4

5

6

7

8

9

CI ( max ξ )

0.00

0.44

1.00

1.63

2.30

3.00

3.73

4.47

5.23

Considering the minimum deviations of the reference comparisons performed for the barriers ξ * and the sub-barriers of each barrier ξ *j which are obtained through the BWM, and the CI specified in Table 3.3, we can obtain the CR for the barriers and sub-barriers of each barrier using Equations (3.9) and (3.10).

( )

3.4

( )

CR =

ξ* CI

(3.9)

 CR =

ξ *j CI

(3.10)

RESULTS AND DISCUSSION

Developing countries are looking to use BDA in their industries and manufacturing companies, but there are many obstacles to accepting BDA in LSCM of these countries. Industry managers in developing countries seek to identify and address these barriers. Iran is one of the countries facing many challenges in implementing BDA. In this study, barriers to BDA in LSCM are identified and ranked according to the existing production environment in Iran. The identified barriers include the five main barriers and their sub-barriers. HBWM is used to weight and rank the barriers and sub-barriers. The HBWM reference comparison questionnaires were prepared by consulting multiple experts in the Iranian manufacturing industry. The most significant and least significant barriers were selected for reference comparisons and then the rest of the barriers were compared and a number ranging from 1 to 9 was assigned to each of them. Table 3.4 shows reference comparisons of the main and sub-barriers. Using Table  3.4 and the HBWM model presented in Equations (3.1)–(3.8), the weights and ranks of the main and sub-barriers were calculated using LINGO software. Finally, the weights of the main barriers and local and global weights of sub-barriers were calculated. The weights and ranks of the barriers are shown in Table 3.5. The results show that the data-related barriers have the highest weight in the LSCM, and also, the most important sub-barrier in this area is the degree of data complexity that will undoubtedly have a significant impact on BD analysis. This shows that managers should pay more attention to the barriers that are directly related to data when using BDA in LSCM. Technological barriers were identified as the second major barriers. Technological factors play an important role in the acceptance and implementation of BD, and appropriate infrastructure for technological tools must be prepared. Expertise and human-related barriers and organizational barriers jointly ranked third, and political barriers ranked fifth.

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TABLE 3.4 Reference Comparisons of the Main Barriers and Sub-barriers Main Barriers

B-O

O-W

Sub-barriers

B-O

O-W

Sub-barriers

B-O

O-W

BR1 BR2 BR3 BR4 BR5

2 3 1 3 4

3 2 4 2 1

BR11 BR12 BR13 BR14 BR15

4 1 7 3 5

3 7 1 4 2

BR21 BR22 BR23

1 4 3

4 1 2

Sub-barriers

B-O

O-W

Sub-barriers

B-O

O-W

Sub-barriers

B-O

O-W

BR31 BR32 BR33 BR34

1 4 5 2

5 2 1 3

BR41 BR42 BR43 BR44 BR45 BR46

1 2 2 5 3 4

5 4 4 1 3 2

BR51 BR52 BR53

2 1 3

2 3 1

TABLE 3.5 Weights and Ranks of the Main and Sub-barriers Main Barriers

Weights

BR1

0.221

BR2

0.147

BR3

0.397

BR4

0.147

BR5

0.088

Subbarriers

Local Weights

Local Rank

Global Weights

Global Rank

BR11 BR12 BR13 BR14 BR15 BR21 BR22 BR23 BR31 BR32 BR33 BR34 BR41 BR42 BR43 BR44 BR45 BR46 BR51 BR52 BR53

0.140 0.498 0.062 0.187 0.112 0.628 0.143 0.228 0.500 0.136 0.091 0.272 0.336 0.191 0.191 0.058 0.127 0.096 0.292 0.542 0.167

3 1 5 2 4 1 3 2 1 3 4 2 1 2 2 6 4 5 2 1 3

0.031 0.110 0.014 0.041 0.025 0.092 0.021 0.034 0.198 0.054 0.036 0.108 0.049 0.028 0.028 0.085 0.019 0.014 0.026 0.048 0.015

12 2 20 9 16 4 17 11 1 6 10 3 7 13 13 5 18 20 15 8 19

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Political barriers are strongly linked to international relations and existing international mechanisms and may have different outcomes for each different country. In general, data complexity was identified as the most significant sub-barrier. Lack of infrastructural facilities was identified as the second significant sub-barrier. This indicates the lack of suitable infrastructure for the BDA in Iranian manufacturing industries. The third significant sub-barrier is the rate of data growth in industries that managers have often overlooked. The ranks of the other sub-barriers are provided in Table 3.5. Figure 3.4 shows the weights obtained using the proposed method for the main and sub-barriers. The results of this study lead to identifying the significance of the factors influencing BDA for the Iranian industrial environment. The results of this study are more applicable and helpful for industries looking to abandon traditional management practices and move toward new management approaches. It is clear that removing all barriers to BDA is very difficult at the same time, so in the first place, removing the more important barriers should be the focus of industry managers.

3.5 CONCLUSION The purpose of this study was to evaluate the barriers to BDA in LSCM. A new MCDM approach, called HBWM, has been used to evaluate these barriers that can calculate the weights of the criteria and alternatives simultaneously. It is difficult to remove the barriers to BDA without a proper strategy. Therefore, identifying and prioritizing these barriers can help decision-makers. Determining the importance of each barrier helps industry managers understand the significance of each barrier in their supply chains. Also, they can formulate their future strategies and policies based on the significance of these barriers. The method can also lead to plans to overcome these barriers. In this study, HBWM was used to determine the significance of the main and sub-barriers. The benefits of this approach include the calculation of the weights of the main and sub-barriers in a single integrated model. Five categories of main barriers and 21 additional barriers were selected based on the literature

FIGURE 3.4 Final prioritization of the barriers to BDA usage and acceptance.

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review and experts’ opinions. Then, the HBWM reference comparisons’ questionnaires were completed by several experts in the Iranian manufacturing industry, and its data were used as inputs to the HBWM model. The results of this study showed that data-based barriers are the most important barriers. Technological barriers were identified as the second important category of barriers. Expertise and human-related barriers and organizational barriers jointly ranked third, and political barriers were identified as the least important category of the main barriers. This prioritization was also done for all sub-barriers. The results of this study revealed the problems and challenges that the Iranian manufacturing industries face when using BDA. Organizations can use the proposed approach to develop appropriate solutions to remove barriers based on the significance and priority of each barrier and achieve a competitive advantage. This information can be helpful for managers’ future decisions on issues such as demand forecasting, production planning, and so on.

REFERENCES Accenture Global Operations Megatrends Study. 2014. “Big data analytics in supply chain: Hype or here to stay?” Alharthi, Abdulkhaliq, Vlad Krotov, and Michael Bowman. 2017. “Addressing barriers to big data.” Business Horizons 60 (3): 285–92. Barnaghi, Payam, Amit Sheth, and Cory Henson. 2013. “From data to actionable knowledge: Big data challenges in the web of things.” IEEE Intelligent Systems 6: 6–11. Brohi, Sarfraz Nawaz, Mervat Adib Bamiah, and Muhammad Nawaz Brohi. 2016. “Identifying and analyzing the transient and permanent barriers for big data.” Journal of Engineering Science and Technology 11 (12): 1793–1807. Chen, C L Philip, and Chun-Yang Zhang. 2014. “Data-intensive applications, challenges, techniques and technologies: A survey on big data.” Information Sciences 275: 314–47. Chen, Hsinchun, Roger H L Chiang, and Veda C Storey. 2012. “Business intelligence and analytics: From big data to big impact.” MIS Quarterly 36 (4): 1165–88. Fallik, Dawn. 2014. “For big data, big questions remain.” Health Affairs 33: 1111–4. Hilbert, Martin. 2016. “Big data for development: A review of promises and challenges.” Development Policy Review 34 (1): 135–74. Malaka, Iman, and Irwin Brown. 2015. “Challenges to the organisational adoption of big data analytics: A case study in the South African telecommunications industry.” In Proceedings of the 2015 Annual Research Conference on South African Institute of Computer Scientists and Information Technologists, Stellenbosch, South Africa, 27. ACM. Manyika, James. 2011. “Big data: The next frontier for innovation, competition, and productivity.” htttp:// Www.Mckinsey.Com/ Insights/ MGI/ Research/ Technology_and_ Innovation/ Big_data_The_next_frontier_for_innovation. McAfee, Andrew, and Erik Brynjolfsson. 2012. “Big data: The management revolution.” Harvard Buiness Review, 90 (10): 61–68. Moktadir, Md Abdul, Syed Mithun Ali, Sanjoy Kumar Paul, and Nagesh Shukla. 2019. “Barriers to big data analytics in manufacturing supply chains: A case study from Bangladesh.” Computers & Industrial Engineering 128: 1063–75. Park, Jong-Hyun, Moon-Koo Kim, and Jong-Hyun Paik. 2015. “The factors of technology, organization and environment influencing the adoption and usage of big data in Korean firms.” Rezaei, Jafar. 2015. “Best-worst multi-criteria decision-making method.” Omega 53: 49–57.

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Sun, Shiwei, Casey G Cegielski, Lin Jia, and Dianne J Hall. 2018. “Understanding the Factors Affecting the Organizational Adoption of Big Data.” Journal of Computer Information Systems 58 (3): 193–203. Tabatabaei, Mohammad Hashemi, Maghsoud Amiri, Mohammad Ghahremanloo, Mehdi Keshavarz-Ghorabaee, Edmundas Kazimieras Zavadskas, and Jurgita Antucheviciene. 2019. “Hierarchical decision-making using a new mathematical model based on the best-worst method.” International Journal of Computers Communications & Control 14 (6): 710–25. Trelles, Oswaldo, Pjotr Prins, Marc Snir, and Ritsert C Jansen. 2011. “Big data, but are we ready?” Nature Reviews Genetics 12 (3): 224. Wang, Gang, Angappa Gunasekaran, Eric W T Ngai, and Thanos Papadopoulos. 2016. “Big data analytics in logistics and supply chain management: Certain investigations for research and applications.” International Journal of Production Economics 176: 98–110. Wang, Yichuan, and Nick Hajli. 2017. “Exploring the path to big data analytics success in healthcare.” Journal of Business Research 70: 287–99. Yadegaridehkordi, Elaheh, Mehdi Hourmand, Mehrbakhsh Nilashi, Liyana Shuib, Ali Ahani, and Othman Ibrahim. 2018. “Influence of big data adoption on manufacturing companies’ performance: An integrated DEMATEL-ANFIS approach.” Technological Forecasting and Social Change 137: 199–210. Zakir, Jasmine, Tom Seymour, and Kristi Berg. 2015. “Big data analytics.” Issues in Information Systems 16 (2): 81–90. Zhong, Ray Y, Stephen T Newman, George Q Huang, and Shulin Lan. 2016. “Big data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives.” Computers & Industrial Engineering 101: 572–91.

4

Big Data in Procurement 4.0 Critical Success Factors and Solutions Bernardo Nicoletti Department of Business Studies, Temple University, Rome, Italy

Andrea Appolloni Department of Management and Law, Faculty of Economics, University of Rome Tor Vergata, 00133 Rome, Italy School of Management, Cranfield University, Cranfield, Bedford MK430AL, UK Institute for Research on Innovation and Services for Development (IRISS), National Research Council (CNR), 80134 Naples, Italy

CONTENTS 4.1 4.2 4.3 4.4 4.5

Introduction ....................................................................................................46 Macroenvironment.......................................................................................... 47 Literature Review ........................................................................................... 47 Methodology ................................................................................................... 49 Critical Success Factors for Procurement 4.0 ................................................. 51 4.5.1 Cybernetics ......................................................................................... 51 4.5.2 Communication .................................................................................. 52 4.5.3 Controllership ..................................................................................... 52 4.5.4 Collaboration ...................................................................................... 52 4.5.5 Connection .......................................................................................... 53 4.5.6 Cognition ............................................................................................ 53 4.5.7 Coordination ....................................................................................... 54 4.5.8 Confidence .......................................................................................... 55 4.6 Critical Success Factors and Procurement Cycle ........................................... 55 4.7 Supporting Solutions....................................................................................... 55 4.8 Application of the Model ................................................................................ 58 4.9 Conclusions, Practical Implications, and Future Research ............................ 59 Abbreviations ...........................................................................................................60 References ................................................................................................................60 45

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4.1 INTRODUCTION The concept of procurement 4.0 was born out of the idea of industry 4.0. The term industry 4.0 derives from the consideration that it represents the fourth industrial revolution after the first three ones, based on the steam engine, the electricity, and the computer. Immediately, it started to be considered a different concept with profound implications for organizations. The term industry 4.0 has been published for the first time in 2011 when an association of representatives from industry, politics, and academia promoted the idea as an approach to improve the German manufacturing competitiveness (Schwab, 2017). Industry 4.0 is the merger of information and communication technologies with plant automation (Nicoletti, 2017). The topics of Production 4.0 and Logistics 4.0 have already been analyzed and discussed. The field of procurement 4.0 needs more investigation. The topic is barely ever mentioned but extremely important due to the increasing relevance of procurement in organizations. This chapter aims to provide to academics, including students, and practitioners an opportunity to understand how to implement, monitor, and benefit from the adoption of industry 4.0 in the procurement processes within organizations. In other terms, it provides a clearer insight into the potential benefits produced by the implementation of new solutions and define a structured plan of procurement 4.0 (Pellengahr et al., 2016). The concept of industry 4.0 started in the manufacturing environment, but soon expanded to all types of organizations. In the new era of procurement management and in the new scenario of procurement 4.0, managers need to redesign the value added by procurement within the organization (Bienhaus and Haddud, 2018). Industry 4.0 also involves a considerable change in procurement (Luo, et al, 2017), labeled in this chapter as procurement 4.0. In general, the transformation of the procurement processes can produce several benefits not only connected with the direct activities (such as selection, negotiations, contracting, and so on) but also to the support of the daily business and administrative activities and to help in decision-making. In particular, procurement 4.0 allows the procurement to concentrate on strategic data-driven decisions and activities (Kiron, 2017). Procurement 4.0 is a powerful approach to improve the integration of procurement with other functions and with external entities supporting organizational efficiency, effectiveness, profitability, and in developing new business models (Bienhaus and Haddud, 2018). In the case of procurement, there are successive phases indicated with numbers (n.0).1 • Procurement 1.0 supports the essential functions of procurement. The service was in its infancy. The main objective was to try manually to get the right product, at the right place, at the right time, and in the correct conditions. • Procurement 2.0 was born out of integrated procurement services. It stresses the need for procurement to coordinate with the other functions in the organization. 1

https://www.slideshare.net/OscarLopezTejido/201505-procurement-3dot0, Accessed 25 March 2019.

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• Procurement 3.0 is the procurement based on collaboration and partnerships with the vendors. • Procurement 4.0 is the latest conceptualization of how modern and innovative organizations should procure goods and services. Digitization or smart manufacturing might be considered a driving factor behind procurement 4.0. However, it would be short-sighted to view procurement 4.0 as just that. Procurement must respond faster to the requests of the organization, be interconnected, digitized, and agile. Procurement 4.0 is the integration of information and communication technologies and automation in support of procurement to add additional value to the customer and the entire organization. It can also be considered a reorganization with the objective of implement a digitization to decrease uncertainties through improved transparency of information among procurement partners (Bag et al., 2020). The literature review considers a certain number of authors who have analyzed procurement 4.0. There is no business model able to describe the Critical Success Factors (CSFs) of procurement 4.0. Organizations are often failing in building a correct roadmap for achieving the objectives that they set. As a result, the adoption of procurement 4.0 is still unclear and in its infancy (Pellengahr et al., 2016). The research presented in this chapter aims to push the organizations to move best way in the direction of procurement 4.0.

4.2

MACROENVIRONMENT

The world is undergoing a dramatic impact from the current outbreak of the pandemic; organizations did not escape from this situation (Williams et al., 2020). One of the functions most impacted has been procurement. Procurement was already undergoing some drastic changes in connection with the digital transformation. The pandemic has brought difficulty in procurement from many vendors and challenges in logistics (Kuziemski, and Misuraca, 2020). There is the need to work and organize remote working. At the same time, there is a drastic surge of online activity and e-commerce. In this scenario, procurement cannot stay still. It must become an engine of innovation and change. In this vision, it is necessary a radical re-thinking of procurement in all the organizations. In this respect, it is important the acceleration toward procurement 4.0, initially generated by the development of industry 4.0. Even if procurement 4.0 was born out of industry 4.0, it is essential to have a vision of procurement 4.0 as a transformation not only in the procurement function but also in the relationships with partners outside the organization. Procurement 4.0 includes the idea of a different business model, more agile, lean, interconnected, and flexible; it is essential to understand which are the CSFs. It is necessary to warn that the situation is in a rapid change and procurement 5.0 is not far away. The CSFs will be important to assure success to the transforming organizations.

4.3 LITERATURE REVIEW Industry 4.0, as related to the procurement and supply chain areas, is a relevant research area attracting particular interest in academia and as well as practitioners

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(Frederico et al., 2019). In this area, the literature is limited as of now. According to Bag et al. (2020), the combination of digitization and procurement is an area to be investigated. Geissbauer et al. (2016) highlighted the actual state-of-the-art of the interaction of “procurement” and “digitization.” They outlined a future framework based on challenges and requirements. A systematic literature review by Frederico et  al. (2019) aims to propose the term “Supply chain 4.0” and a novel conceptual framework capturing the essence of industry 4.0. This study aims to facilitate perspective of the development of a supply chain 4.0 strategy from infancy to a maturity level. While digitization is a key driver of industry 4.0; an article by Bienhaus and Haddud (2018) underlines the differences in the organization approaches to deal with this topic. It helps to get a clearer picture of the opportunities and challenges relative to the digital transformation. The findings of this paper indicate that digitization of procurement process can support daily business and administrative activities. Procurement will also start supporting complex decision-making processes. In this way, procurement will become more focused on strategic decisions and activities. It will become a crucial interface to support organizational efficiency, effectiveness, profitability, and the definition of new business models, products, and services. Other authors state that industry 4.0 can also be called “smart manufacturing” and “the next industrial revolution” (Geissbauer et al., 2016). A combination of new technologies – from big data analytics (Russom, 2011) to 3D printing (Mohr, and Khan, 2015) – is revolutionizing organizations’ decisional, operational, and administrative processes. It supports the creation of innovative products and services. In diverse industries, organizations need to consider the way digital innovation can disrupt organizations’ work today and the entire value proposition of procurement to their vendors, customers, and process partners. The on-going discussions about the digital revolution and disruptive competitive advantages led to the creation of the industry 4.0 approach. The term and its actual impact on businesses are still unclear. A paper addresses this gap and explores more specifically the consequences and potentials of industry 4.0 for the procurement, supply, and distribution management functions (Gas and Kleeman, 2016). These authors used combined literature-based deductions and results from a qualitative study to explore this trend. The findings show that solutions underlying industry 4.0 legitimize the next level of maturity in procurement. Empirical findings support these conceptual considerations. Henke and Schulte (2015) state that the partners’ relationships and the production solutions interface layer position procurement as a critical factor for the development of industry 4.0. They propose several business opportunities in their studies. Batran et al. (2017) reviewed the state of the art in the literature specifically on procurement 4.0. The authors show that the dynamic, interconnected value networks are critical factors of sustainable business success. Strategic procurement officers, in their new role as value network coordinators, should manage and guide this transformation. It is necessary to develop a tool for procurement with which it is possible to check the current value of the partners at any time. In this way, the organization is able to evaluate the partners. Similarly, procurement must have a tool that simplifies the management of a complex business while ensuring best-price security. The books on “Procurement 4.0” start exactly at these points (Kerkhoff et al., 2017;

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Nicoletti, 2020). They present an integrated solution that is under development in certain markets. Procurement costs are the central lever for increasing the economic viability of organizations (Klünder et  al., 2019). Procurement costs can be material costs and commodity costs. Material costs are more important across all industries. They are getting cheaper over time. This paper, based on the procurement process, derives five central cost elements and combines them with corresponding technologies to establish a procurement 4.0-framework. The empirical examination of the correlation between digitization efforts and procurement costs shows significant results. It emphasizes the importance of distinguishing between material costs and commodity costs. The results show the expected negative correlation, which confirms the hypothesis of cost reduction through digitization. The results are without doubts supporting the use of the procurement 4.0 framework. The development of procurement 4.0 systems is not necessarily a simple job. Bienhaus and Haddud (2018) provided a list of barriers that are curbing the development of procurement 4.0 systems. They suggested that focus on procedures, capacity, and capability can eliminate these impediments. The literature reveals the importance of the procurement function for organizations, and especially for manufacturers, organizations need to focus more on digital and open programs to deploy procurement 4.0. Wang et al. (2016) noted that the amount of data produced and communicated over the Internet is significantly increasing, especially in organizations applying industry 4.0. Organizations can benefit from the analysis of this massive influx of data. These authors underlined the importance of big data business analytics. They reviewed and classified the literature on the application of Big Data and Business Analytics (BDBA) on logistics and supply chain management, based on the nature of analytics (descriptive, predictive, and prescriptive) and the focus on strategy and operations. The objective was to define the use of big data analytics for providing unique insights into, inter alia, market trends, customer sourcing patterns, and maintenance cycles. BDBA can also help in lowering costs and enabling more targeted business decisions. These authors proposed a maturity framework of what they defined as supply chain analytics (SCA), based on four capability levels, that is, functional, process-based, collaborative, agile SCA, and sustainable. In conclusion, the currently available literature evidences the strategic importance of the procurement function in the organizations. Manavalan and Jayakrishna (2019) suggest investigating more empirical and practice-oriented research to extend the knowledge base in the future. There is limited specific literature on procurement 4.0 (Bag et al., 2020). Organizations are currently lacking a concrete roadmap as to how the targets that they set are achievable. As a result, the implementation of procurement 4.0 needs to be better defined and especially guided. More definitions of theoretical frameworks and empirical research are required to provide a basis to extend the knowledge base and get success. This chapter has this objective.

4.4

METHODOLOGY

This research aims to investigate the CSFs for procurement 4.0 to succeed, the solutions to be used, and the expectations of the organizations regarding procurement  4.0.

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TABLE 4.1 Generations of Industry and Procurement Generation

Industry n.0

Procurement n.0

1

Steam Engine Industrial production and transportation Electricity Assembly line Division of labor Computer Network ERP Cloud Internet of Everything Convergence of industrial automation and ICT

No distinct function from operations

2

3

4

Procurement function was born Procurement at a regional or global level Integrated procurement E-procurement Container transportation Cognitive Procurement Coordination of the organization’s ecosystem Agile Procurement

Adapted from: Nicoletti (2020).

Initially, there was an extensive analysis of the available literature on procurement 4.0 modelling. The results were limited since there are very few researches on this subject. A model for Procurement 4.0 was designed with respect to the stages of Industry n.0 (see Table 4.1) (Nicoletti, 2020). The question came up on which would be the CSFs. The following action was to conduct a focus group and interviews with two different groups. The scope of the focus group was to define the requirements and understand which characteristics of Procurement 4.0 would satisfy them. The first one was composed of 15 persons from different industries, potentially interested in procurement 4.0. The second group comprised eight persons from various functions of the same service organizations already engaged in industry 4.0, but with a desire to extend it to other functions. There was no overlap between the two groups. The second group aimed to understand the potential uses of industry 4.0 in a service organization with respect to the industrial base of the components of the first group. Qualitative research was also conducted in the form of semistructured interviews, aiming at generalizing as much as possible the procurement 4.0 concept to different types of organizations. Qualitative research was especially suited to understanding the experiences of the participants, the environment in which the participants operate, and making discoveries. The interview questions were complemented by the analysis of a pilot study on procurement 4.0 by Perona (2019). The interviews were focused on procurement 4.0 CSFs within the organizations in the first part and ask about the experiences and expectations of the participant to the groups regarding procurement 4.0 solutions in the second part. After the interviews, the recordings and the notes taken during the interviews were used to synthetize the hypothesis of the CSFs model. The transcripts summarized the opinions and predictions expressed by the participants of the two groups. In the following pages, the results of one organization are presented, summarized, and analyzed.

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4.5 CRITICAL SUCCESS FACTORS FOR PROCUREMENT 4.0 This section examines in detail the CSF for procurement 4.0. The CSF can be summarized with six words starting with “C” for mnemonic reasons: Cybernetics, Communication, Control, Collaboration, Connection, and Cognition. There are two other CSFs “Cs.” Coordination requires strong governance of procurement 4.0. At the same time, “Confidence” refers to mutual trust. It is the basis for the success of a procurement 4.0 initiative. The following pages examine each of the eight “Cs” in the case of procurement 4.0.

4.5.1

CYBERNETICS

The industry 4.0 revolution shifts an organization from a supply chain model to a value network (Nicoletti, 2017). The modes of marketing, operations, and other functions are not anymore linear (“chain”). There is now a network of organizations in the ecosystem which add value to the components of this ecosystem. It links automated machines and computer applications, customers, partners, and regulators. At the same time, procurement processes need to be lean and streamlined. The “smart” operations centers (Shrouf, 2014) share real-time information among all stakeholders. They make the procurement processes optimized and transparent. Cybernetics is the science of communication and control theory. It is relative to the comparative study of automatic control systems (such as the nervous system and brain).2 With the use of cybernetics, procurement 4.0 needs an automatic system to be sure that data are available at the right time and in the right place to support the procurement activities. At the same time, the system needs to be secure and, hence, protected and accessed only by authorized profiles. The management of procurement 4.0 is based on an extensive network in which all parties involved in the procurement ecosystem (organization, customers, distributors, financial institutions, government entities, and partners) have access to what they need. This network is made possible by an internet platform that handles the relationships with all stakeholders in real time. The intravalue network or the movement of goods within the operations is automated and integrated. Information received from intranet and internet platforms, used by all stakeholders, is at the basis of the planning (Vis, I. F., 2006). The platforms connect from an information point of view all stakeholders. They can provide the information necessary to the movements of goods and the provision of services. In this way, there is a reduction in inventory costs. There is a real-time processing of the customer requirements and orders to partners. It is necessary to plan the dates of receipt of all the materials and components needed for the production, to provide the final products, possibly pulled by the customers, in proper quantity and quality, and on schedule. The organizations’s management and partners can track deliveries using GPS to know their exact position in real time. The complete automation has the main purpose to help operators in their activities and to provide an efficient, secure, and safer working environment for the workers.

2

https://www.merriam-webster.com/dictionary/cybernetics. Accessed 11 February 2020.

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4.5.2 COMMUNICATION Industry 4.0 emphasizes automation and Information and Communication Technology (ICT) smart or intelligent systems (Schmidt et  al., 2015). This support is different from the operational work provided by e-procurement systems. The “smart” adjective in the procurement sector is more than the basis for the automation of end-to-end procurement processes. Procurement 4.0 solutions should automatically recognize the demand for a specific material or component, based on the customer requirements, the characteristics of the production processes, and the relevant historical data. These solutions can automatically generate a purchase order transmitted automatically to the partners. Another difference between procurement 4.0 and e-procurement is in-depth automation. Procurement 4.0 is a system without the requirement of human interventions, interruption of processing (STP – Straight Through Processing) (Khanna, 2010), and fully transparent and secure. Huang and Handfield (2015) made two crucial considerations: ERP systems can enable real-time information sharing and integration of business functions. The same set of functionalities can prove useful to organizations in the implementation of procurement 4.0 systems.

4.5.3 CONTROLLERSHIP The procurement 4.0 model implies a significant transformation in the way procurement works. It requires a substantial rethinking of the organization and competencies necessary across the full product or service cycle. Procurement 4.0 accelerates communication in a more tightly interconnected ecosystem. In the past, it was enough to be aware of some potential low-cost markets for procurement, for instance, Asia and Eastern Europe. Procurement 4.0 requires the control and organization of procurement with an integrated and global approach. For example, having the procurement organization nucleus located in the central office location was efficient and effective in the past. More procurement professionals should be close (physically or virtually) to competitive procurement markets for each category to control them properly.

4.5.4 COLLABORATION Procurement 4.0 revolutionizes procurement because its professionals no longer need to waste time in repetitive price inquiries and negotiations. They optimally collaborate with the other functions along with the value network. The optimal value, measured not only by price but also by quality, delivery, reputation, and so on, of the supply can be ensured throughout the product lifecycle. Procurement 4.0 solutions benefit not only procurement but also other functions, such as sales or controlling. The sales department receives a transparent basis, for example, to enforce legitimate price increases, and the controlling function can regularly compare the current supply prices with the optimal prices. The paradigm in terms of a greater “productive collaboration” in procurement 4.0 is different with respect to an e-procurement approach (Schuh et al., 2014). The productivity benefits resulting from e-procurement initiatives derive especially from

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a reduction in transaction and process costs (Essig, 2006). E-procurement allows the organization turning paper documents into digital documents processed by ICT applications. In this way, there is a change from a labor-intensive activity in the automated workflows and sustainable ICT processes. E-procurement supports critical activities like the process of managing relationships with partners (Essig, 2006). In procurement 4.0, these relationships are integrated and automated. The driving factors of procurement 4.0 collaborative productivity are improvements in terms of procurement, production, and engineering (Essig, 2006). Industry 4.0 enables the development of processes of production substantially shorter with respect to the past. The organization can activate new product-service functions and improve procurement (Schuh et al., 2014). E-procurement focuses on process efficiency. The objectives of procurement 4.0 are increased productivity, responsiveness, and performance to meet the rigorous requirements of customers and contribute to assuring their satisfaction and possibly delight (Kagermann, 2014).

4.5.5

CONNECTION

The handling operations contribute to the efficiency of the material/product flow in their movement along with the value network (Sreenivas, and Srinivas, 2006). It is critical to ensure full interoperability and flexibility. By optimizing the external transport and internal handling processes, it is possible to act on the structure of the procurement costs. Automatic operations, robots, and similar autonomous control solutions can solve some of the problems in the movements and transportation intra- and especially interorganizations in the value network (Tsorakis, 2018). To implement the automation of internal logistics, the organization must act in the different activities of the transportation processes. It is essential to implement an automatic path between work stations and warehouses. The organization needs to consider the legal and safety aspects, besides the technical aspects. Even partial automation of these pathways can impact on procurement or attributed to procurement costs. This automation can decrease the costs of operators and increases the overall safety of the workplace. Some companies are evaluating and testing the use of drones in procurement. For example, DHL is planning to use drones as emergency delivery means (Sreenivas, and Srinivas, 2008).

4.5.6

COGNITION

Big Data Analytics is an excellent enabler for procurement 4.0 (Koch et al., 2014). The intelligent use of advanced technologies and algorithms allows the aggregation, extraction, processing, and analysis of large volumes of data from many diverse sources. Using Big Data Analytics, organizations can improve their knowledge of the partners, markets, and customers; forecast market trends; and take actions to improve the deficiencies of processes and partners (Gupta et al., 2018). Big Data Analytics allows managers to take better and more informed decisions. In a large number of cases, Big Data Analytics can automatically take operational decisions regarding procurement (Nicoletti, 2014).

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Analysis of the data and their intelligent use is one of the CSFs for the organizations that want to exploit procurement 4.0. The Big Data Analytics methodologies and tools can support the organization and its partners in improving the design and performance of their components. The predictive analysis on where and when to expect the next faults allows the optimization of the maintenance and the availability of spare parts and maintenance materials (Lee et al., 2013). It is the responsibility of the organization, ensuring that Big Data Analytics maximizes the procurement opportunities connected with procurement 4.0. Procurement should work with partners to allow both the organization and the partners to benefit from the improvements resulting from a more effective, efficient, and economic data-driven ecosystem.

4.5.7

COORDINATION

A previous section dealt with the CSF collaboration. Collaboration implies working together to accomplish an activity and solve severe problems. Coordination is the balanced adjustment or interaction of different people or things to achieve an objective. Industry 4.0 implies that there must be close coordination, internal and in the ecosystem, on procurement processes, systems, and automation. Partners with the e-procurement systems have provided considerable support for the exchange of information with the customers and the other partners in the e-procurement applications (Kollmann, 2011). Innovative solutions in the procurement 4.0 initiative vastly increase the potential of such coordination. The critical change is the transition from an “exchange of information” to the “free flow of information” in the value network among the products, services, partners, and related organizations (Schlick et  al., 2014). This exchange must be coordinated effectively (Van Weele, 2010). When there is a free flow of information, there must be a large degree of exchangeability of the data, a higher degree of automation in the exchange of information, and possibly also an integrated use of the data in the approach of Big Data Analytics (Lee et al., 2013) or, in the future, on the use of blockchain, as described later in this chapter. Procurement 4.0 improvements on respect to e-procurement can be classified as (Glas and Kleemann, 2016): • Functional and cross-organization integration; • Reduction in procurement activities thanks to the use of ICT applications, automation, and their coordination. A flexible and coordinated procurement approach based on the digitization and automation of processes and infrastructure can provide several benefits. In procurement 4.0, both the depth of integration (especially among organizations) and the automation potential of the procurement processes are much more than those based on e-procurement. The latter is limited to facilitate the activities using digital applications based on information and documents. The automation process and a higher degree of integration characterize procurement 4.0. The base of procurement 4.0 is the digitization and advanced automation of the partners within the ecosystem. It is not limited to the use of new, improved but stand-alone ICT solutions.

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Besides the extent of integration, relations with partners may also differ in procurement 4.0 (for example, around the procurement of new goods and services (Essig, 2006)). The impact of procurement 4.0 in the organizations is broad and pervasive. All these components of the model substantially challenge and transform the workings of procurement. They require a critical and holistic re-engineering of the organization and competencies, both of which should change in a coordinated way (Geissbauer et al., 2016). Organizations need to create new professional profiles, for example, buyers of new categories of products, contract experts on intellectual property, or data scientists to analyze relevant data, their management, and their use. To find these talents is necessary to use new sources with the help of partners. There is a need for new partnership programs with universities and research centers. It is also useful to explore new channels such as social networks, social media, and similar. Large organizations should consider establishing relationships with academia to conduct educational and informational activities, explore new ideas, set up cross-functional training, and workshops, also for the partners. Only if all professionals who work in the procurement ecosystem have digital competencies (also named e-competencies (Sternberg et  al., 2003)), an organization can fully benefit from the opportunities offered by the digitization (Sanz et al., 2018).

4.5.8 CONFIDENCE Confidence (or trust or intimacy) plays a vital role in the organization’s ecosystem. Within traditional buyer–partner relationships, trust is an influential factor based on the (personal) interactions between them. It is at the basis of procurement 4.0 thanks to the directly interconnected and automated procurement 4.0 ecosystem as well as external influencing factors (Keith et al., 2016). In the value network ecosystems, trust must be maintained mutually among all the stakeholders more than only between two parties. Trust is a challenge for organizations with respect to their traditional behavior. It requires a change in the organizational culture (Harshak et al., 2013) and the solutions used.

4.6

CRITICAL SUCCESS FACTORS AND PROCUREMENT CYCLE

It is useful to relate the 8 Cs to the steps in the procurement cycle. This aspect can clarify the CSFs and evaluate better how it can support the procurement processes. Figure  4.1 shows which are the main CSFs for each of the procurement 4.0 subprocesses. All of them are important for each subprocess. Some factors are more important than others.

4.7

SUPPORTING SOLUTIONS

Figure 4.2 shows a graphical representation of the business model for procurement 4.0. With respect to each of the factors, Figure 4.2 also lists the enabling solutions to leverage procurement procedures, processes, and digitization supporting each component. It is not necessary to comply all the factors and the solutions examined in this chapter, but at least a certain number of them.

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FIGURE 4.1 Procurement processes and CSFs.

FIGURE  4.2 CSFs model for procurement 4.0 and the supporting tools (Adapted from Nicoletti, 2020).

Robotic Process Automation (RPA) can support Cybernetics (Van der Aalst et al., 2018). RPA is the automation of simple and repetitive activities, which need no longer be carried directly by the procurement team. It is implemented with both physical and virtual robots. The Connection can be implemented cheaply and effectively via the cloud, mainly the Internet. This solution allows to access from everywhere and any device, the applications that manage industry 4.0 and in particular procurement 4.0 (Nicoletti, 2013). It allows cheaply connecting partners even if small and not with many interactions.

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Computer numerical controls (CNC) support Controllership. CNC is a method for automating control of machine tools through the use of software embedded in a microcomputer attached to the tool (Radhakrishnan, 2014). It is commonly used in manufacturing for machining metal and plastic parts. In procurement 4.0, this tool is extended to the entire production cycle or at least its principal steps. CNC can automatically provide information to procurement and partners on their needs of sourcing. Collaboration has its basis on smart systems. They are applications and methods that can mimic human muscular and nervous systems. Smart systems collate leading technologies and solutions for the design of new generation embedded and cyber-physical systems (Crepaldi et al., 2014). They can be applied to a broad range of application domains, from everyday life to mission and safety critical activities. They can achieve a wide set of functionalities using diverse architectures. Thanks to them, collaboration can be automated and made more effective, efficient, and economic. Internet of Things (IoT) can support Connection (Manavalan and Jayakrishna, 2019). IoT is the interconnection via the internet or the intranet of computing devices enclosed in everyday objects, enabling them to send and receive data. IoT can be used in logistics, warehousing, and transportation. Assisted cognition systems mimic functions of the human brain in different ways, including natural language processing, data mining, and pattern recognition (Kautz et al., 2003). Big Data Analytics can support Cognition. Cognition robots (physical or virtual) can help the administrative work connected with procurement or the selection of potential partners to hire. Enterprise resource planning (ERP) is the business process for managing and integrating the relevant parts of the organization. ERP software applications are essential to organizations as they help implement resource planning by integrating all of the processes needed to run organizations with an integrated system using a consolidated data base. An ERP software system can integrate planning, sales, marketing, purchasing, inventory, production, maintenance, administration and finance, human resources, and more. ERP applications can help Coordination within an organization. When they are extended to include other partners in the organization ecosystem, they are labeled as Extended ERP applications (Plikynas, 2008). Cybersecurity solutions can help Confidence (Singer, and Friedman, 2014). Another emerging solution is blockchain, a computerized open ledger in which it is possible to record every type of transaction for a specific application (Harshak et al., 2013). The blockchain is available for all participants. If registered, a participant may also enter new data. They can see and check it. There is a log that allows standard visibility of operations and services. The automatic sharing of the data eliminates the need for data transfer between organizations. One can also imagine blockchain as “digital trust” (Harshak et al., 2013). This expression indicates that the blockchain is a set of data that can be considered reliable. Their correctness is based on the fact that a large number of actors have a consensus on them. From a technical point of view, a blockchain is a secure database. It is managed through a global network of independent servers. They provide a shared vision. Blockchain solutions are located in the cloud. So, they are easy to access from any location. Blockchain solutions help to eliminate all differences of data between partners and customers. They may

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provide the test according to where the materials originate, such as certified areas, environmentally and socially responsible. For example, blockchain solutions support the management of products and partner quality certificates, proofs of ownership, references of a specific partner, contracts, and purchase orders. They could help organizations quickly resolve delivery differences in the data end-to-end throughout the full process from request to payment.

4.8 APPLICATION OF THE MODEL Alfa is a multinational food business operating in the global markets, with annual sales of two billion euros (The name is fictitious due to a nondisclosure request) (Perona, 2019). For moving to procurement 4.0, this company planned to use the following solutions: • Cybernetics through RPA. It is the automation of simple and repetitive activities, which need no longer be carried out by the procurement team. An example of this activity is the verification of the completeness of the documents provided by the bidders in a tender. This solution can verify if the managers have approved the order and all the operational and administrative steps required are complete. • Controllership through integrated analytics (IA) allows to switch from the provision of elementary information and information based on the knowledge of the analysis of aggregated processes to the statistical inference and multivariate analysis. • Cognitive procurement processes optimize the implementation of responses of the procurement based on the context. Cognitive computing, for example, allows the interpretation and response to requests of the partners, made via emails, chats, or calls. The use of these solutions can bring benefits, such as the possibility of reducing the number of the procurement office staff of 41 full-time equivalents (FTEs) out of 112, corresponding to 37% of the total procurement workforce. There are other benefits regarding procured materials and services. They can decrease the procurement costs of these materials/services, delivery times and time reliability, quality of materials and services, and similar. The benefits can be analyzed based on the organization’s and process points of view and that of the processes. The procurement organization is composed of the following: • The front office takes care of the relationships with the internal functions and the external partners; • The middle office is a go-between front office and back office. It helps the front office to respond to external requests and manage the relationships of the front office with the back office; • The back office takes care of the administrative activities and management of the documentation.

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The organizational benefits are relative to the back-office, middle-office, and frontoffice innovations: • The savings in the back office are up to 89%. Repetitive and standardized activities are a big part of this area. RPA can support them. • The front office needs creativity and interpersonal competencies. It has savings equal to 7% of the FTEs. • The middle office can get savings of 75% The improvement in the processes can be strategic, tactical, and transactional benefits. • At the transactional level, procurement 4.0 can generate savings of 12.2 FTE, equal to 90% of the current workforce in this sector. This level includes all routine and repetitive activities. They are well suited to the adoption of analytics tools; • At a tactical level, the savings are 15.7 FTE. They correspond to 36% of the total employment in this sector. This level takes a routine and sometimes reactive approach to procuring materials and supplies using quick quote and order processes to support the production operations. It aims to ensure that the organization has the right supply at the right price and right time; • At a strategic level, there are savings of 13.1 FTE equal to 24% of the total workforce in this sector. This level includes activities such as spend analysis, market research, vendor rating/selection, and relationship management.

4.9 CONCLUSIONS, PRACTICAL IMPLICATIONS, AND FUTURE RESEARCH This chapter presents an innovative and integrated set of CSFs for procurement 4.0 and of the tools which can support them. It is composed of eight CSFs that are synergetic among themselves: Cybernetics, Communication, Control, Collaboration, Connection, Cognition, Coordination, and Confidence. The scope of Procurement 4.0 is creating new additional value propositions, supporting old and new business needs, and moving to a data-driven organization based on Big Data Analytics with data across different functions and value chains (Strategy & PwC, 2016). Transformation into a data-driven, linked innovation needs to follow the eight factors described above. It takes years, since – in addition to digitization of processes – a culture change and empowerment of procurement are required. The early implementers of procurement 4.0 in several industries digitally transformed in the medium-long term their businesses by integrating all the listed factors into their internal and value network. The factors examined are the basis for the success of a procurement 4.0 initiative in a specific organizational context. The management of the procurement processes is undergoing structural changes. Procurement 4.0 is an essential model to realize and support industry 4.0. It goes beyond it in the creation of an effective, efficient, and economical data-driven

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procurement. This chapter also defined the solutions that can make procurement 4.0 real (Nicoletti, 2017). It is not easy to predict what the future reserves. Procurement 4.0 is a revolution with respect to e-procurement. For instance, blockchain solutions, as a digital consensus tool, and smart contracts, as self-executing contracts, are only some examples of future solutions. New solutions will surpass the imagination. Some managerial implications are focusing on procurement 4.0 to stimulate managerial capabilities for the entire organization, starting from procurement 4.0. The adoption of procurement 4.0 strategies can initiate and support the procurement (and not only) managers. They can get important capabilities in terms of collaborative relationships, operative know-how with the partners, and advanced solutions. At the same time, they can improve transparency and traceability along with the value network basing their decision-making and operations on sound data. Digitization can improve globalization and communications. Whereas it was once enough to know about specific supply markets such as Asia and Eastern Europe, procurement 4.0 requires a truly global organization. For example, having the core of the procurement organization housed at headquarters might have worked in the past. Looking ahead, more and more procurement team members will be active (physically or virtually) in the most competitive supply markets for each category (Nicoletti, 2017a). The intervals of time for moving from one industrial revolution to the following one have decreased over time. Procurement 5.0 is approaching. It will be a new way to innovate procurement. The expectation is that more and more the role of procurement will change substantially. It will move from an internal purchasing function to a data-driven controllership and coordination of the ecosystem. This study and related model furnish the CSFs to consider for the realization of the digital transformation to procurement 4.0. They will be necessary to implement also for procurement 5.0.

ABBREVIATIONS 3D BDBA CNC CSF FTE ICT LSCM RBA SCA STP UK

Three Dimensions Big Data and Business Analytics Computer Numerical Controls Critical Success Factors Full-Time Equivalent Information and Communication Technology Logistics and Supply Chain Management Robotic Process Automation Supply Chain Analytics Straight Through Processing United Kingdom

REFERENCES Bag, S., Wood, L. C., Mangla, S. K., & Luthra S. (2020). Procurement 4.0 and its implications on business process performance in a circular economy. Resources, Conservation and

Big Data in Procurement 4.0

61

Recycling, 152, 104502. https://doi.org/10.1016/j.resconrec.2019.104502. Accessed 20 October 2019. Batran, A., Erben, A., & Spell, F. (2017). Procurement 4.0: A Survival Guide in a Digital, Disruptive World. University of Chicago Press Economics Books, Chicago, IL. Bienhaus, F., & Haddud, A. (2018). Procurement 4.0: factors influencing the digitization of procurement and supply chains. Business Process Management Journal, 24(4), 965– 984. https://doi.org/10.1108/ BPMJ-06-2017-0139. Accessed 20 October 2019. Crepaldi, M., Grosso, M., Sassone, A., Gallinaro, S., Rinaudo, S., Poncino, M., … & Demarchi, D. (2014). A top-down constraint-driven methodology for smart system design. IEEE Circuits and Systems Magazine, 14(1), 37–57. Essig, M. (2006). Electronic procurement - konzeption und anwendung. In: Zentes, J. (ed.), Handbook Handel. Gabler, Wiesbaden, pp. 735–758. Frederico, G. F., Garza-Reyes, J. A., Anosike, A.,  & Kumar, V. (2019). Supply Chain 4.0: Concepts, maturity and research agenda. Supply Chain Management: A International Journal, 25, 262–282. Geissbauer, R., Weissbarth, R., & Wetzstein, J. (2016). Procurement 4.0: are you ready for the digital revolution? United States: Strategy & PwC. https://www.strategyand.pwc.com/ report/procurement-4-digital-revolution, Accessed 20 October 2019. Glas, A. H., & Kleemann, P. D. (2016). The impact of industry 4.0 on procurement and supply management: a conceptual and qualitative analysis. International Journal of Business and Management Invention, 6(5), 55–66. Gupta, S., Kar, A. K., Baabdullah, A., & Al-Khowaiter, W. A. (2018). Big data with cognitive computing: A review for the future. International Journal of Information Management, 42, 78–89. Harshak, A., Schmaus, B., & Dimitrova, D. (2013), Building a digital culture: how to meet the challenge of multichannel digitization. https://www.strategyand.pwc.com/media/file/Strategyand_Building-a-Digital-Culture.pdf. Accessed 17 November 2019. Henke, M. & Schulte, A.T. (2015), Einkauf und die 4. Industrielle Revolution. Beschaffung Aktuell, 62(3), 20–21. Huang, Y. Y., & Handfield, R. B. (2015). Measuring the benefits of ERP on supply management maturity model: A “big data” method. International Journal of Operations  & Production Management, 35, 2-25. doi:10.1108/ IJOPM-07-2013-0341. Kagermann, H. (2014). Chancen von Industrie 4.0 nutzen. In: Bauernhansl, T., ten Hompel, M.,  & Vogel-Heuser, B. (eds.), Industrie 4.0 in Produktion. Automatisierung und Logistik. Springer, Wiesbaden, pp. 603–614. Kautz, H., Etzioni, O., Fox, D., Weld, D., & Shastri, L. (2003). Foundations of assisted cognition systems. University of Washington, Computer Science Department, Technical Report. Keith, B., Vitasek, K., Manrodt, K., & Kling, J. (2016), Strategic Sourcing in the New Economy: Harnessing the Potential of Sourcing Business Models for Modern Procurement. Palgrave Macmillan, New York. Kerkhoff, G., Schäfer, D., et  al. (2017). Procurement 4.0: Digitales Management von Variantenvielfalt zur kontinuierlichen Best-Preis-Sicherung - Erfolgreiche Praxisbeispiele. Wiley VCH Verlag GmbH, Weinheim. Khanna, A. (2010). Straight Through Processing for the Financial Services. Academic Press. Cambridge, MA. Kiron, D. (2017). Lessons from becoming a data-driven organization. MIT Sloan Management Review, 58(2), 17. Klünder, T., Dörseln, J. N., & Steven, M. (2019). Procurement 4.0: How the digital disruption supports cost-reduction in procurement. Production, 29, e20180104. doi:10.1590 /0103-6513.20180104.

62

Big Data Analytics in Supply Chain Management

Koch, V., Kuge, S., Geissbauer, R., & Schrauf, S. (2014). Industry 4.0: Opportunities and challenges of the industrial internet. Strategy & PwC. Kollmann, T. (2011). E-Business: Grundlagen elektronischer Geschäftsprozesse in der Net Economy. Gabler, Wiesbaden. Kuziemski, M., & Misuraca, G. (2020). AI governance in the public sector: Three tales from the frontiers of automated decision-making in democratic settings. Telecommunications Policy, 44, 101976. Lee, J., Kao, H. A., & Yang, S. (2013), Service innovation and smart analytics for Industry 4.0 and big data environment, Procedia CIRP, 16, 3–8. Luo, H., Wang, K., Kong, X. T., Lu, S.,  & Qu, T. (2017). Synchronized production and logistics via ubiquitous computing technology. Robotics and Computer-Integrated Manufacturing, 45, 99–115. https://doi.org/10.1016/j.rcim.2016.01.008. Accessed 12 November 2019. 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. https://doi.org/10.1016/j.cie.2018.11.030. Accessed 20 October 2019. Mohr, S., & Khan, O. (2015). 3D printing and its disruptive impacts on supply chains of the future. Technology Innovation Management Review, 5(11), 20. Nicoletti, B. (2013). Cloud Computing & Financial Services. Palgrave-MacMillan, London. (also translated in Chinese). Nicoletti, B. (2014). Using Operational Analytics to Achieve a Digitized, Visible Supply Chain. Inbound Logistics. Nicoletti, B. (2017a). Agile Procurement. Volume I: Adding Value with Lean Processes. Springer International Publishing, London. ISBN 978-3-319-61082-5. Nicoletti, B. (2020). Procurement 4.0 and the Fourth Industrial Revolution. Springer International Publishing, London. Pellengahr, K., Schulte, A. T., Richard, J., & Berg, M. (2016). Pilot Study on Procurement 4.0. The Digitalization of Procurement, Frankfurt, Germany. Perona, M. (2019). Procurement 4.0, the check list for Digital Procurement. https:// www.digital4.biz/ procurement/ ufficio-acquisti-digitale-cose-quali-benefici-delprocurement-4-0/. Accessed 20 March 2019. Plikynas, D. (2008). Extended enterprise resource planning: conceptual approach using multiagent systems. In WSEAS International Conference. Proceedings. Mathematics and Computers in Science and Engineering (No. 10). World Scientific and Engineering Academy and Society. Radhakrishnan, P. (2014). Computer Numerical Control Machines and Computer Aided Manufacture. New Academic Science Ltd, London. Russom, P. (2011). Big data analytics. TDWI Best Practices Report, Fourth Quarter, 19(4), 1–34. Sanz, L. F., Gómez-Pérez, J.,  & Castillo-Martinez, A. (2018). Analysis of the european ICT competence frameworks. In: Ahuja, V.,  & Rathore, S. (eds.), Multidisciplinary Perspectives on Human Capital and Information Technology Professionals. IGI Global, Hershey, PA, pp. 225–245. Schlick, J., Stephan, P., Loskyll, M.,  & Lappe, D. (2014). Industrie 4.0 in der praktischen Anwendung. In: Bauernhansl, T., ten Hompel, M., & Vogel-Heuser, B. (eds.), Industrie 4.0 in Produktion, Automatisierung und Logistik. Springer, Wiesbaden, pp. 57–84. Schmidt, R., Möhring, M., Härting, R. C., Reichstein, C., Neumaier, P.,  & Jozinović, P. (2015). Industry 4.0 -potentials for creating smart products: empirical research results. In: The International Conference on Business Information Systems. Springer, Cham, Switzerland, pp. 16–27.

Big Data in Procurement 4.0

63

Schuh, G., Potente, T., Wesch-Potente, C., Weber, A. R., & Prote, J. P. (2014), Collaboration mechanisms to increase productivity in the Context of Industrie 4.0. In: Proceedings of the 19th Robust Manufacturing Conference, CIRP, Bremen, Germany, pp. 51–56. Schwab, K. (2017). The Fourth Industrial Revolution. Currency, London. Shrouf, F., Ordieres, J., & Miragliotta, G. (2014). 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: 2014 IEEE International Conference on Industrial Engineering and Engineering Management. IEEE, pp. 697–701. Singer, P. W., & Friedman, A. (2014). Cybersecurity: What Everyone Needs to Know. OUP, New York. Sreenivas, M., & Srinivas, T. (2008). The role of transportation in logistics chain. European Journal of Operational Research, 95, 1–10. Sternberg, R. J., Sternberg, R. J.,  & Grigorenko, E. L. (eds.) (2003). The Psychology of Abilities, Competencies, and Expertise. Cambridge University Press, Cambridge. Strategy  & PwC. https://www.pwc.nl/en/assets/documents/pwc-industrie-4-0.pdf. Accessed 17 November 2019. Tsolakis, N., Bechtsis, D.,  & Srai, J. S. (2018). Intelligent autonomous vehicles in digital supply chains: from conceptualization, to simulation modelling, to real-world operations. Business Process Management Journal. 25(3), 414–437. https://doi.org/10.1108/ BPMJ-11-2017-0330. Accessed 20 October 2019. Van der Aalst, W., Bichler, M., & Heinzl, A. (2018). Robotic process automation. Business & Information Systems Engineering, 60(4), 269–272. Van Weele, A. J. (2010). Purchasing  & Supply Chain Management: Analysis, Strategy, Planning and Practice. Cengage Learning EMEA, Andover. Vis, I. F. (2006). Survey of research in the design and control of automated guided vehicle systems. European Journal of Operational Research, 170(3), 677–709. Wang, G., Gunasekaran, A., Ngai, E. W., & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, 98–110. Williams, S. N., Armitage, C. J., Tampe, T.,  & Dienes, K. (2020). Public perceptions and experiences of social distancing and social isolation during the COVID-19 pandemic: A UK-based focus group study. medRxiv.

5

Recommendation Model Based on Expiry Date of Product Using Big Data Analytics Abhisekh Kumar Singh and Maheswari Raja School of Computer Science and Engineering, VIT Chennai

Azath Hussain School of Computing Science and Engineering, VIT Bhopal University

CONTENTS 5.1

Introduction..................................................................................................... 65 5.1.1 Statement and Objective......................................................................66 5.1.2 Literature Survey ................................................................................66 5.2 Product Recommendation System................................................................... 68 5.2.1 User’s Preferences/Choices ­ ................................................................. 69 5.2.2 Keyword Classification ....................................................................... 69 5.3 Implementation of Statistical Analysis for Products....................................... 69 5.3.1 One-Sided and Two-Sided T-Test of Data Sets.................................... 71 5.3.2 Linear Regression Model .................................................................... 72 5.3.3 Experimental Assessment ................................................................... 72 5.4 Effects of Recommendation System ............................................................... 72 5.4.1 Recommendation for Ratings and Reviews of the Customer of Products .......................................................................................... 75 5.4.2 Advantages of the Recommendation System...................................... 75 5.5 Conclusion....................................................................................................... 76 References ................................................................................................................ 76

5.1 INTRODUCTION Due to the expansion of the network, e-commerce has overgrown and has attracted a significant number of internet customers around the globe. In every e-commerce company, customers can search, compare, and choose the product they like and can also recommend the popular product for others. In e-commerce company 65

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(e.g., Amazon, eBay, and Flip kart), variety of products were selected and recommended for purchase by reading customer reviews and comments. This results in the accumulation of a large amount of data that can be processed and analyzed more effectively. Large amounts of information are available on the internet in the form of ratings, rankings, reviews, suggestions, and comments about the product. Nowadays, the recommendation of the product gets populated through different social media channels (Facebook, Twitter, Instagram, etc.) apart from an e-commerce site. Big data comprise large and complex datasets which is challenging to process using traditional hands-on database system tools. There are many concepts related to big data. Earlier, there were three properties such as volume, variety, and velocity; later, two more dimensions such as value and veracity were added. In this context, variety indicates to the range of the data, volume denotes the size of the data, velocity illustrates the speed at which data get generated in e-commerce organization, value refers the assessment dimension of the data, and veracity shows the reliability toward the customer’s feedback. The remainder of the paper is structured with a basic description of the proposed methodology, analysis of implementation, and result. The key contribution of the proposed system is to deliver specific and accurate endorsements, and to meet the recommendation system requirements in real time which influence the overall performance of the system. Without adequate data and in the absence of big data, a traditional recommendation system cannot do its job in an efficient way. A recommendation system is coined by considering a large number of user data, including past history of purchase, statistical analysis such as descriptive statistics means measure of data set consisting of mean, median, mode, and inferential statistics means data visualization such as correlation, regression, testing analysis, ratings, and user feedback for making appropriate and successful recommendations. Filtering technique such as content-based (CB) infers the information retrieval from product, user-based refers to recommendation based on user’s choice, and collaborative filtering (CF) signifies the people having the same taste regarding product; it all plays a vital role in the recommendation system as illustrated through the flowchart in Figure 5.1.

5.1.1

STATEMENT AND OBJECTIVE

The objective of this work is to provide product recommendations based on expiry date using CF and CB filtering. It also recommends the unsold product with any offer/discounts to balance the profit/loss between the seller and the customer. It targets to minimize the unsold expired products using data analytics estimating 60%– 70% of progress.

5.1.2

LITERATURE SURVEY

Table 5.1 shows the literature survey supporting the proposed work, which includes the complete analysis required for the development of the proposed system [1–5]. The inferences, drawback, and conclusion have been tabulated for various kinds of techniques ranging from Big Data, Data Mining, Review Analysis, Recommendation System, Data Quality, Data Processing through e-commerce, Social Media Platforms.

Recommendation Model Based on Expiry Date

FIGURE 5.1

Flowchart of product recommendation.

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TABLE 5.1 Literature Survey Title

Inference

Recommendation for the social network using Big Data analytics

Performed analyzation Sometimes the Discovering popular via popular friendship recommendation may users and mining in Social be wrong leading to recommending friends. network. wrong friendship. Introduced a priori Sometimes they are Suitable for products algorithm difficult to set up for showing repetitive running. purchase pattern. Hadoop framework Less efficient CF methods to measure the quality of the prediction.

Recommendation system for e-commerce sector Improved recommendation of a system with review analysis Data quality in the big data processing: issues solution and open problems Big data analytics using Hadoop framing

Drawback

Conclusion

Data quality

Less scalable

Analyze the solution in big data processing.

Hadoop framework

Very sensitive due to large data sets sometimes.

Used CB filtering, CF, and hybrid filtering for the recommendation system.

FIGURE 5.2 Product recommendation system.

5.2 PRODUCT RECOMMENDATION SYSTEM The ratings/reviews from the customer are processed using the standard statistical analysis such as linear regression, testing analysis, and their modes. The output of the statistical analysis is subjected to various filtering techniques like collaborative and CB filtering. Based on the outcome of these filtering methodologies, the appropriate recommendation is provided to the new user from the existing customer. The complete flow of the proposed product recommendation system is illustrated in Figure 5.2. Three types of recommendations were provided in the existing system based on classification such as CB filtering, CF, and hybrid filtering [6]. Using CB filtering product gets recommended based on the user’s likes and dislikes. In collaborative-based filtering, customer receives suggestions based on the same categories that are further classified into separate user-based sections [7]. The predicted performance in terms of CF relies on the rating of other similar products by the same customer in user-based systems, depending on the rating of the comparable product by various customers [8].

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FIGURE 5.3

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Attributes of the test data set.

The multiple attributes of the test data set are shown in Figure 5.3. In this method, the most frequently searched products of the customer are coined as keywords, and these keywords are used to display both the user’s preferences and quality of the products. The purpose of the proposed system is to calculate the customized rating of items for a user and, then, submit a personalized list of recommendations and recommend the most appropriate products to the user. Figure 5.4 illustrates the various modes in Statistics over a test data set.

5.2.1

USER’S PREFERENCES/CHOICES

In this module, the preference of active users and former users is formalized in their corresponding keyword sets. An active user of this method refers to a current user who needs advice. i. Active user preferences: Active user can set preferences by selecting and recording a grocery product of any category. It identifies the active user of the product based on the similarity of their preferences if the previous user and the active user have the same taste. ii. Preferences of a former customer: A previous customer can give preference to any product and share an assessment of the product.

5.2.2

KEYWORD CLASSIFICATION

After reviewing the product, each item has been categorized based on comments such as positive or negative. If the customer’s review comment is positive for the product, then it could be recommended through social media such as Instagram, Facebook, Twitter, and many other advertising links [9,10]. But in case if any particular product has received any negative feedback/comments, then to balance the profit/loss of that specific product, an affordable offers or discount could be offered to that product.

5.3

IMPLEMENTATION OF STATISTICAL ANALYSIS FOR PRODUCTS

The various implementation methodology of statistical analysis of the product was illustrated in this section. Figure 5.5 refers to the correlation analysis such as

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FIGURE 5.4 Illustration of various modes in statistics.

FIGURE 5.5

Correlation analysis.

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Pearson’s product-moment correlation for comparing rating and helpfulness of the customer based on correlation analysis. Here df$score leads to the rating from the customer, and df$HelpfullnessNumerator signifies the helpfulness factor of the product to the customer; the p-value is the population value of the overall data set whose value varies from −∞ to +∞. The proposed system ensures 95% confidence interval over the test data set.

5.3.1

ONE-SIDED AND TWO-SIDED T-TEST OF DATA SETS

Figure 5.6 signifies the helpfulness of the product through one-sided testing process of the collected data which helps to identify the overall mean of the test data. In the existing system, there are two types of T-Test available such as one-sided T-test and two-sided T-test [11,12]. Figure  5.7 shows the ratings of the product through one-sided testing process of the collected data, which helps to identify the overall, mean of the test data. Figure 5.8 signifies the helpfulness of the product through two-sided testing process of the collected data which helps to identify the overall mean of the test data (with one lakh data) ranging from −∞ to +∞. Figure 5.9 shows the ratings of the product through two-sided testing process of the collected data which helps to identify the overall mean of the test data −∞ to +∞.

FIGURE 5.6 T-test analysis with helpfulness.

FIGURE 5.7

T-test analyses with score.

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FIGURE 5.8 Two-sided T-test for helpfulness.

FIGURE 5.9 Two-sided T-test for score.

5.3.2 LINEAR REGRESSION MODEL A linear regression model is one form of the statistical analytical methodology which helps to determine the association existed between two variables such as dependent and independent variables [13,14]. Figure  5.10 signifies the outcome of a linear regression model for two independent variables such as product ID and rating which includes first quartile, median, minimum, and maximum value.

5.3.3 EXPERIMENTAL ASSESSMENT As experimental data, real company sales data in a company from January 2018 to November 2019 are considered with approximately 4,000, the number of food products holding approximately 5,00,000 customers, while the number of orders was nearly 1,00,00,000 and it was analyzed using the R-studio technology. Table 5.2 demonstrates about the principal component analysis (PCA) which converts linearly correlated variables into linearly uncorrelated variables. Precision, coverage, recall are a part of PCA which calculates a statistical value on the basis of data sets. The graphical representation of the PCA is shown in Figure 5.11.

5.4

EFFECTS OF RECOMMENDATION SYSTEM

CF and CB systems are two typical recommendation engines used in the existing system. CF systems recommend goods by contrasting user preferences to other specific users. CF multiples the dimension of the data in terms with peers that are highly

Recommendation Model Based on Expiry Date

FIGURE 5.10

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Outcome of linear regression model.

TABLE 5.2 PCA-Result Analysis Recommendation Baseline user-CF User-based CF of [3] User-based CF (2) Item-based CF (2) Associate prod (3) (1)+(2) (1)+(3) (2)+(3) (1)+(2)+(3)

Precision (%)

Recall (%)

6.67 8.18 6.75 9.55 14.44 10.71 11.48 9.61 12.23

4.38 4.31 4.57 4.33 8.06 5.2 9.93 8.07 10.96

Coverage (%) 66 66 77 64 66 58 66 65 66

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FIGURE 5.11 Graphical representation of PCA.

TABLE 5.3 Comparison Between User Ratings and Review Score Product Id

User Id

B006K2ZZ7K B00813GRG4 B000LQOCH0 B000UA0QIQ B006K2ZZ7K B006K2ZZ7K B006K2ZZ7K

A3SGXH7AUHU8GW A1D87F6ZCVE5NK ABXLMWJIXXAIN A395BORC6FGVXV A1UQRSCLF8GW1T A1SP2KVKFXXRU1 A1IDNJ1Y42GMLS

Ratings 3 5 4 2 1 5 5

Review Score 4.46 3.69 0.16 0.07 4.49 1.19 3.25

correlated and then suggest the most popular items among peers to the customer. CB, on the other hand, recommends products based on the characteristics of the service instead of the purchase history or reviews of either user [15]. CB system aims to balance the user profile with the features of the product and suggest products that are strictly compatible with the user profile. Thus, the data collected for product recommendation through various analysis and filtering calculate a recommendation value over customeri and customerj which is indicated as rij in Equation (5.1) and peer-to-peer sale (i.e., recommendation by point-user similarity) is denoted by Sij in Equation (5.2).

∑ +  ∑

n

rij =   x j

slj x

l =1 p

(5.1) sij

l =1

Recommendation Model Based on Expiry Date

Slj =  ( X l − xl ) ( X j − X j ) ÷ X l − X j ⋅ X j − X j

75

(5.2)

Generally, customers prefer recommended product based on ratings. The comparison between user rating and review score is tabulated in Table 5.3.

5.4.1

RECOMMENDATION FOR RATINGS AND REVIEWS OF THE CUSTOMER OF PRODUCTS

Customer satisfaction rating is often a significant indication of the success of the customer management program of the product. Generally, the customer satisfaction rating is evaluated on a five-point scale (1 being “poor” and 5 being “happy”) in all the following figures. The graphical analysis of recommendation for customers rating is shown in Figure 5.12. A recommendation system or engine is a subclass of knowledge filtering system which seeks to predict the “rating” or “preference” of a product. To customize the recommendations for the new users, the review and rating from the existing customer are made mandatory. Figure  5.13 shows the graphical representation of the user’s recommendation based on studies. After reviewing the product through different ways of statistical analysis, if the product is close to the expiry date, the proposed methodology recommends a product with an offer/discount to balance the profit/loss between the seller and the customer.

5.4.2

ADVANTAGES OF THE RECOMMENDATION SYSTEM

• Personalized recommendation: The system will take current user preferences and generates recommendations on that basis. Each user shall be provided with a separate list of items according to their requirements.

FIGURE 5.12 Recommendation for ratings.

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FIGURE 5.13 Recommendation for customers.

• Calculation time reduction: With the help of Hadoop and map-reducing algorithm, the system becomes more efficient in terms of calculating time than the existing system.

5.5 CONCLUSION This chapter deals with the product recommendation scheme, depending on its expiry date through different methods such as CB or CF technique which is further split into product-based and user-based approaches. Thus, the final product which is advertised in the social media through different recommendation engine is targeted to the suitable customer, which will be well-enforced through different types of platform such as modes of statistics, testing analysis, and regression model followed by ratings and reviews of the customer. This recommendation system provides a substantial increase in a sale, which minimize the unsold expired products using data analytics estimating 60%–70% of progress.

REFERENCES 1. S.S.R. Abidi, and J. Ong, 2000, Automated data clustering based on a synergy between self-organizing neural networks and k-means clustering techniques, Proceedings of IEEE TENCON, Kuala Lumpur, pp. 568–573. 2. G. Adomavicius, and A. Tuzhilin, 2001, Using data mining methods to build customer profiles, IEEE Computer, 34, pp. 74–82. 3. J.L. Herlocker, J.A. Konstan, and J. Riedl, 2000, Explaining collaborative filtering recommendations, Proceedings of the ACM 2000 Conference on Computer Supported Cooperative Work, Philadelphia, pp. 241–250. 4. B. Mobasher, H. Dai, T. Luo, and M. Nakagawa, 2002, Discovery and evaluation of aggregate usage profiles for web personalization, Data Mining and Knowledge Discovery, 6, pp. 61–82.

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5. P.S. Yu, 2002, Data mining and personalization technologies, Proceedings of the Sixth International Conference on Database Systems for Advanced Applications, Hsinchu, Taiwan, pp. 6–13. 6. T. Mingdong, Z. Tingting, L. Jianxun, C. Jinjun, 2018, Cloud service QoS prediction via exploiting collaborative filtering and location-based data smoothing, Concurrency and Computation-Practice & Experience, 27(18), pp. 5826–5839. 7. P. Nikolaos, and P.-B. Andrei, 2017, Adaptive sentiment-aware one-class collaborative filtering, Expert Systems with Applications, 43, pp. 23–41. 8. T. George, and S. Merugu, 202005 , A scalable collaborative filtering framework based on co-clustering, International Conference on Data Mining (ICDM), Houston, TX, USA, pp. 625–628. 9. N.M. Khanian, and M. MohdNaz’ri, 2019, A systematic literature review on the state of research and practice of collaborative filtering technique and implicit feedback, Artificial Intelligence Review, 45(2), pp. 167–201. 10. T. Steven, 2016, The economics of reputation and feedback systems in E-commerce marketplaces, IEEE Internet Computing, 20(1), pp. 12–19. 11. Y. Dong, S. Liu, and J.C. Chai, 2016, Research of hybrid collaborative filtering algorithm based on news recommendation, 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, Datong, China pp. 898–902. 12. C. Langcai, L. Zhihui, and L. Yuanfang, 2017, Research of text clustering based on improved VSM by TF under the framework of Mahout, Proceedings of the 29th Chinese Control and Decision Conference CCDC 2017, Chongqing, China, pp. 6597–6600. 13. G. Shani, A. Gunawardana, F. Ricci, L. Rokach, B. Shapira, and P. Kantor, 2011, Evaluating Recommendation Systems in Recommender Systems Handbook, Boston, MA: Springer. 14. Y. Fan, Y. Shen, and J. Mai, 2008, Study of the model of e-commerce personalized recommendation system based on data mining, International Symposium on Electronic Commerce and Security, Guangzhou, China, pp. 647–651, 3–5 August. 15. A.A.C.G. Karuna, and C. Gull, 2014, A clustering technique to rise up the marketing tactics by looking out the key users taking Facebook as a case study, IEEE International Advance Computing Conference, Gurgaon, India, pp. 579–585.

6

Comparing Company’s Performance to Its Peers A Data Envelopment Approach Tihana Škrinjaric´ University of Zagreb

CONTENTS 6.1 6.2 6.3

Introduction .................................................................................................... 79 Previous Related Research ............................................................................. 81 Methodology Description ............................................................................... 83 6.3.1 Slacks-Based Measure of Efficiency .................................................. 83 6.3.2 Multiple Criteria Decision-Making .................................................... 85 6.4 Empirical Results............................................................................................ 86 6.4.1 Data Description and Preprocessing................................................... 86 6.4.2 Main DEA Results .............................................................................. 91 6.4.3 Discussion on the Best and Worst Ranked Companies ...................... 93 6.4.4 Robustness Checking – MCDM ......................................................... 95 6.4.5 Further Possible Integrations of DEA and MCDM ............................96 6.5 Conclusion ......................................................................................................97 Appendix ..................................................................................................................99 References .............................................................................................................. 103

6.1

INTRODUCTION

A company needs to re-evaluate its performance and constantly compare itself to others (Soboleva et al. 2018). This is due to the pressure continuously rising in all business industries, constant changes in the market, demand, technology, sustainability demands, and other factors influencing the business itself. Thus, it is important for a company to know where it stands compared to others. In order to do this, an objective approach needs to be made, where important and relevant variables and factors are taken into consideration. The management cannot make good decisions for future business if the meaningful analysis is not made (Narkunienė and Ulbinaitė 2018). Concept of business performance is now commonly used not only within academic literature but in circles of professional managers (Yildiz and Karaka 2012). Furthermore, adequate mathematical modelling should be applied and used in order 79

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to obtain objective results. It is easily seen how the whole process of obtaining such results needs cooperation and interaction between the firm’s management, quantitative modellers, and financial experts as well. As Matthias et al. (2017:41) state that analysis, which is not critical and done on poorly understood data, cannot generate new knowledge. Such analysis has become a necessity (Šarlija and Jeger 2011). On the other hand, such comparisons can be made from the (potential) investor’s side, which is looking at investment possibilities for their portfolios. Dynamic changes in the stock markets and in the businesses themselves force the investors to constantly re-evaluate the financial assets and companies in their portfolios. Again, an objective approach of comparisons and ranking system are needed, which can facilitate investment decision-making. Furthermore, business banks also have benefits in using such data when making decisions on approving new loans and constructing credit scoring (Roje 2005; Chan-Lau 2006; Demerjian 2007). Recent decades have experienced the development of many different models, methods, and techniques within mathematics, statistics, and econometrics in order to answer specific questions. Some classifications of different approaches were made in Granger (1989), Ho et al. (2002), Taylor and Allen (1992), and Wallis (2011). On the other hand, evaluating business performance has become more complex, due to many different aspects of the business itself which need to be taken into consideration. The financial ratios are often used in order to compare businesses one to another (Yalcin et al. 2012; Neely et al. 1995; Marshall et al. 1999; Najmi and Kehoe 2001), as they give insights into the profitability, productivity, liquidity, and other relevant aspects of the business. Due to many financial ratios that need to be evaluated for many companies, such problems lie within big data analytics. Big data can be very useful in the decision-making process (Li et al. 2016; Matthias et al. 2017), but the complexity of analyzing so many financial ratios can be seen in Beaver (2010), Myšková and Hájek (2017), or Laitinen (2018). One of the popular approaches includes some of the models from Data Envelopment Analysis (DEA) (Feroz et al. 2003; Kohers et al. 2000; Cummins et al. 2000; Yu et al. 2013), as a branch of Operations Research (OR), a set of mathematical models, and methods which are used to evaluate relative efficiency of alternatives which are being compared. This is not a new approach in comparing the businesses one to another. However, previous related research mostly focuses on basic models (e.g., Charnes-Cooper-Rhodes and Banker-Charnes-Cooper models), which have several drawbacks. Furthermore, research often compares a relatively smaller number of firms and even starts with a small number of financial ratios in the analysis. That is why this research focuses on a bigger sample with respect to the number of firms and financial ratios, in order to reflect real-life problems in such analysis. Furthermore, this research employs the SBM (slacks-based measure) model within DEA methodology, as this approach has several advantages compared to basic models. The SBM model does not depend on the data translation, measures of each variable, and is nonradial (for details, please see the methodology section). Thus, it could provide better results in terms of reliability. Another contribution of this research is that robustness checking of the results is conducted. This is often ignored in the literature. The robustness testing will be performed by another methodological approach, multiple criteria decision-making (MCDM) model. This model belongs to another branch of OR, which is used in constructing a ranking system

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of different alternatives based on several criteria. Thus, the main goals of this research include the following. First, a comprehensive literature overview will be given, in order to obtain as many insights as possible. Second, a detailed empirical analysis will be provided so that all those interested could make similar research in the future, with straightforward interpretations. Third, the mentioned robustness checking will be performed, so that the results obtain greater reliability. The rest of this research is structured as follows. The second section deals with the literature overview which is most related to this study. The methodology used in this study is described in the third section. The fourth section deals with the empirical analysis, while the final, fifth section concludes the research with recommendations for future research.

6.2

PREVIOUS RELATED RESEARCH

By observing the existing literature which examines the financial ratios in business comparisons, it can be seen that probably all of the ratios have been used in empirical analysis at some point. Commonly used methodologies include econometric techniques (regression analysis, time series analysis methods, and models) (e.g., Penman et al. 2007; Jordan et al. 2007; Dempsey 2010) in which the stock returns are modelled and forecasted based on financial ratios; nonparametric approaches such as the DEA (Zamani et al. 2014; Škrinjarić 2014), Gray Relational Analysis (GRA, Fang-Ming and Wang-Ching 2010; Huang et al. 2015; Škrinjarić and Šego 2019), and Analytic Hierarchy Process (AHP, Li et al. 2010) in which the ranking systems are made based on the financial ratios criteria. Earlier work includes testing the Efficient Market Hypothesis with book-to-market ratio as a proxy for the value premium of stock returns (Fama and French 1992, 1993). The focus was made on more developed markets: Japan in Chan et al. (1991), USA in Kothari and Shanken (1997), and a set of developed countries (UK, USA, Belgium, Germany, France, etc.) in Fama and French (1998). Another popular financial ratio is the dividend yield, which has its roots in Lintner (1956), Brennan (1970), and Litzenberger and Ramaswamy (1979). Today, different dividend policies are being considered in theory and empirical applications (stable dividend policy, Leary and Michaely 2011; residual policy, Baker and Smith 2006, etc.). Since the early work of Basu (1977, 1983), the P/ E (price-to-earnings) ratio has been included in the asset pricing models as it increases the forecasting power of such models (see Noda et al. 2015; Alcock et al. 2011). Earnings per share (EPS), return on assets (ROA), liquidity indicators, and receivable turnover ratio have been found as the most useful indicators about the business in Wu (2000). ROA has been in focus in Muhammad and Scrimgeour (2014), as it is a proxy for firm’s performance and management’s efficiency to generate profits from assets, whereas others (Jablonsky and Barsky 2001) argue that ROIC (return on invested capital) is a better measure compared to ROA and ROE, due to ROIC being calculated based on value above the average cost company pays for its equity capital and debt. The econometric approach of estimation has been very popular over the decades. Some of the recent research includes Lee and Lee (2008), Dempsey (2010), Gregoriou et  al. (2017), etc. Other approaches include chi-square test and ratio analysis (Damjihabi 2016), or factor

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analysis (Hornungova  and František 2016). Banking systems have also been often observed in the literature as well (see Maradin et al. 2018). However, the approaches in such research are not linked closely to this one. The second group of research utilizes similar approaches. Thus, the focus will be made more on them. The DEA and related approaches are found in the following papers. Powers and McMullen (2002) have focused on 185 American and British stocks in evaluating their efficiency by including EPS, market betas (capital asset pricing model), standard deviation, and other risk and return indicators. 230 American firms have been examined in Edirisinghe and Zhang (2007), where authors developed a generalized DEA indicator (RFSI – relative financial strength indicator). A simulation was included in the study, where authors compared trading strategies based on including the RFSI variable in the consideration or not. Some of the papers which focus mostly on creating trading strategies from the investor’s point of view include Chen (2008), who has observed three years of data (2004–2007) on the Taiwanese market; Lopes et al. 2008), who have examined the Brazilian stocks; Lim et al. (2013), who have used a large amount of market and financial ratios data in their analysis; Zamani et  al. (2014) have focused on the Mumbai stocks; Gardijan and Škrinjarić (2015), who have examined the Croatian stock market, etc. However, such research uses stock market characteristics, financial ratios, or combination of both. Such analysis does not provide some answers into the (in)efficiencies of businesses; its purpose is to provide rankings of stocks based on some criteria which is important for the investor so that he can re-evaluate his portfolio over time and rebalance it accordingly. Emrouznejad and Cabanda (2010) examined the General Non-Parametric Corporate Performance via DEA model on a sample of 27 UK industries and 6 performance ratios. This research was more technical (comparing methodological approaches and changes) and did not focus on questions as this research does. A combination of DEA model with other methodologies is found in Rosini and Gunawan (2018) where authors combined DEA with TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution); Ding and Sickles (2018) where authors combined DEA, SFA (stochastic frontier analysis), and panel two-step GMM (generalized method of moments) on US banks; Fang-Ming and Wang-Ching (2010) where the DEA was compared to GSD (Grey Systems Decision); Huang et al. (2015) combined a two-level DEA and the GRA approaches, etc. Oberholzer (2012) compared 55 manufacturing companies on Johannesburg Stock Exchange with several input and output variables within the DEA model (sales, dividend payouts, tangible assets, etc.) and found usefulness in such models to detect relative (in)efficiencies when comparing firms. Demerjian (2017) is more focused on methodological aspects of using DEA models in financial analysis. This research is extensive but provides detailed insights into the robustness of such models in particular applications. Lin et al. (2010) focused on the shipping industry combined DEA with ABC (activity-based costing) methodologies. The sample included only 14 firms with 6 variables. Siew et al. (2017) examined financial companies in Malaysia (for period 2010–2015), with basic calculations and interpretations (ranking of the companies, comments on the efficiency, and input and output weights in the optimization process). Malaysian stocks have been examined in Arsad et al. (2018) as well, where SFA and DEA results were compared for 115 companies in 2015. The comparison

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83

showed that rankings differ based on the chosen methodology, but no explanations were given on why. A lot of researchers do not state why they do or not include specific financial ratios and other variables in the analysis. The majority of research uses yearly data due to the nature of the variables, as companies release balance sheets and other relevant financial statements on a quarterly and yearly basis. Furthermore, a lot of research utilizes basic DEA models, which forces the researchers to use those variables which are suitable for such models (due to the model assumptions). Thus, the contribution of this research includes explanations and rationale on why some ratios are used in the empirical part or not, with extensions of basic models so that the model is closer to reality.

6.3 6.3.1

METHODOLOGY DESCRIPTION SLACKS-BASED MEASURE OF EFFICIENCY

DEA is a set of models and methods of mathematical programming, which enables comparisons of the relative efficiency of the Decision-Making Units (DMUs). DEA is a specific part of OR which focuses on ranking the DMUs based on a set of criteria, where it is assumed that each DMU produces outputs based on production inputs. The terminology used within this area comes from DEA being developed for production firms. The term inputs refers to the variables which a DMU is aiming to reduce, while the term outputs refers to the variables which should be greatest possible. Furthermore, the term relative efficiency denotes the efficiency of one DMU compared to others. Thus, this methodology compares a set of DMUs one to another, not to the best possible solution determined by the researcher, industry, etc. For a basic introduction to the DEA terminology, ideas, and models, interested readers are referred to Cooper et al. (2011), Sexton (1986), Doyle and Green (1994), or Sickles and Zelenyuk (2019). The basic model notation is as follows. The DMU uses m inputs (x1j,x2j,…,xmj) to produce s outputs ( y1j,y2j,…,ysj), where j denotes the DMU, j ∈ {1, 2,…, n}. Inputs and outputs in this study refer to the financial ratios which are used for comparison purposes. If the data are put in matrix form, matrices X and Y consist of all inputs and outputs, respectively: ⎛ x11 ⎜ x21 X =⎜ ⎜  ⎜ x ⎝ m1

x12 x22  xm 2

   

x1n x2n  xmn

⎞ ⎛ y11 ⎟ ⎜ ⎟ and Y = ⎜ y21 ⎟ ⎜  ⎟ ⎜ y ⎠ ⎝ s1

y12 y22  ys 2

   

y1n y2n  ysn

⎞ ⎟ ⎟ . (6.1) ⎟ ⎟ ⎠

Each column in matrices X and Y consists of data for the j-th DMU, xo = ( x1o , x 2o , , x mo )′ and yo = ( y1o , y2o , , yso )′, where xo ≥ 0, xo ≠ 0 and yo ≥ 0, yo ≠ 0 holds. The two most basic models which were developed (and mostly used today) are the Charnes-Cooper-Rhodes and the Banker-Charnes-Cooper model. The main difference between them is the assumption of returns to scale in the production

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(constant versus variable returns to scale). However, these two models suffer from several disadvantages. They are sensitive to data translation. As the data have to be nonnegative, depending on whether this refers to inputs or outputs, some variants of these models cannot be used. Furthermore, both models assume an equal proportion of input decrease and/or output increase of a selected DMU in order to get to the efficient frontier. That is why many different models and extensions have been developed over the years. The SBM (slacks-based measure) model (Tone 1997, 2001) has a certain advantage compared to the two mentioned models, which is being an additive model, with units-invariance property, alongside the possibility of being nonoriented and nonradial. This means that the optimization process consists of simultaneously decreasing inputs and increasing outputs, without forcing the same rate of improvements for inputs and outputs. This is especially useful when ratios are used as inputs and/or inputs, which is often the case in analyzing the financial performance of companies. The model is dimensionally free in that way (Thrall 1996). In order to measure the (in)efficiency of each DMU, the following model is optimized:

min ρ =

λ ,s− ,s+

1 1− m 1 1+ s

m

− i

∑ xs i=1 s

∑ i=1

io

+ r

(6.2)

s yro

s.t. xo = X λ + s− yo = Y λ − s +

λ ≥ 0, s− ≥ 0, s+ ≥ 0 where s– and s+ are vectors of input and output slacks, respectively. A DMU is called SBM-efficient if and only if ρ * = 1. This is equivalent to s– = 0 and s+ = 0, which means that no input excess and no output shortfalls are present in the optimal solution. The SBM projection to the efficient frontier for a DMU under consideration is xˆ o ⇐ xo − s*− yˆ o ⇐ yo + s*+

(6.3)

Thus, the main idea in this research is that when comparing the performances of companies one to another, the DEA methodology can be very useful due to it comparing the efficiency in “producing” outputs by using the smallest amount of inputs. Financial ratios that should be the smallest possible will be observed as inputs, while the opposite is true for the output variables. Often the problem with real data is missing data. The values in input and output vectors should not be missing in order for the DEA model to be optimized.

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85

One approach is to delete those DMUs from the analysis which have missing data. However, this could result in a small sample in the end which cannot be used for the analysis. Another approach is to add penalties to missing data. Kuosmanen (2009) advises this. Missing input values should be set to a large value, greater than the maximal input value for the existing data. The missing values for output values should be given such that these values are smaller compared to the lowest output value which is available in the sample. As Kuosmanen (2009) showed, the optimal values in such an approach are as good as the approach of deleting the DMUs with missing data. Finally, correlations between inputs and outputs are important in the analysis as well. The main idea is that the correlation between inputs and outputs is as greatest possible, while the correlations between inputs (outputs) themselves should be lowest possible (due to containing the same information if the correlation is great). Thus, in order to reduce the unnecessary number of inputs and/or outputs in the analysis, the correlation matrices will be observed. For more information on the correlations within DEA analysis, please see López et al. (2016).

6.3.2

MULTIPLE CRITERIA DECISION-MAKING

In order to test the robustness of the results, another approach will be made in order to obtain rankings of the companies in the empirical part of the analysis. MCDM is also a part of the OR, in which optimization of mathematical models is done on a set of different alternatives based on different criteria. As many economic and financial decisions are often based on conflicting criteria, the MCDM modelling aids in constructing the ranking system of the alternatives which are taken into consideration. Here, the alternatives are the DMUs from the previous subsection terminology and approach, and the criteria are the inputs and outputs from the DEA terminology. Details on the MCDM methodology with applications in finance can be found in Hurson and Zopounidis (1995). The main flow of this approach consists of the following. The problem of the analysis, with objectives and decisions, has to be made, by identifying the alternatives which are compared one to another. Then, the mathematical model is formulated and needs to be solved. The solution of the model has to be observed by the decision-maker, and (s)he needs to make the final decision, based on the topic observed, or redefine some of the steps of the whole process. For more details, please see Bigaret et  al. (2017). As a simple model, this research utilizes the MOORA model (multiobjective optimization by ratio analysis). As Brauers and Zavadskas (2010) showed, this approach is robust with respect to seven criteria analyzed. First, this approach is more robust when more stakeholders are involved compared to one decision-maker (Brauers, 2007). Second, the noncorrelated multiobjective-based method is more robust compared to the limited number of objectives. Third, it is nonsubjective; fourth, if it is based on cardinal numbers, it is more robust when compared to ordinal numbers. Fifth, the method is robust in cases when all interrelations between objectives and alternatives are taken into consideration when compared to examination two-by-two (Brauers, 2004). Sixth, this method is more robust when using the newest data possible, and finally, when all of the previously mentioned conditions are met, the optimization which utilizes two methods compared to one is

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more robust, three methods are more robust compared to two, etc. For more details, please see Brauers and Ginevičius (2009). Every alternative j is observed with its criteria i. All of the data are put into matrix Xij = [xij], where i ∈ {1, 2,..., n}, j ∈ {1, 2,..., m}. Every value xij is put into the following ratio, in order to obtain the following normalization: xij* =

xij

(6.4)

m

∑x

2 ij

j=1

so that values in (6.4) fall in the range [0,1]. These normalized values are summed up if the criterion should be greatest possible or detracted if the main goal is to obtain minimized values of any criterion. In that way, the normalized assessment value is calculated as: g

y*j =

∑ i=1

n

xij* −

∑x

* ij

.

(6.5)

i = g+1

Each value y*j in (6.5) can be finally ranked. Each criterion in (6.5) has equal weights in calculating y*j . The researcher can give different weights to each criterion, based on previous knowledge, experience, etc. More details on this modelling approach in finance can be found in Baležentis et al. (2012) or Xidonas et al. (2009). Finally, in terms of the financial ratios observed in this study, the criteria which should be greatest possible (addition in (6.5)) are those ratios which should be greatest possible, while the opposite is true for those ratios which should be minimal possible.

6.4 6.4.1

EMPIRICAL RESULTS DATA DESCRIPTION AND PREPROCESSING

For the purpose of the empirical comparison, data on 320 stocks constituting NASDAQ Computer Components Index have been collected from Investing (2019). The data include financial ratios which are given in Table 6.1. Based on the financial theory and previous research, the ratios have been classified into inputs and outputs (last column). The full list of companies observed in the study is given in Table A1 in the Appendix. As can be seen in Table 6.1, if a company wants to compare its performance to others, there is a lot of data that need to be processed and taken into consideration. R software was used to perform the DEA and MOORA analyses.1 As this research uses DEA as the main methodological approach of modelling, due to the nature of the data, the following steps are made. First, the companies 1

The following packages were utilized: DJL, Benchmarking and MCDM, with all of the code provided in their respective documentation.

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TABLE 6.1 List of Financial Ratios Used in the Study with Description Full Name of Financial Ratio

Abbreviation

5-year capital spending growth 5YA 5-year EPS growth 5YA

CSG

5-year sales growth 5YA

S_G

Asset turnover TTM

AT

Basic EPS ANN

BEPS

Book value/share MRQ

BV/S

Cash flow/share TTM

CF/S

Cash/Share MRQ

C/S

Current ratio MRQ

CR

Diluted EPS ANN

DEPS

Dividend growth rate ANN Dividend yield 5-year avg, 5YA

DGR DY5Y

EPS_G

Description

DEA: I vs. O

Growth rate of company’s investing into capital to maintain and grow business. Growth rate of EPS over the 5-year horizon. Growth rate of company’s sales over the 5-year horizon. Ratio of company’s net sales revenue and the average total assets value. Higher value of this ratio means that company is generating more revenue per monetary unit of assets. Ratio of company’s net income reduced by preferred dividends and the number of shares outstanding. Ratio of difference between company’s total equity value and preferred equity and the number of shares outstanding. If a market share price is smaller compared to the BV/S, the company could be considered undervalued. Ratio of a company’s after-tax earnings increased by depreciation and number of shares outstanding. Greater values indicate company’s status to generate cash. Ratio of company’s total cash and number of shares outstanding. A great value of this ratio could indicate that a company is performing well but can also indicate a cost of capital inefficiency. Ratio of company’s current assets and liabilities. Best value is if the ratio is around 1:1. (See Tracy 2004.) Ratio of company’s net income reduced by preferred dividends and number of shares outstanding increased with the conversion of dilutive securities. Indicates the worst-case scenario in terms of EPS Growth rate of the dividend yield. Ratio of company’s annual dividend payment and the market capitalization; 5-year average.

O O O O

O

O

O

I

Unit value; O

O

O O

(Continued)

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TABLE 6.1 (Continued) List of Financial Ratios Used in the Study with Description Full Name of Financial Ratio

Abbreviation

Dividend yield ANN

DY

EPS(MRQ) vs. Qtr, 1 Yr, Ago MRQ EPS(TTM) vs. TTM 1 Yr, Ago TTM Gross margin 5YA Gross margin TTM

EPS_Q

Inventory turnover TTM

IT

LT debt to equity MRQ

LTD/E

Net income/employee TTM

NI/E

Net profit margin 5YA Net profit margin TTM

NPM5Y NPM

Operating margin 5YA Operating margin TTM

OM5Y OM

P/E ratio TTM

P/E

Payout ratio TTM

PR

Pre-tax margin 5YA Pre-tax margin TTM

PM5Y PM

Description

DEA: I vs. O

Ratio of company’s annual dividend payment and the market capitalization. Ratio of company’s net profit and number of common shares.

O

Ratio of difference of company’s revenue and cost of goods sold and revenues. The lower the ratio is, the less the company retains on each monetary unit of sales to service costs and debts. Ratio of company’s net sales and average inventory. Higher value means that company could have inadequate inventory levels (too low); lower value could mean that company is overstocking. Ratio of company’s long term debts and the total shareholders’ equity. Higher ratio means the company is more risky. Ratio of company’s net income and number of employees. Ratio of company’s net profit and revenues. Greater values indicate that greater percentages of revenues are turned into profits. Ratio of company’s operating earnings and revenues. Smaller values of this ratio indicate that company is less able to pay nonoperating costs. Ratio of a company’s share price to the company’s EPS. High P/E ratio indicates that the market perceives it as lower risk or higher growth or both when compared to a company with a low P/E ratio. Ratio of company’s dividends paid to shareholders and the net income. Greater value indicates that firm is more mature and it does not need to reinvest much more of the net income it earns. Ratio of company’s profits and sales before tax reduction. Greater value indicates better profitability.

O

O

EPS_TTM GM5Y GM

O

I

O O

O

O

O

O

(Continued)

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TABLE 6.1 (Continued) List of Financial Ratios Used in the Study with Description Full Name of Financial Ratio

Abbreviation

Price to book MRQ

P/B

Price to cash flow MRQ

PCF

Price to free cash flow TTM

PFCF

Price to sales TTM

P/S

Price to tangible book MRQ

P/TB

Quick ratio MRQ

QR

Receivable turnover TTM

RT

Return on assets 5YA Return on assets TTM

ROA5Y ROA

Return on equity 5YA Return on equity TTM

ROE5Y ROE

Return on investment 5YA Return on investment TTM

ROI5Y ROI

Description

DEA: I vs. O

Ratio of company’s market capitalization and company’s total book value. Empirical research has shown that low P/B stocks outperform high P/B stocks. Ratio of a company’s market capitalization with the company’s operating cash flow. The greater the value of this ratio, the lower the value of the stock is (firm is not generating enough cash flows). Ratio of a company’s market capitalization to company’s free cash flow. Higher value of this ratio indicates that the company could be overvalued; it cannot generate additional revenues. Ratio of a company’s market capitalization by revenues. The greater the ratio, the worse the investment is due to paying more for a more than each unit of sales. Ratio of company’s share price to tangible book value per share. Interpreted as amount of money investor (shareholder) would receive if the company would shut down and liquidate all assets. Lower value of this ratio indicates smaller possible share price losses. Ratio of company’s liquid assets and quick liabilities. Best value is if the ratio is around 1:1. (See Tracy 2004.) Ratio of company’s net credit sales and average accounts receivable. If a company is more effective in collecting its receivables, the ratio is bigger. Ratio of company’s net income and total assets. Smaller values mean that company is less able to generate income from the assets it uses. Ratio of company’s net income and equity. Smaller values mean that company is less able to generate income from the equity it uses. Ratio of company’s net income and cost of investment. Smaller values mean that company is less able to generate income from its investments.

I

I

I

I

I

Unit value; O

O

O

O

O

(Continued)

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TABLE 6.1 (Continued) List of Financial Ratios Used in the Study with Description Full Name of Financial Ratio

Abbreviation

Revenue/employee TTM

R/E

Revenue/share TTM

R/S

Sales (MRQ) vs. Qtr, 1 Yr, ago MRQ

S_Q

Sales (TTM) vs. TTM 1 Yr, ago TTM

S_TTM

Tangible book value/share MRQ

TBV/S

Total debt to equity MRQ

D/E

Description

DEA: I vs. O

Ratio of company’s total revenue and number of employees. Ratio of company’s total revenues and number of shares outstanding. Higher values indicate greater revenues. Growth rate of company’s sales, quarter of this year compared to same quarter of last year. Growth rate of company’s sales, trailing 12 months this year compared to same trailing 12 months of last year. Ratio of company’s total tangible assets and number of shares outstanding. Higher values indicate company has a lot in value regarding tangible assets. Ratio of company’s total liabilities and the total shareholders’ equity. Higher ratio means the company is more risky.

O O

O

O

O

I

Source: Investing (2019) Note: TTM, 5YA, and MRQ denote Trailing Twelve Months, 5-Year Average, and Most Recent Quarter, respectively. I and O denote input and O, respectively.

which had only a few data available were removed from the analysis. Thus, the initial sample of companies was reduced to 292. This is due to some companies having only a couple of data available, but the number of other ratios that were not available was too big. Maybe some newer companies do not have specific data available yet and introducing artificial values of inputs and outputs (penalties in the mentioned approach in the methodology section) could hurt its performance. Second, the Kuosmanen (2009) approach was applied to fill the gaps in data for the companies which had only a few missing data. Third, the correlation matrix between all inputs and outputs was estimated and is shown in the Appendix in Table A2. As DEA models do not need all of the financial ratios to compare one company to others, only those inputs and outputs were chosen in the end which filled the following criteria: inputs being correlated very low one to another, the same was observed for outputs, inputs and outputs which had the greatest correlations one to another, and those inputs and/or outputs were given a slight advantage if they were calculated based on the last 12 months and 5-year averages compared to those calculated in the last quarter. These ratios contain more information about the company compared to the last quarter. The final inputs and outputs used in the study are the following ones.

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Output variables consist of P/ E Ratio TTM, Return on Investment TTM, EPS (TTM) vs TTM 1 Year Ago, Asset Turnover TTM, Dividend Yield 5YA, whereas inputs include Price-to-Sales ratio TTM, Price-to-Cash Flow MRQ, Price-to-Tangible Book Ratio MRQ, and Cash/Share MRQ. Thus, the rest of the analysis is performed based on the mentioned ratios. Another approach of choosing the inputs and outputs for the analysis could consist of asking the managers (or investors) in which specific aspects of the business itself they are interested in. This should be done carefully, as the results could differ compared to the approach from the mathematical optimization and statistical properties of correlations in this research. This opens interesting questions for future work which should consider such issues.

6.4.2

MAIN DEA RESULTS

The SBM model was optimized for the sample of 292 stocks, and the rankings of every company are shown in Figure 6.1. There are 13 companies in total which have an efficiency score equal to unit value, and 124 which are inefficient with the score equal to zero. The empirical histogram is shown in Figure  6.2. Some of the most efficient companies are shown in Figure 6.3 (i.e., their efficiency scores, equal to unit value), while some of the inefficient ones (with efficiencies greater than zero value) in Figure 6.4. Now, a company can compare itself to others, where it stands in terms of relative (in)efficiency and can make a better focus on those companies which are most interesting to it. The interest could be based on similar production and business focus to see the position of the competitors; or simply by observing the most efficient ones and what the characteristics of those companies are. The focus can be shifted solely on several most important or interesting companies and their financial ratios and other relevant aspects of the business itself. A company does not need to observe the entire sample, i.e., all of the companies within the branch. This is one of the advantages of having such an approach in comparing the performances. Those companies which were shown in Figure 4 as inefficient were examined in more detail in Table 6.2. This table depicts the optimal slack values from optimizing the problem (6.2). The first five columns show output slacks, i.e., how much does each DMU (i.e., firm) needs to increase output values, whereas the last four columns indicate how much does each firm needs to decrease its input values. This is helpful in depicting the sources of inefficiencies of each firm which can be in focus of the researcher, manager, investor, or other interested parties. As an example, the DMU number 38 does not need to affect business regarding the P/ E ratio, return on investment (ROI), EPS, dividend yield, and price-to-tangible book ratios. However, it needs to re-evaluate part of the business which affects the asset turnover, price to sales, price-to-cash flow, and cash per share ratios. Thus, it could be said that this company may be having problems with stock pricing as the majority of needed changes are related to the ratios which involve the prices of its stocks. Similar interpretations can be made for other companies. Thus, the usefulness of the SBM model can be seen in observing in detail which aspects of the business itself need to be taken into greater consideration when thinking about what needs to be improved upon. From the point of view of the investor, he can obtain insights into which company

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FIGURE 6.1

SBM rankings of all 292 companies. (Source: author’s calculation.)

FIGURE  6.2 calculation.)

Empirical histogram of efficiency scores from Figure  6.1. (Source: author’s

Data Envelopment Approach

FIGURE 6.3

93

Most efficient companies (efficiency scores). (Source: author’s calculation.)

FIGURE 6.4 Sample of inefficient companies (efficiency scores). (Source: author’s calculation.)

is under- or overpriced on the stock market based on many different measures used in the analysis. Thus, he can adjust his portfolio structure with respect to the results and his preferences. Finally, Table 6.3 is showing optimal weights of the efficient DMUs in the optimization process for every inefficient DMU from Table 6.2. These weights are interpreted as how much percentage of the efficient DMUs’ outputs and inputs has been used in order to project an inefficient DMU to the efficient frontier. In essence, this tells the researcher or the manager on which specific efficient DMUs and their characteristics he needs to focus so that he can enhance the business in a way which would provide the arrival on the efficient frontier in the best way possible. If one observes DMU number 38, it is seen that 87.5% in total weighting scheme is given to DMU 183. Thus, DMU 38 should aim to observe the business of DMU 183 in greater detail compared to other efficient DMUs, and especially, the whole sample. Thus, such analysis provides even such detailed information, which reduces the number of DMUs which need to be taken into consideration when making important decisions on the future business itself.

6.4.3

DISCUSSION ON THE BEST AND WORST RANKED COMPANIES

The best-performing companies have high P/ E ratios, which suggest that investors can expect higher earnings growth in the future. This is one of the most important

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TABLE 6.2 Slacks for Inefficient DMUs from Figure 6.4 DMU

P/E

ROI

EPS_TTM

AT

DY5Y

P/S

PCF

P/TB

C/S

38 123 240 269 146 157 225 51 236 278 48 164 26 180 219

0 0 0 0 0 0 0 0 0 0 0.003 0 0 0 0

0 0 0 0 0.053 0 0 0 0.149 0 0 0.090 0 0.013 0

0 64327 0 174437 0 309931 233383 187998 83460 301835 0 206399 0 314605 174055

3.930 2.853 3.067 3.166 3.137 2.348 3.071 2.550 2.718 2.559 2.451 2.270 2.032 2.316 2.119

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

169.990 471.192 94.857 0 10.414 997.476 1.878 152.791 0 0 6.394 0 0 0 0

0.128 0 0 0.068 0 0 0 0 0.003 0.060 0.033 0.176 0.319 0 0.192

0 0 0 0 0 0 0 0 0 0 0 0 0.031 0 0

0.264 0.341 0.124 0.359 0.343 0.719 0.109 0.436 0.056 0.273 0.029 0 0.072 0.165 0.388

Source: author’s calculation.

TABLE 6.3 Weights of Efficient DMUs (First Row) Which were Used in Optimization for the Inefficient DMUs (First Column) DMU/ Weights

46

55

71

131

159

166

38 123 240 269 146 157 225 51 236 278 48 164 26 180 219

0 0 0 0 0.185 0 0.119 0 0 0 0 0 0 0.136 0

0.045 0.038 0.020 0.015 0.015 0.013 0.024 0.011 0.050 0.035 0 0.042 0 0.047 0.050

0 0 0 0.314 0 0 0 0 0.893 0.818 0 0.704 0.586 0.778 0.676

0 0 0 0 0.020 0 0.012 0 0 0 0 0 0 0.031 0

0 0.150 0.121 0 0 0.143 0.161 0.190 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0.222 0.311 0 0.147

Source: author’s calculation

174 183 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0.875 0.636 0.683 0.500 0.699 0.523 0.684 0.568 0.022 0.036 0.546 0 0 0.008 0

191

237

264

282

0.027 0.069 0.024 0.030 0 0.043 0 0.072 0.034 0.020 0.002 0.032 0.017 0 0.012

0.042 0.108 0.072 0.142 0 0.280 0 0.159 0 0.091 0.045 0 0.020 0 0.116

0.009 0 0.081 0 0.080 0 0 0 0 0 0.407 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0.067 0 0

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criteria in the financial analysis for potential investors. As the best-ranked companies have high P/E ratios, it is expected that by investing in them, the earning will grow over time and the expectations on achieving good returns could be justified. The opposite is true for the low-ranked companies. Furthermore, the ROI can gauge the investment’s profitability, and this was also recognized as an important variable in the analysis. The worst-performing companies often had negative ROI values. Thus, this should be improved over time so that they become more efficient and attractive to investors. As EPS was found to be an important variable for the most efficient companies, the inefficient ones have problems with profitability. As profitability is today one of the most relevant indicators on a company’s health, it is not surprising that the most inefficient ones were ranked based on this indicator. The asset turnover was also used in the analysis. This indicator is best to use within the same type of companies, which makes it excellent for the analysis provided here. The best-performing companies had good usage of the given assets in order to generate sales. This is a good indicator of an efficient company. The dividend yield indicated that it is obvious that dividends are important for assessing the efficiency of a company. Although not all companies can payout dividends or they do not want to do that, investors care about the dividends and their relations to the company’s value. The dividend yield indicates if a company is more mature than others. Thus, greater values of this indicator have shown that the company is a better performer if it is not in its infant state. As the P/S ratio should be lowest possible, those companies which had the highest values of this ratio had problems in terms of low sales and revenues. This immediately indicates that the inefficient companies have problems in either attracting customers to generate revenues, or is struggling with the products and new releases so that the revenues could increase. Another variable used was the price-to-cash flow ratio, which is better to use than the P/E ratio as cash flows cannot be manipulated as much as the earnings. Greater cash-generating companies were found to be more efficient in the analysis. The final two indicators used were the price-to-tangible book ratio and the cash per share. The first indicator was the smallest for the efficient companies, as they obtained smaller share price losses, whereas the latter indicator is considered more reliable compared to EPS. As the most efficient companies were found to have the lowest values of the cash per share, this was due to their greater liquidity compared to others. As can be seen from the discussion so far, there exists a great complexity in such an analysis. This is due to different knowledge being needed to understand both financial theory and quantitative methods used to assess the efficiency of a company.

6.4.4 ROBUSTNESS CHECKING – MCDM Finally, the robustness of the results has been checked via the MCDM model. The main idea in the MCDM approach was made that all of the previously mentioned output variables were examined as objectives that need to be the greatest possible. On the other hand, the input variables from the DEA approach were observed in MCDM as objectives that need to be the smallest possible. Thus, it is obvious that the objectives are conflicted, as it is a usual case in business decision-making. As 9 inputs and outputs variables are used in the MCDM, and the decision-maker can

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FIGURE 6.5 Comparison of rankings based on SBM ( y axis) and MCDM (x axis). (Source: author’s calculation.)

give weights to the objectives based on previous knowledge and experience, it was opted that all of the objectives have equal weights. In that way, the analysis is as objective as possible. The rankings from the MCDM results have been contrasted to the rankings of the SBM model from the previous subsection. These comparisons are shown in Figure 6.5. It can be seen that the correlation between the two ranking systems is more than 80%, which gives confidence that the results are reliable and can be used in future research as well. The analysis in practice should not stop here. Now, with all information obtained as in previous subsections, the management, alongside financial experts and quantitative modellers, would need to focus on the specific aspect of the business, which is indicated in the poor-performing financial ratio results.

6.4.5

FURTHER POSSIBLE INTEGRATIONS OF DEA AND MCDM

Something which can be considered for future theoretical and empirical work is as follows. A researcher or the investor can opt for a multistep optimization process in which in the first step, the MCDM approach could be used on the initial data set. The obtained rankings could be used to divide companies into several groups: best-ranked ones, middle ones, and lowest-ranked ones. Each subgroup can be then evaluated via DEA models so that detailed insights can be obtained into the business characteristics of the best, middle, and worst-ranked companies. Something similar could be done in obtaining the DEA results first as was done in this research. Then, in the second step, the input excess and output slacks could be used in MCDM rankings so that the researcher can obtain one number (rank) regarding the company of interest concerning the excess and slacks of that company. Of course, these individual values are important for a company to make the best decisions possible on which inputs should be reduced and which outputs should be increased. However, to obtain fast results in terms of where the company is standing compared to others, such an approach could be useful. Other considerations for future work are examined in the conclusion section.

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6.5

97

CONCLUSION

Business performance evaluation is something that has been generally accepted both in academic and business circles. The reasoning lies in the rising globalization and competition, alongside other important factors that influence the conduction of competitive business today. That is why businesses today need to re-evaluate their performance and compare themselves to others continuously. Since managers and researchers have to face a lot of different data while comparing the business performance by using financial ratios within an industry, such problems lie within big data analysis. This research focused on a sample of companies that constitute the same stock market index (NASDAQ) so that comparability can be the greatest possible. The approach of the study employs a nonparametric approach to modelling: the DEA approach. This was chosen due to characteristics of this methodology, which include no assumptions on the distribution of data have to be made, interpretations are straightforward, with details on the sources of (in)efficiencies of the DMUs under consideration, objectivity is present, as the researcher does not implement subjective judgment as some other approaches ask, etc. However, a parsimonious approach was made. Namely, the sample started with more than 300 stocks and more than 40 financial ratios. Not all of the ratios are needed in the DEA optimization modelling, as greater correlations among inputs and/or outputs are interpreted as the same information, which could lead to spurious results. Thus, the following steps are advised for future research, as well as managers, financial experts, and others present in the evaluation process. First, the DMUs (i.e., firms) which have barely any data at all should be removed from the analysis. Second, the rest of the DMUs which have some missing data are advised not to be removed from the analysis. This is due to problems that could arise as the sample could be reduced to a nonmeaningful one. The problems of missing data have been examined within the DEA modelling, and this research followed a simple procedure in which penalties are given to missing input and output data. Third, the correlation matrix has been examined in order to remove the input and output variables which were excess in the analysis. In that way, the total process relieved so that the manager (and others) can make reasonable interpretations with several ratios (when compared to over 40). The analysis provided the following information: the efficiency score of every DMU is calculated, and since it belongs to the interval [0,1], it is easy to compare how one DMU compares to others, especially the efficient ones. Furthermore, the sources of inefficiencies are estimated as well. This gives detailed insights into which aspects of the business itself the company needs to re-evaluate and change in order to become more efficient. This saves time and money in the research process so that timely decisions can be made. Furthermore, the analysis provides insights into the weights of efficient companies in evaluating the inefficient ones. Such information can be crucial for a company, as with such information, it can focus solely on those competitors which were recognized as the best comparable ones. This could also save time and money when making such an analysis. Furthermore, the robustness of the results needs to be checked. This is often neglected and bad results over time could be explained by not checking the reliability of obtained results. A rather simple approach was shown in this study, and it confirms

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the previously obtained results. This is an additional step that can help tremendously. Although, parsimony is advised, as every step, calculation, and decision in the business take time and money. Recommendations for future research and all those included in evaluating performances of business as provided in this study are as follows. Lastly mentioned parsimony is always advised, as very complex models take up a lot of time and money, as well as knowledge. If a model can be useful and reliable with fewer data needed to reflect the reality, it could be taken into consideration when making business decisions. Furthermore, when obtaining the results on the efficiency scores, the sample can be reduced to focus only on the efficient companies and maybe those similar to the company of the interest (if it is not efficient). This saves money and time in finding the sources of inefficiencies of the business itself, but also managers can focus on those efficient businesses to see what they are doing so that their results indicate the best performance in the sample of interest. Finally, recommendations consist of trying to combine a purely mathematical standpoint of view of the observed data with the knowledge of managers, investors, and other relevant people who make the decisions within the business itself. This refers to choosing the inputs and outputs in the analysis, by looking at both the statistical characteristics of data which affect the modelling results and those variables which are relevant for the decision-maker. Some of the shortfalls of this research include the following. Only financial ratios data were included in this study. However, more businesses today are focusing on the social and environmental factors on conducting business as well. Thus, the analysis should be repeated in the future with the inclusion of variables that could measure such specific questions. The paradigm of sustainability (and circular economy) is getting more attention today than a couple of decades and years ago. If a company is focusing on such concepts as well, future work should study the existing findings on this matter and the analysis should include variables that are able to measure such concepts. Furthermore, only a static analysis was done in this study. This means that the data used in the empirical research reflect only one fiscal year in the majority of the cases. The best-performing companies could be coincidental in that year, but previously they could have been worse. This is why a dynamic approach is suggested in future work so that a company can follow its performance and changes over time, to see which aspects of the business should be focused on in each year. Furthermore, other related approaches could be observed in the future as well, maybe as a robustness checking of the results. This includes the GRA methodology, as it serves also as a ranking system (but does not provide detailed interpretations as DEA does), and other nonparametric ranking construction methodologies as well.

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APPENDIX TABLE A1 List of Stocks Used in the Study Name

Name

Name

21Vianet 2U Inc 36Kr Holdings ACI Worldwide Acm Research Adesto Technologies Corp Adobe Advanced Energy Aehr Test Systems Agilysys AGM A Akamai Akerna Alarm,com Holdings Alithya A Allot Communications Allscripts Alpha & Omega Semiconductor Alphabet A Alphabet C Altair Engineering AMD Amdocs Ameri American Software Amkor Amtech Analog Devices ANSYS Appfolio Inc Appian Apple Applied Materials ASML ADR Aspen AstroNova Atlassian Corp Plc Atomera

Ebix eGain EMCORE Endurance Intl Entegris ePlus Everbridge Everspin Tech Evolving Systems Exela Tech Facebook Fangdd Network Finjan Hold FireEye Five9 Forescout Tech FormFactor Formula Systems ADR Forrester Fortinet Fronteo GDS Holdings Gogo Inc Great Elm Capital Gridsum GSE Systems GSI Technology GTY Tech A Health Catalyst Himax i3 Verticals IAC/InterActiveCorp icad Ichor Holdings Ideanomics Identive Immersion Corp Impinj

Opera Optibase Park City Group PC Connection PC-Tel PDF Solutions Pegasystems Perficient Perion Network Photronics Pintec Tech Pixelworks Power Integrations Powerbridge Premier Inc Presidio Progress Proofpoint PTC QAD A QAD B Qorvo Inc Qualcomm Qualstar Qualys QuickLogic Qumu Corp Qutoutiao Rambus Rapid7 Inc RCM Technologies RealPage Red Violet RigNet Rimini Street Ruhnn RumbleON Safe-T (Continued)

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TABLE A1 (Continued) List of Stocks Used in the Study Name

Name

Name

AudioEye Aurora Mobile Autodesk Aware Axcelis AXT B Communications Baidu Bandwidth Baozun Inc. Benefitfocus Beyond Air Blackbaud Blackline Blucora Boingo Borqs Tech Bottomline Bridgeline Digital Brightcove Broadcom BroadVision Brooks Automation BSQUARE Cabot Cadence Design Canaan Carbonite CDK Global Holdings LLC CDW Corp Cerner CEVA Check Point Software China Finance ChipMOS Tech Cinedigm Cirrus Citrix Systems CLPS Cogent Cognizant A Cohu

Infinera Innodata Inpixon Insight Enterprises Intel InterDigital Internap Intuit j2 Global Key Tronic KLA-Tencor Kopin Kulicke & Soffa Lam Research Lattice Limelight LivePerson Livongo Logitech LogMeIn Luokung Tech Magic Majesco Manhattan Associates Marin Software Marvell Match Group Materialize NV Maxim Meet Group Mellanox MER Microchip Micron Microsoft MicroStrategy Mimecast Ltd MIND CTI Mitek MKS Instruments MobileIron Mohawk Group

Sapiens ScanSource Sciplay Seagate Secureworks Semtech SGOCO Group Ltd SharpSpring Sify Silicom Silicon Labs Silicon Motion SilverSun Tech Simulations Plus SINA Corp Sitime Corp SITO Mobile Ltd. Skyworks Smart Global Smith Micro Software Sohu,Com Sphere 3D Splunk Sprout Social SPS Commerce SS&Cs Stratasys Ltd. Streamline Super League Gaming Support.com SVMK Synacor Synaptics Synchronoss Synopsys Tabula Rasa HealthCare Talend Taoping Tech Data TeleNav Tenable Teradyne (Continued)

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TABLE A1 (Continued) List of Stocks Used in the Study Name

Name

Name

CommVault Computer Programs and Systems Computer Task Cornerstone On Demand CounterPath Coupa Software Covetrus Creative Realities Cree Criteo Sa Crowdstrike Holdings CSG Systems CSP CVD Equipment Cyberark Software Cypress Cyren DarioHealth Data I/O Datadog Digimarc Diodes DocuSign Domo DouYu Dropbox DSP Group

Momo Inc MongoDB Monolithic MoSys My Size Neonode NetApp NetEase NetScout NetSol Neurotrope Nextgen Healthcare NIC nLIGHT NortonLifeLock Nova Nuance Communications Nutanix NVIDIA NXP NXT-ID O2Micro Okta ON Semiconductor One Stop Systems Onespan Open Text

Texas Instruments Descartes Systems The Hackett TiVo Tower TransAct Tucows Inc, Ultra Cleans Upland Software Inc Varonis Systems Verint VeriSign Veritone Virtusa Wayside Weibo Corp Western Digital Wix.Com Ltd Workday Xilinx Xperi XpresSpa Yandex Yunji Zix Zscaler

Source: Investing (2019)

P/S 1,000 -0,064 -0,011 0,021 0,018 0,023 0,017 0,023 0,017 0,031 0,031 0,017 0,010 0,016 0,042 0,033 0,084 0,055 0,083 0,053 0,032 0,034 0,006 0,020 -0,025 0,001 0,156 -0,033 -0,006 -0,041 0,044 -0,001 0,074 -0,007 0,100 0,033 -0,023 0,004 -0,023 -0,035 0,035 -0,006 -0,028 -0,031

PCF

1,000 0,644 -0,114 0,005 -0,110 0,004 -0,110 0,005 0,029 0,028 -0,040 -0,033 0,022 0,125 0,005 0,067 -0,025 0,065 0,020 0,060 0,107 0,033 -0,079 -0,121 0,086 -0,020 -0,356 0,010 -0,025 0,052 0,048 0,067 -0,226 -0,134 0,044 0,245 0,086 0,101 0,289 0,100 0,035 -0,003 -0,010

1,000 0,122 0,104 0,132 0,103 0,132 0,102 0,124 0,123 0,092 0,028 0,159 0,156 0,018 0,265 0,176 0,253 0,138 0,102 0,030 0,126 -0,118 -0,190 0,110 0,028 -0,246 0,044 0,035 0,150 -0,025 0,151 -0,100 -0,013 0,016 -0,072 0,075 0,013 0,148 0,088 0,111 0,029 0,017

PFCF

1,000 0,133 0,997 0,133 0,997 0,133 0,031 0,030 0,031 0,006 0,045 0,049 0,010 0,253 0,427 0,236 0,052 0,124 0,013 0,031 0,000 -0,009 0,038 0,030 0,074 0,012 0,081 0,044 0,037 0,029 0,025 0,055 -0,044 -0,914 0,024 0,031 0,025 0,021 0,016 0,036 0,039

P/B

1,000 0,151 1,000 0,151 1,000 0,028 0,028 0,033 0,012 0,041 0,028 0,077 0,219 0,263 0,151 0,270 -0,012 -0,008 0,028 0,008 -0,068 0,024 0,028 0,055 0,011 0,073 0,012 0,019 0,026 0,022 0,043 -0,305 -0,354 0,021 0,026 0,021 0,015 0,026 0,024 0,026

P/TB

1,000 0,151 1,000 0,151 0,037 0,037 0,034 0,009 0,053 0,061 0,013 0,300 0,430 0,283 0,060 0,122 0,013 0,034 -0,005 -0,014 0,041 0,030 0,080 0,014 0,087 0,067 0,038 0,031 0,028 0,060 -0,062 -0,913 0,026 0,034 0,028 0,023 0,019 0,037 0,040

C/S

1,000 0,151 1,000 0,026 0,026 0,033 0,013 0,040 0,027 0,077 0,216 0,259 0,148 0,266 -0,012 -0,008 0,028 0,008 -0,069 0,024 0,027 0,056 0,010 0,072 0,010 0,018 0,025 0,022 0,043 -0,304 -0,355 0,021 0,026 0,021 0,015 0,026 0,023 0,026

1,000 0,151 0,037 0,037 0,034 0,008 0,052 0,061 0,013 0,300 0,429 0,283 0,059 0,121 0,013 0,034 -0,005 -0,014 0,041 0,031 0,079 0,014 0,087 0,066 0,038 0,031 0,028 0,060 -0,063 -0,913 0,026 0,034 0,028 0,023 0,019 0,037 0,040

LTD/E D/E

Source: author’s calculation

P/S PCF PFCF P/B P/TB C/S LTD/E D/E P/E GM GM5Y OM OM5Y PM PM5Y NPM NPM5Y BEPS DEPS BV/S TBV/S CF/S ROE ROE5Y ROA ROA5Y ROI ROI5Y EPS_Q EPS_TTM EPS_G S_Q AT IT R/E NI/E RT DY DY5Y DGR PR R/S QR CR

1,000 0,026 0,025 0,032 0,012 0,038 0,027 0,077 0,214 0,259 0,146 0,266 -0,012 -0,009 0,027 0,008 -0,068 0,023 0,026 0,056 0,010 0,070 0,010 0,017 0,024 0,021 0,042 -0,305 -0,355 0,020 0,026 0,021 0,015 0,025 0,024 0,027

P/E

1,000 1,000 0,532 0,462 0,699 0,170 0,125 0,458 0,334 0,438 0,333 -0,010 0,036 0,176 -0,008 -0,017 0,070 0,142 -0,002 0,008 0,316 0,273 0,044 0,073 0,221 0,159 0,114 0,027 0,016 0,024 0,045 0,026 0,500 -0,061 -0,077

GM

1,000 0,528 0,459 0,695 0,170 0,124 0,458 0,333 0,438 0,333 -0,010 0,035 0,175 -0,007 -0,016 0,069 0,143 -0,002 0,009 0,314 0,272 0,043 0,073 0,220 0,158 0,114 0,027 0,016 0,024 0,044 0,027 0,496 -0,061 -0,077

1,000 0,855 0,897 0,077 0,087 0,180 0,173 0,168 0,154 0,005 0,045 0,158 -0,014 -0,038 0,068 0,111 -0,031 -0,005 0,387 0,207 -0,052 0,072 0,465 0,188 0,053 -0,021 -0,013 -0,023 -0,072 -0,042 0,916 -0,059 -0,071

GM5Y OM

1,000 0,759 0,033 0,048 0,098 0,099 0,086 0,086 0,031 0,017 0,098 -0,010 -0,020 0,020 0,091 0,010 -0,005 0,348 0,169 -0,046 -0,082 0,319 0,222 0,160 0,007 0,008 0,009 -0,038 -0,004 0,887 -0,143 -0,151

OM5Y

1,000 0,142 0,105 0,374 0,293 0,348 0,276 0,098 0,105 0,224 -0,031 -0,041 0,068 0,129 -0,032 -0,004 0,426 0,331 0,042 0,115 0,387 0,185 0,035 -0,009 -0,012 -0,025 -0,014 -0,017 0,848 -0,032 -0,041

PM

1,000 0,039 0,480 0,157 0,492 0,155 0,009 0,033 0,079 -0,017 -0,032 0,020 0,031 -0,038 0,021 0,064 0,261 -0,077 0,066 0,047 0,096 0,029 -0,035 0,038 0,044 -0,009 0,019 0,056 -0,015 -0,019 1,000 0,156 0,219 0,148 0,237 -0,015 0,253 0,094 -0,016 -0,046 -0,037 0,002 -0,009 0,017 0,004 0,109 -0,020 0,063 0,042 0,026 0,045 0,012 0,046 0,043 0,056 0,008 0,052 0,000 -0,002 1,000 0,616 0,971 0,586 0,067 0,158 0,210 -0,072 -0,109 0,093 0,093 -0,018 0,054 0,271 0,459 0,047 0,147 0,099 0,161 -0,055 -0,195 0,069 0,083 0,068 0,061 0,149 -0,009 -0,028 1,000 0,577 0,903 0,047 0,028 0,196 -0,045 -0,116 -0,031 0,092 -0,039 0,042 0,239 0,166 -0,019 0,125 0,087 0,115 0,043 -0,370 0,052 0,073 0,024 0,012 0,136 -0,049 -0,068 1,000 0,567 0,045 0,126 0,189 -0,088 -0,135 0,083 0,085 -0,045 0,050 0,275 0,433 0,055 0,136 0,100 0,164 -0,010 -0,159 0,067 0,085 0,067 0,057 0,138 -0,002 -0,030 1,000 -0,010 0,014 0,183 -0,063 -0,141 -0,040 0,061 -0,073 0,036 0,229 0,163 -0,024 0,111 0,085 0,093 0,041 -0,034 0,039 0,067 0,007 0,004 0,119 -0,048 -0,072 1,000 0,265 0,052 0,033 0,018 0,036 0,055 0,007 -0,003 -0,009 0,045 0,010 0,056 0,010 -0,033 0,009 -0,091 0,001 0,013 0,033 0,024 0,024 -0,024 -0,021 1,000 0,068 0,011 0,016 0,058 0,055 -0,005 0,026 0,085 0,089 0,050 0,063 0,023 -0,023 0,004 0,028 0,019 0,012 0,060 0,066 0,044 -0,005 -0,006

PM5Y NPM NPM5Y BEPS DEPS BV/S TBV/S CF/S

TABLE A2 Correlations Between Inputs and Os in DEA Analysis

1,000 0,104 -0,030 0,103 0,167 0,011 0,020 0,132 0,168 -0,001 0,044 0,073 0,150 0,031 0,019 -0,006 -0,024 0,076 0,030 0,121 -0,014 -0,022 1,000 0,644 -0,102 0,182 0,341 -0,005 -0,032 -0,222 0,231 0,141 -0,012 -0,048 -0,003 0,022 -0,001 0,016 -0,012 0,002 -0,021 -0,014 0,030 1,000 -0,121 0,214 0,589 -0,009 -0,063 -0,258 0,371 -0,043 -0,008 -0,076 0,010 0,044 -0,028 -0,021 -0,022 -0,009 -0,033 0,008 0,087 1,000 0,354 -0,042 -0,014 0,169 0,177 0,004 0,003 0,005 -0,061 -0,037 -0,028 -0,016 -0,003 0,097 0,063 0,073 0,183 0,163 1,000 -0,061 -0,069 0,114 -0,132 0,022 -0,024 0,025 0,003 0,011 -0,002 0,011 -0,049 0,086 0,096 0,110 0,093 0,078 1,000 0,007 0,070 0,075 0,305 -0,081 0,232 -0,044 -0,130 -0,145 -0,039 -0,021 -0,017 -0,014 -0,059 0,012 0,086

1,000 -0,054 0,010 -0,021 0,036 -0,004 0,053 -0,062 -0,023 -0,016 0,001 -0,037 0,066 -0,004 -0,028 -0,029

1,000 0,176 0,163 0,012 0,508 0,106 0,018 -0,053 0,034 -0,007 0,101 -0,032 0,347 0,025 0,004

1,000 0,161 0,086 0,097 0,115 -0,080 -0,004 0,069 0,017 0,094 0,037 0,213 0,044 0,039

1,000 -0,015 -0,011 -0,101 -0,074 0,011 -0,028 -0,019 0,020 0,020 0,009 0,124 0,157

ROE ROE5Y ROA ROA5Y ROI ROI5Y EPS_Q EPS_TTM EPS_G S_Q

1,000 -0,003 0,037 0,026 -0,022 -0,029 -0,009 -0,049 -0,058 -0,006 -0,009 -0,012

AT

1,000 0,043 -0,064 -0,055 -0,025 -0,026 -0,044 -0,041 0,353 0,019 0,016

IT

1,000 0,484 -0,073 -0,061 0,054 -0,130 -0,072 0,155 -0,121 -0,135

R/E

1,000 0,161 0,094 0,033 -0,100 -0,074 0,085 -0,200 -0,236

NI/E

1,000 0,026 0,072 0,130 0,074 0,016 -0,035 -0,040

RT

1,000 0,060 0,247 0,019 0,016 0,103 0,088

1,000 0,184 0,150 0,008 0,015 0,009

1,000 0,198 0,008 0,494 0,492

DY DY5Y DGR

R/S

QR

CR

1,000 -0,033 1,000 -0,016 -0,032 1,000 -0,032 -0,036 0,981 1,000

PR

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REFERENCES Alcock, J., Steiner, E. and Tan, K. J. K. 2011. Future earnings growth volatility and the value premium. Paper presented at the 24th Australasian Finance and Banking Conference, Sydney, Australia. Arsad, R., Isa, Z. and Shaari, S. N. M. 2018. Estimating efficiency performance of decision making unit by using SFA and DEA method: A cross sectional data approach. International Journal of Engineering & Technology 7 (4.33): 25–31. Baker, H. K. and Smith, D. M. 2006. In search of a residual dividend policy. Review of Financial Economics 15(1): 1–18. doi:10.1016/j.rfe.2004.10.002. Baležentis, A., Baležentis, T. and Misiunas, A. 2012. An integrated assessment of Lithuanian economic sectors based on financial ratios and fuzzy MCDM methods. Technological and Economic Development of Economy 18(1): 34–53. doi:10.3846/20294913.2012.656151. Basu, S. 1977. The investment performance of common stocks in relation to their price to earnings ratio: A test of the efficient markets hypothesis. The Journal of Finance 32, 663–682. doi:10.1111/j.1540–6261.1977.tb01979.x Basu, S. 1983. The relationship between earnings yield, market value, and return for NYSE common stocks: Further evidence. Journal of Financial Economics 12(1): 129–156. doi:10.1016/0304-405X(83)90031-4. Beaver, W. H. 2010. Financial statement analysis and the prediction of financial distress. Foundations and Trends in Accounting 5(2): 99–173. doi:10.1561/1400000018. Bigaret, S., Hodgett, R. E., Meyer, P., Mironova, T. and Olteanu, A.-L. 2017. Supporting the multi-criteria decision aiding process: R and the MCDA package. EURO Journal on Decision Processes 5(1–4): 169–194. doi:10.1007/s40070-017-0064-1. Brauers, W. K. M. 2004. Optimization Methods for a Stakeholder Society – A revolution in Economic Thinking by Multi-Objective Optimization. Boston: Kluwer Academic Publishers. Brauers, W. K. M. 2007. What is meant by normalization in decision making? International Journal of Management and Decision Making 8(5–6): 445–460. doi:10.1504/ IJMDM.2007.013411. Brauers, W. K. M. and Ginevičius, R. 2009. Robustness in regional development studies, the case of Lithuania. Journal of Business Economics and Management 10(2): 121–140. doi :10.3846/1611-1699.2009.10.121-140. Brauers, W. K. M. and Zavadskas, E. K. 2010. Project management by multimoora as an instrument for transition economies. Technological and Economic Development of Economy 16(1): 5–24. doi:10.3846/tede.2010.01. Brennan, M. J. 1970. Taxes, market valuation, and corporate financial policy. National Tax Journal 23(1): 417–427. doi:10.1016/0047-2727(73)90008-X. Chan, L. K., Hamao, Y. and Lakonishok, J. 1991. Fundamentals and stock returns in Japan. The Journal of Finance 46(5): 1739–1764. doi:10.1111/j.1540-6261.1991.tb04642.x. Chan-Lau, J. 2006. Fundamentals-based estimation of default probabilities: A survey. IMF Working Papers 2006(149). doi:10.5089/9781451864090.001. Chen, H.-H. 2008. Stock selection using data envelopment analysis, Industrial Management & Data Systems, 108(9), 1255–1268. Cooper, W., Seiford, L. M. and Zhu, J. 2011. Handbook on Data Envelopment Analysis, New York: Springer. Damjihabi, S. D. 2016. Performance measurement through ratio analysis: The case of indian hotel company ltd. The IUP Journal of Management Research 15(1): 30–36. Demerjian, P. R. 2007. Financial Ratios and Credit Risk: The Selection of Financial Ratio Covenants in Debt Contracts. AAA 2007 Financial Accounting & Reporting Section (FARS) Meeting Paper. Available at SSRN: https://ssrn.com/abstract=929907 or http://dx.doi.org/10.2139/ssrn.929907.

104

Big Data Analytics in Supply Chain Management

Demerjian, P. R. 2017. Calculating Efficiency with Financial Accounting Data: Data Envelopment Analysis for Accounting Researchers. Available at SSRN: https://ssr n. com/abstract=2993687 or http://dx.doi.org/10.2139/ssrn.2993687. Dempsey, M. 2010. The book-to-market equity ratio as a proxy for risk: Evidence from Australian markets. Australian Journal of Management 35(1): 7–21. doi:10.1177/ 0312896209351451. Ding, D. and Sickles, R. C. 2018. Frontier efficiency, capital structure, and portfolio risk: An empirical analysis of U.S. banks. BRQ Business Research Quarterly. doi:10.1016/j. brq.2018.09.002. Doyle, J. and Green, R. 1994. Efficiency and cross-efficiency in DEA: Derivations, meanings and uses. Journal of the Operational Research Society 45 (5): 567–578. doi:10.1057/jors.1994.84. Edirisinghe, N. C. P. and Zhang, X. 2007. Generalized DEA model of fundamental analysis and its application to portfolio optimization. Journal of Banking & Finance 31, 3311– 3335. doi: 10.1016/j.jbankfin.2007.04.008. Emrouznejad, A. and Cabanda, E. 2010. An aggregate measure of financial ratios using a multiplicative DEA model. International Journal of Financial Services Management 4(2): 114–126. doi: 10.1504/ IJFSM.2010.032435. Fama, E. F. and French, K. 1992. The cross-section of expected stock returns. The Journal of Finance 47(2): 427–465. doi:10.1111/j.1540-6261.1992.tb04398.x. Fama, E. F/ and French, K. 1993. Common risk factors in the returns on stocks and bonds. Journal of Financial Economics 33(1): 3–56. doi:10.1016/0304-405X(93)90023-5. Fama, E. F. and French, K. R. 1998. Value versus growth: The international evidence. The Journal of Finance 53(6): 1975–1999. doi:10.1111/0022-1082.00080. Fang-Ming, L. and Wang-Ching, C. 2010. A precaution diagnosis of financial distress via Grey Situation Decision. Journal of Grey System 22(4): 395–403. Feroz, E. H., Kim, S. and Raab, R. L. 2003. Financial statement analysis: A data envelopment analysis approach. Journal of the Operational Research Society 54(1): 48–58. doi:10.1057/palgrave.jors.2601475. Gardijan, M. and Škrinjarić, T. 2015. Equity portfolio optimization: A DEA based methodology applied to the Zagreb Stock Exchange. Croatian Operational Research Review (CRORR) 6: 405–417. Granger, C. J. 1989. Combining forecasts – twenty years later. Journal of Forecasting 8(1): 167–173. Gregoriou, G.N., Gultek, M. M., Demirer, I. 2017. Efficiency of Cruise Ships: A Data Envelopment Analysis Approach International Journal of Global Business 10(1): 8–25. Ho, S., Xie, M. and Goh, T. 2002. A comparative study of neural network and box-jenkins ARIMA modeling in the time series prediction. Computers and Industrial Engineering 42(2): 371–375. doi:10.1016/S0360-8352(02)00036-0. Hornungova, J. and František, M. 2016. Financial performance evaluation of the Czech agricultural companies with factor analysis. Scientific Papers of the University of Pardubice, Series D 37 (2): 26–38. Huang, C., Dai, C. and Guo, M. 2015. A hybrid approach using two-level DEA for financial failure prediction and integrated SE-DEA and GCA for indicators selection. Applied Mathematics and Computation 251, 431–441. doi:10.1016/j.amc.2014.11.077. Hurson, C. H. and Zopounidis, C. 1995. On the use of multicriteria decision aid methods to portfolio selection. In: Clímaco, J. (ed.): Multicriteria Analysis. Berlin, Heidelberg: Springer. Investing. 2019. Available at https://www.investing.com/indices/nnasdaq-computer-components. (Accessed on 29 December 2019). Jablonsky, S. F. and Barsky, N. P. 2001. The Manager’s Guide to Financial Statement Analysis. New York: Wiley.

Data Envelopment Approach

105

Jordan, C. E., Clark, S. J. and Smith, W. R. 2007. Should earnings per share (EPS) be taught as a means of comparing inter company performance? Journal of Education for Business 82(6): 343–348. doi: 10.3200/JOEB.82.6.343-348. Kohers, T., Huang, M. and Kohers, N. 2000. Market perception of eff iciency in bank holding company mergers: the roles of the DEA and SFA models in capturing merger potential. Review of Financial Economics 9(2): 101–120. doi:10.1016/S1058-3300(00)00019-7. Kothari, S. P. and Shanken, J. 1997. Book-to-market, dividend yield, and expected market returns: a time-series analysis. Journal of Financial Economics 44(2): 169–203. doi:10.1016/S0304-405X(97)00002-0. Kuosmanen, T. 2009. Data envelopment analysis with missing data. Journal of the Operational Research Society 60(12): 1767–1774. doi:10.1057/palgrave.jors.2600056. Laitinen, E. K. 2018. Financial reporting: Long-term change of financial ratios. American Journal of Industrial and Business Management 8, 1893–1927. doi:10.4236/ajibm.2018. 89128. Leary, M. T. and Michaely, R. 2011. Determinants of dividend smoothing: Empirical evidence. The Review of Financial Studies 24(10): 3197–3249. doi:10.1093/rfs/ hhr072. Lee, C. and Lee, W. H. 2008. Can financial ratios predict the Malaysian stock return? Integration & Dissemination 2, 7–8. Li, H-Y., Zhang, C. and Zhao, D. 2010. Stock investment value analysis model based on AHP and grey relational degree. Management Science and Engineering 4, 1–6. Li, X., Song, J. and Huang, B. 2016. A scientific workflow management system architecture and its scheduling based on cloud service platform for manufacturing big data analytics. The International Journal of Advanced Manufacturing Technology 84(1–4): 119–131. doi:10.1007/s00170-015-7804-9. Lim, S., Oh, K.W. and Zhu, J. 2013. Use of DEA Cross-Efficiency Evaluation in Portfolio Selection: An application to Korean Stock Market, European Journal of Operational Research 236(1): 361–368. Lin, W.-C., Liu, C.-F. and Chu, C.-W. 2010. Financial ratios and credit risk: The selection of financial ratio covenants in debt contracts. Journal of the Eastern Asia Society for Transportation Studies 8, 246–260. Lintner, J. 1956. Distribution of incomes of corporations among dividends, retained earnings, and taxes. American Economic Review 46(2): 97–113. Litzenberger, R. and Ramaswamy, K. 1979. The effects of personal taxes and dividends on capital asset prices: Theory and empirical evidence. Journal of Financial Economics 7(2): 163–195. doi:10.1016/0304-405X(79)90012-6. Lopes, A., Lanzer, E., Lima, M. and da Costa, Jr., N. 2008. DEA investment strategy in the Brazilian stock market. Economics Bulletin 13(2): 1–10. López, F. J., Ho, J. C. and Ruiz-Torres, A. J. 2016. A computational analysis of the impact of correlation and data translation on DEA efficiency scores. Journal of Industrial and Production Engineering 33(3): 192–204. doi:10.1080/21681015.2015.1136704. Maradin, D., Olgić Draženović, O. and Benković, S. 2018. Performance evaluation of banking industry by using DEA method. 35th International Scientific Conference on Economic and Social Development – “Sustainability from an Economic and Social Perspective”, Lisbon, Portugal, 684–690. Marshall, M., Wray, L., Epstein, P. and Grifel, S. 1999. 21st century community focus: better results by linking citizens, government and performance measurement. Public Management 81(10): 12–18. Matthias, O., Fouweather, I., Gregory, I. and Vernon, A. 2017. Making sense of Big Data – can it transform operations management? International Journal of Operations  & Production Management 37(1): 37–55. doi:10.1108/ IJOPM-02-2015-0084. Muhammad, N. and Scrimgeour, F. 2014. Stock returns and fundamentals in the Australian market. Asian Journal of Finance & Accounting 6(1): 271–290. doi:10.5296/ajfa.v6i1.5486.

106

Big Data Analytics in Supply Chain Management

Myšková, R. and Hájek, P. 2017. Comprehensive assessment of firm financial performance using financial ratios and linguistic analysis of annual reports. Journal of International Studies 10(4): 96–108. doi:10.14254/2071-8330.2017/10-4/7. Najmi, M. and Kehoe, D. F. 2001. The role of performance measurement system in promoting quality development beyond ISO 9000. International Journal of Operations  & Production Management 21(1/2): 159–172. doi:10.1108/01443570110358512. Narkunienė, J. and Ulbinaitė, A. 2018. Comparative analysis of company performance evaluation methods. Entrepreneurship and Sustainability Issues 6(1): 125–138. doi:10.9770/jesi.2018.6.1(10). Neely, A., Gregory, M., Platts, K. 1995. Performance measurement system design: A literature review and research agenda. International Journal of Operations & Production Management 15(4): 80–116. Noda, R. F., Martelanc, R. and Kayo, E. K. 2015. The earnings/price risk factor in capital asset pricing models. Revista Contabilidade  & Financas 27(70): 281–293. doi: 10.1590/1808-057x201412060. Oberholzer, M. 2012. The relative importance of financial ratios in creating shareholders’ wealth. South African Journal of Economic and Management Sciences 15(4): 416–428. Penman, S. H., Richardson, S. A. and Tuna, I. 2007. The book-to-price effect in stock returns: Accounting for leverage. Journal of Accounting Research 45(2): 427–467. Powers, J. and McMullen, P. 2002. Using data envelopment analysis to select efficient large cap securities. Journal of Business and Management 7(7): 31–42. Roje, G. 2005. The role of accounting determinants in predicting long term credit ratings. In: Rimmel, G. (ed.), 28th European Accounting Association Congress: Collected Abstracts. Goteborg, Sweden: BAS Publishing House. Rosini, I. and Gunawan, J. 2018. Financial ratio and performance airlines industry with DEA and TOPSIS Model. International Journal of Pure and Applied Mathematics 119(10): 367–374. Šarlija, N. and Jeger, M. 2011. Comparing financial distress prediction models before and during recession. Croatian Operational Research Review 2(1): 133–142. Sexton, T. R. 1986. Data envelopment analysis: Critique and extension. New Directions for Program Evaluation 32: 73–105. doi:10.1002/ev.1441. Sickles, R. C. and Zelenyuk, V. 2019. Measurement of Productivity and Efficiency Theory and Practice. Cambridge: Cambridge University Press. Siew, L. W., Fai, L. K. and Hoe, L. W. 2017. An empirical investigation on the efficiency of the financial companies in Malaysia with DEA model. American Journal of Information Science and Computer Engineering 3(3): 32–38. Škrinjarić, T. 2014. Investment strategy on the zagreb stock exchange based on dynamic DEA. Croatian Economic Survey 16, 129–160. doi:10.15179/ces.16.1.5. Škrinjarić, T. and Šego, B. 2019. Using grey incidence analysis approach in portfolio selection. International Journal of Financial Studies 7(1): 1–16. doi:10.3390/ijfs7010001. Soboleva, Y. P., Polyanin, A. V., Leonova, O. V., Korgina, O. A. and Merkulov, A. V. 2018. Strategic approach to performance evaluation of a business entity from the position of analyzing its business potential. Revista Espacios 39(33): 31–45. Taylor, M. and Allen, H. 1992. The use of technical analysis in the foreign exchange market. Journal of International Money and Finance 11(3): 304–314. doi:10.1016/0261-5606(92) 90048-3. Thrall, R. M. 1996. Duality, classification, and slacks in DEA. Annals of Operations Research 66: 23–125. doi:10.1007/ BF02187297. Tone, K. 1997. Several algorithms to determine multipliers for use in cone-ratio envelopment approaches to efficiency evaluations in DEA. In: Amman, H., Rustem, B. and Whinston, A.B. (eds.): Computational Approaches to Economic Problems, Kluwer Academic Publishers, Dordrecht, 91–109.

Data Envelopment Approach

107

Tone, K. 2001. A slack-based measure of efficiency in data envelopment analysis. European Journal of Operational Research 130(3): 498–509. doi:10.1016/S0377-2217(99)00407-5. Tracy, J. A. 2004. How to Read a Financial Report: Wringing Vital Signs Out of the Numbers. New York: John Wiley and Sons. Wallis, K. F. 2011. Combining forecasts – forty years later. Applied Financial Economics 21(1–2): 33–41. doi:10.1080/09603107.2011.523179. Wu, L. S. 2000. A Survey and An Analysis of Investor’s Demands for Listed Companies’ Accounting Information, Economic Research Journal 4, 41–48. Xidonas, P., Mavrotas, G. and Psarras, J. 2009. A multicriteria methodology for equity selection using financial analysis. Computers & Operations Research 36 (12): 3187–3203. doi:10.1016/j.cor.2009.02.009. Yalcin, N., Bayrakdaroglu, A. and Kahraman, C. 2012. Application of fuzzy multi-criteria decision making methods for financial performance evaluation of Turkish manufacturing industries. Expert systems with applications 29, 350–364. doi:10.1016/j. eswa.2011.07.024. Yildiz, S. and Karaka, A. 2012. Defining methods and criteria for measuring business performance: a comparative research between the literature in Turkey and foreign. Procedia Social and Behavioral Sciences 58: 1091–1102. doi:10.1016/j.sbspro.2012.09.1090. Yu, Y.-S., Barros, A. and Wu, W.-H. 2013. Data envelopment analysis and financial ratio: A new approach to estimate the efficiency of Taiwan computer’s peripheral industry. Journal of Information and Optimization Sciences 34(1): 69–84. doi:10.1080/02522667. 2013.777176. Zamani, L., Beegam, R. and Borzoian, S. 2014. Portfolio selection using data envelopment analysis (DEA): A case of select Indian investment companies. International Journal of Current Research and Academic Review 2(4): 50–55.

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Sustainability, Big Data, and Consumer Behavior A Supply Chain Framework Brianna A. Currie Dalhousie University

Alexandra D. French Dalhousie University

M. Ali Ülkü Dalhousie University

CONTENTS 7.1 7.2

Background ................................................................................................... 109 Attributes Impacting Consumer’s Purchasing Behavior .............................. 113 Purchase Price ............................................................................................... 113 Derived Utility .............................................................................................. 117 Product Quality ............................................................................................. 118 Product Support Services .............................................................................. 120 Return Policy ................................................................................................ 121 Summary ....................................................................................................... 123 7.3 A Bidirectional Supply Chain Framework ................................................... 123 7.4 Concluding Remarks .................................................................................... 125 References .............................................................................................................. 127

7.1 BACKGROUND It is consumer demand that drives businesses. A business would be as sustainable as the society in which it operates in and the society will be as sustainable as nature allows. Therefore, as has been evident with the COVID-19 pandemic, it is of the utmost importance that human consumption and behavior should minimize the burden it places on the environment, strive toward a just and equitable society in which diversity is truly embraced and seen as strength. Businesses should be resilient and smart enough to respond to the constantly changing demands of a volatile and global economy. The current pandemic has highlighted the immense value of resiliency in the local and global healthcare supply chains and commercial goods. 109

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Almost all the news related to the pandemic mention the word “supply chain.” This global health crisis provides an unprecedented opportunity for rethinking the “business as usual” mentality; one that is better by incorporating more sustainability goals and practices than the prepandemic one. Accordingly, supply chain researchers all around the world are working hard to help recovery (e.g., Esper 2020; Ivanov 2020; Ülkü and Engau 2020). Therefore, in these tumultuous times, understanding the changing dynamics of consumer behavior and how technology can enhance sustainable efficiency in supply chain operations is pivotal for viability and resiliency. This chapter focuses on consumer behavior toward sustainable products with the goal of developing a framework for how companies can respond to such emerging segmented markets (c.f., Laroche et al. 2001; do Paço et al. 2009) using big data capabilities such as those found in a supply chain. Within this chapter, the term “product” represents a general class of commodities for sale; it could be a manufactured good (e.g., a bar of soap), a service (e.g., banking), or a combination of both (e.g., an automobile with service warranties). A distinction is also made between the terms “customer” and “consumer.” A consumer is the end-customer, who buys the final finished product. On the other hand, a customer could be any supply chain member; a retailer is a customer to the manufacturer, so is a manufacturer to a raw material supplier. In other words, while customers engage in a business-to-business (B2B) relationship, a retailer for example, is in a business-to-consumer (B2C) relationship. The term, “sustainability,” is the avoidance of the depletion of natural resources, such that an ecological balance is sustained. Sustainability in business is generally coined with the term, triple bottom line, TBL (Elkington 1998). TBL approach includes the financial (profit), environmental (planet), and social (people) performance measures that impact an enterprise. Evidently, the markets for sustainable products such as organic fresh produce and fair-trade goods have seen dramatic increases in sales in the past decade (Khalamayzer 2017). The growth of sustainability labels on products is a major sign of the ideas increasing popularity, empowering consumers to make more sustainable choices (Van Loo et al. 2015). According to Sachs (2015), Sustainable development is both a way of looking at the world with a focus on interlinkages of economic, social and environmental change and a way of describing our shared aspirations for a decent life, combining economic development, social inclusion and environmental sustainability.

While past research has evaluated consumer’s attention to product information before purchase (e.g., Ülkü and Hsuan 2017), not much is known regarding the factors involved in formulating a demand for the sustainability of a product, and how recent changes in shopping behavior and logistics capabilities can be molded into a supply chain framework. This chapter aims to address this gap in research by carefully reviewing scholarly publications focused on the intersection between consumer’s reasons for choosing a product and sustainability issues. Behe et al. (2015) noted that highly involved consumers exhibited greater fixation on product information compared to consumers with lower product involvement. This research will also investigate whether consumers with this high involvement are more likely to make purchasing choices based on the sustainability information.

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Transparent, complete, and detailed information on products (from sourcing of the raw supplies to the delivery of the finished goods to the consumer), unfortunately, or perhaps strategically, is rarely communicated in an effective manner. Thus, the consumers are given no or insufficient information to understand sustainability policies or actions of the company (and its links in the supply chain) that manufactured and distributed the product (Hill and Lee 2012). In a worst-case scenario, a company may make unsupported claims about their product or its content. For example, unlike the food industry, there are no laws requiring companies that manufacture personal care products to meet specific standards before labeling a product as “natural” or “organic” (Beerling and Sahota 2014). Conversely, some companies are revising their business models to match sustainability requirements while still managing to generate profits. Because of their sustainability-related activities, they have been referred to as Sustainability-Driven Innovators (Kiron et al. 2013). Sustainability characteristics for many products are credence attributes. This means that products have attributes that are not directly observable by consumers before purchase and cannot be experienced after purchase (Van Loo et  al. 2015). Such credence issues make it very difficult for consumers to assess the utility of the product. However, consumers may resort to using different factors in their choices of sustainable products. Simply labeling a product is not sufficient to influence a decision; consumers seek involvement with a product. Zaichkowsky (1985) defined involvement as “a person’s perceived relevance of the object based on inherent needs, values, and interests.” Such involvement influences the type of information sought by consumers for their purchasing decision process (Laaksonen 1994). It generally includes factors such as price, utility function, quality, support services, and return policy. The aim of this research is to explore how these factors affect consumer choice behavior when purchasing sustainable products. For this research, a thorough review of several broad databases was conducted to identify scholarly publications related to sustainability and consumer-purchasing choices. This comprised three main steps: database searches, article selection, and content analysis of the selected articles. Through extensive research, key terms related to consumer demands and sustainability were used as filters to obtain relevant articles. Selected articles were then critically examined to ascertain their fit by factor (price, utility function, quality, services, and return policy) and relevance to sustainability. Figure 7.1 shows the number of publications, as of June 20, 2020, related to the keywords chosen for this study, namely, “sustainability,” “supply chain,” “consumer behavio(u)r,” and “big data”. Each pie-chart displays the percentage of those publications ( journal articles, books, theses, and other citable works; cases, lawsuits, and patents excluded) with respect to the segmented decades (1990–1999, 2000–2009, and 2010–2019) with the total number of publications indicated below the pie chart. For instance, in the last three decades, more than 4.5 million publications included the keyword “sustainability,” with 95% of them almost equally divided between the last two decades. The next largest number of publications (876,000), in comparison with the selected four keywords, relates to “supply chain” where 60% of the publications in the domain of supply chain belong to 2010–2019. “Big data” resulted in about 493,000 publications, 96% of which date between 2010 and 2019. Finally, the word “consumer behavior,” in

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FIGURE  7.1 date range.

Keyword-based publication searches in Google Scholar, within 1990–2019

FIGURE 7.2

Joint search of keywords, for the date range 1990–2019.

American English, or “consumer behaviour” in British English, appeared in almost 50,000 publications in the last three decades, the majority of which (72%) have been published since 2000. These numbers support the fact that the prevalence of technology (particularly, Internet and cloud computing) has increased in our daily lives, impacting the consumers’ changing demand on the products (e.g., greener products) and the way they purchase (e.g., through omnichannel retailers). These observations form the basis for Bartels and Onwezen (2014) claiming that different streams of literature are increasingly focusing on sustainable consumer behavior. Results of the search for article that included all the keywords (sustainability, big data, consumer behavior, and supply chain) are presented in Figure 7.2. These results indicate that in the last decade, there was a sharp increase in the number of publications that jointly studied or mentioned all four of the preceding keywords in their texts. This chapter contributes to literature by first proposing a renewed understanding of consumer purchase decision attributes toward sustainable products, and then by offering a generalized supply chain framework in which those attributes are linked to supply chain operations, product/process/consumer data generation, and

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sustainability goals. A systemic literature review is conducted for the concepts used in building the framework, where for brevity and clarity, referencing to the pertinent literature is done throughout the text, rather than condensing them in one part. Next, in Section 7.2, the attributes consumers would most likely consider in their purchases are evaluated, and the cross-effects of those attributes, such as the intricate relationship between the quality of the product and the information transparency, are discussed. Then, we introduce our supply chain framework in Section 7.3. This framework provides a holistic view of the interactions among and the data generated by the consumers, supply chain partners (i.e., raw-material supplier, manufacturer, distributor, retailer) and the strategic goals of sustainability. Finally, concluding comments are presented in Section 7.4.

7.2 ATTRIBUTES IMPACTING CONSUMER’S PURCHASING BEHAVIOR We now focus on the accompanying attributes (factors) that consumers would consider in their decisions when purchasing a sustainable product, a product that has been manufactured or rendered with TBL considerations; responsibly sourced-made-delivered, environmentally friendly, and socially beneficial. To narrow down the number of articles, additional keywords/phrases were used, including demand and sustainability, consumer green demands, circular economy, fair trade products, green and organic products, and eco-labeling/designs. After successive searches, 80 scholarly papers that met the criteria were extracted. The content of these articles was analyzed and evaluated to gain understanding of the accompanying decision factors consumers use when purchasing sustainable products. These selected papers were subjected to critical appraisal and evaluation. Each article was reviewed to determine its fit within one of the five categories (purchase price, derived utility, product quality, product support services, and returns policy) and relevance to product sustainability. Table 7.1 lists 20 of these papers chosen as the most supportive examples for our discussions in this review. Consumer behavior, an interdisciplinary branch of the field of marketing, emerged in the mid-20th century and keeps evolving with the changing dynamics of consumers, and the products. The study of consumer behavior focuses on the consumer’s emotional, mental, and physical responses for pre-, during, or post-purchase, use, disposal, or return of the product. Each buying factor category (price, utility, quality, services, and return) is presented along with a detailed description of its relevance to the research question.

PURCHASE PRICE Purchase price is perhaps the most influential decision attribute for the majority of consumers. However, there is a “value-action” gap (Young et al. 2010) when it comes to purchasing green products: many consumers are very concerned about environmental issues, but they are reluctant to purchase greener products. While many underlying factors influence the price of a product, many of the selected articles that were reviewed, justifiably, focused on consumers’ willingness to pay in relation to

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TABLE 7.1 Sample Articles Categorized by Buying Factors Publication Enger and Lavik (1995) Florida (1996)

Schlegelmilch et al. (1996) Emmons and Gilberts (1998) Thøgersen (2000) Jarvenpaa and Staples (2000) Roe et al. (2001) Tibben-Lemke (2002) Evanschitzky et al. (2004) Yao et al. (2005) Choi and Parasa (2006)

P

U

Q

S

R

 



 

  

  

 







Dornfeld and Wright (2007)



Al-Salem et al. (2009)



Owusu (2013)



Young et al. (2010)



Bartels and Onwezen (2014) Scholz et al. (2015)



Ülkü and Hsuan (2017)











Notes Studies consumers’ attitudes toward eco-labeling in Norway and their perception of quality goods. Studies the creative responses that manufacturers have come up with to improve product utility and at the same time reduce production costs. Studies the link between green purchasing decisions and the measures of environmental consciousness. Examines the effects of return policies on the entire supply chains profits. Studies the importance of labeling and its impact on quality and consumers’ decision. Talks about the issue of the media as a means of information sharing to consumers. Analyzes green electricity in the US and suggests that a wide array of product features cause price premiums. Studies consumer’s return policies and the effect on the quality of the products in question. Studies consumer’s price knowledge in the German market and the benefits of discounts and promotions. Studies the effect of return policies on manufacturers and retailers’ upstream dynamics. Demonstrated that many customers are still unwilling to pay premiums for green products, a sign that consumer sensitivity should be considered. Highlights companies that followed the concept of “green business” and policies they put forward regarding the concept. Studies the recycling and recovery routes for plastic solid waste as a service to the community and environment. Takes quality as a different concept when it comes to tangible products versus the quality of services. Emphasizes the value of product information and green labeling in purchase decisions, and impact of purchasing decisions on sustainable consumption patterns. Examines consumers’ willingness to pay for food products claiming ethicality and greenness. Elaborates on different estimation methods used to estimate the utility of a product with some proving superior and others having shortcomings. Highlights the competitive pricing of modular products for green consumers as the concern for sustainable development is peaking. (Continued)

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TABLE 7.1 (Continued) Sample Articles Categorized by Buying Factors Publication

P

U

Ülkü and Gürler (2018)





Bhavsar et al. (2020)





Q

S

R

Notes

 Studies the impact of consumer behavior of deliberately taking advantage of return policies on stock management  Investigates how fair-trade products can be better integrated to retail stocks for catering green consumers and sustainment and well-being of suppliers in developing countries.

P, purchase price; U, derived utility; Q, product quality; S, support services; R, return policy.

the purchase price (e.g., Choi and Parasa 2006; Ülkü and Gürler 2018). For example, Bartels and Onwezen (2014) noted consumers’ growing awareness of ethical concerns related to the environment and society. They conducted a study on consumers’ willingness to buy food products that make environmental and ethical claims. Participants provided demographic information (mainly education and income levels) and completed the online questionnaire. Their results demonstrated that consumers who fully supported sustainable agriculture and had the financial means were more willing to buy environmentally friendly and ethical products. In another study on sustainability and pricing, Ülkü and Hsuan (2017) highlighted consumers’ escalating concerns for unsustainable product development and the need for competitive pricing on modular products for green consumers. Similarly, Chen and Liu (2014) studied the  theoretical and empirical analysis of pricing and design decisions for green products in a market that contains nonenvironmentally conscious products (brown products). they concluded that both environmentally conscious (green) and nonenvironmentally conscious (brown) consumers, under price leadership, play a role in product quality and pricing. Using a quality-based approach, Owusu (2013) also studied consumers’ perception of quality in relation to price while Chang and Wildt (1994) suggested that product features and price are major decision variables influencing purchase behaviors. In fact, Smith and Nagle (2002) concluded that consumers are willing to pay if they receive value or benefit from using a green product versus a cheaper competitive substitute. Erickson and Johansson (1985), however, stated that the price of a product is viewed as a constraint as it results in a reduction of consumer wealth despite its signaling of the product quality. According to Poelman et al. (2008), credence attributes are usually close to impossible to identify, and if identified on a product, their measurement has a very high cost, as it frequently requires previous analysis or appraisals. For example, “fair trade” and “organic” are recognized credence attributes that are also associated with high price premiums. An analysis of green electricity in the United States (Roe et al. 2001) suggested that a wide array of product features were the main causes of price premiums. The premium price structure, however, can be explained by the use of newly invented renewable energy generators to supply power as well as by other credence attributes

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via required certifications, licensing, and brand name registration. Unfortunately, as evidenced by other research findings, some corporations charge excessive prices for green products in an unjustified and nontransparent way, which discourages consumers from becoming involved in buying green products and in sustainable consumption (Koufmann et al. 2012). According to Choi and Parsa (2006), many customers are hesitant to pay premiums for environmental-friendliness, a sign that perhaps the pricing of these products is not sensitive to consumers’ expectations and ability to pay additional costs for green initiatives. Borden and Francis (1978) noted that people are more likely to act ecologically when they have the resources (time, money, and energy) to deal with social and proenvironmental issues and can pay the price premiums that come with sustainable goods. While Degeratu et al. (2000) found that consumer price sensitivity was linked to household income, Tsen et al. (2006) showed that there is a positive relationship between consumer’s willingness to pay for environmentally friendly products and their attitudes, behaviors, and values. Customer value-based pricing has been linked to perceived customer value of the product as the final price determinant (Hinterhurber and Liozu 2012). However, Kang et al. (2012) disagreed, noting that the relationship between consumers’ perceptions of corporate social responsibility and their purchasing behaviors is not fully known, because consumers do not have the necessary information to compare ethical practices to products. Roe et al. (2001) concluded that the success of green products is determined by how they perform during market upheavals. If green offerings could survive when household electricity bills were spiraling upward and consumers were seeking cheaper alternatives, then consumer-driven purchases are inevitable. Another factor that impacts the (strategic) consumers’ purchasing decisions is temporary price reductions through seasonal promotions and discounts. Consumers consider their budget in buying and want to maximize the value they get from their purchases. It is very likely that what the consumer “wants” to buy (e.g., a sustainable product) will be different than what “can be afforded,” for example, nonorganic food at a cheaper price (Evanschitzky et al. 2004). In such cases, a discount on the organic food may be regarded a service to not only consumers but to the environment as well. According to Aschemann and Zielke (2017), many European countries have set a goal to increase organic farming which in turn should increase consumer demand for organic food consumption. This is possible because the higher the supply, the lower the price, and thereby, the higher the demand, building toward a market equilibrium. Aydinliyim and Pangburn (2012) demonstrated that leveraging discounts and reducing packaging, as important steps in reducing the product’s carbon footprint, may drive both demand and profit. Tseng (2016) also studied the effect of price discounts on green consumerism, by giving the example of reducing disposable cup use at coffee shops, stating that The major difference in buying take-out beverages from a green setting and a general setting is based on whether consumers are willing to make the additional efforts to bring their own cups, which is considered a perceived non-monetary sacrifice.

Perceived sacrifice, derived from the consumers’ perception of both monetary and nonmonetary sacrifice, is what must be given up or paid to exhibit a certain behavior.

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As discount-pricing may influence consumers toward sustainable consumption, it was included as a potential purchasing attribute in our analysis.

DERIVED UTILITY The concept of utility (fitness for use, worthiness, or value) is based on relatable, global economic contributors. Economists have generally portrayed the creators of economic values as providing time (the product is delivered to the consumer at the right time), place (the product is available at the right location), possession (the amount of usefulness or perceived value from owning a product), and form (how well the product meets the customer’s needs) utility to consumers. For a product to possess utility, it should be judged based on its value and/or usefulness. In a classical sense, the utility function incorporates all four utilities as a proxy to consumer demand patterns, actual purchases, and sustainability. According to Scholz et  al. (2015), there have been numerous methods applied to estimate the overall utility of a product, but they all appear to have shortcomings with respect to either accuracy or consumer effort. Such discrepancy may indicate a lack of trust between the seller and the buyer. As a remedy, sellers should develop and communicate social and environmental plans with short- and long-term goals that are ethical, sound, and manageable. Such actions can be applied to companies manufacturing various products, therefore increasing their value and utility. For example, form utility is generally associated with production and manufacturing of products in a way that is of use to and valued by customers (Scholz et al. 2015). Winsor et al. (2004) related this concept to “goods” retailers, who make significant modifications to the products they sell to consumers. With environmental issues in manufacturing receiving increasing attention, many consumers would prefer to uphold global environmental values. According to Dornfeld and Wright (2007), the topic of “green business” has gained popularity with companies and consumers, emphasizing the need for greening of not only the manufacturing process but the whole supply chain. More than a decade earlier, Florida (1996) had considered manufacturing practices and innovative approaches that were environmentally sound as increasing product utility and value. He noted that firms who were innovative in terms of their manufacturing processes were also more likely to address environmental issues across the product life cycle and forge close relationships between end users and suppliers. The onus, he stated, was on companies to adopt green design and production strategies to address consumer demands while improving product utility, value, and quality. Greater understanding of form utility and its importance to the growing number of green consumers is further discussed by Chahal (2012), who focused on the concept of lean manufacturing as a way of producing high-quality products. This would be done by continually identifying and eliminating waste and by only producing what is needed when it is needed. The relationship between operational practices and performance among early adopters of green supply chain management was examined by Zhu and Sarkis (2004). Recently, many companies in varying industries seem to embrace the value of form utility and are coming up with inventive ways to win over consumers not only with the products themselves but also introducing by

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biodegradable/ recyclable packaging and containers. Livingstone and Sparks (1994) and others (e.g., Gerschenson et al. 2017; Van den Oever et al. 2017) have reported on various green and/or recycling packaging options that have increased a product’s status as well as utility. Finally, place utility is another important aspect going hand-in-hand with time utility. This can be defined as having products available where and when they are needed by consumers. Products are usually moved from points of lesser value to points of greater value. We cannot expect consumers to leap to the idea of a sustainable future if the products driving this idea are not readily available to them. As pointed out by Wackernagel et al. (2006), “cities and regions depend on resources and ecological services from distant ecosystems. The well-being of a city and region’s residents is affected by both the health and availability of these ecosystems, especially in today’s ecologically strained world.” A good example would be the situation in Kenya. According to Nzila et  al. (2012), biogas production would be the main driver toward ending energy poverty in the rural areas as well as set the pace for a reduction in energy consumption in Kenya’s capital, Nairobi. However, consumers cannot be expected to quickly take to this technology if it is not readily available. This is where we think about how we can provide environmentally friendly technologies, especially in areas that contribute to the vast amount of atmospheric pollution. Biogas is also promoted in other developing countries in Africa, Asia, and South America. However, a sustainable development requires investment in infrastructure and market conditions enabled for easy consumer access to the product by the consumers. Otherwise, the knowledge of an existing solution will not suffice and not serve the purpose.

PRODUCT QUALITY Product quality in a broad sense is defined as the superiority or excellence of a product (e.g., Reeves and Bednar 1994). There are, however, two major problems with this definition: First, it ignores the fact that a product’s quality can vary greatly, from a range of poor or unacceptable to superior. The second problem is the intrinsic element of subjectivity which is used to determine where the quality of the product lies, within what range, and how it is oriented. In a customer-driven organization (a for-profit company), quality is established with a focus on satisfying or exceeding the requirements, expectations, needs, and preferences of customers. The meaning of quality also differs depending upon circumstance and perception. According to Owusu (2013), “Quality is how the recipient of the product or service views the product or service before buying, upon delivery and after the delivery/use. Quality is satisfying a customer and it is defined by a customer.” Many would look at a product and immediately connect quality to aspects such as color, material, texture, brand name, packaging, price, labels, or even just the product’s online reviews. Owusu (2013) suggested that the quality of tangible products is also time-based or situational. Macdonald and Sharp (2000) agreed and added that brand awareness for specific products affected consumer choices due to their perceived quality, e.g., “I’ve heard of the brand, so it must be good.” Likewise, Chang and Wildt (1994) stated that perceived quality is positively influenced by intrinsic product attribute information

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such as perceived price and value. Although some may assume that because a product is expensive, it is automatically of good quality; Sutton and Riesz (1979) cautioned that the price–quality relationship for certain consumer goods is often not positive. According to Sutton and Riesz (1979), it is apparent that the experience of value, or the relationship between quality and price, may be less relevant for product categories in which a consumer’s self-worth may be elevated through the act of purchase. Self-worth in this case would be referring to our focus on purchasing sustainable products. Thorgesen (2000) made it clear that Knowing a label is a prerequisite for using it in decision making and understanding it is a prerequisite for using it correctly. Understanding a label implies that the person knows it exists, what it looks like, and what it means.

To do this, a consumer needs to be able to distinguish between the concepts of eco-labeling, and an unethical corporate practice, termed, “green washing.” With increased environmental awareness, especially after the 1987 Sustainable Development Goals signed off by the United Nations (Brundtland, 1987), supply chains have recognized the growing value of promoting their products and companies as being green to attract a growing environmentally aware market segment (Furlow 2010). Awareness of the power of marketing green products has also given rise to unscrupulous business practices with some products being falsely labeled or promoted as eco-friendly. This increase in greenwashing is having a profound negative effect on consumer confidence in green products (Delmas and Burbano 2011; Nidumolu et  al. 2009; Ramus and Montiel 2005; Lim et  al. 2013). Companies in most countries are not legally required to publish their environmental policy statements; however, some do it voluntarily (Ramus and Montiel 2005). Tobler et  al. (2011) and Van Loo et  al. (2015) acknowledged the fact that consumers are more willing to purchase and consume sustainable products, beginning with eating green. Thogersen (2000) and others (Mackenzie 1991; Eden 1994; Enger and Lavik 1995; Schlegelmilch et al. 1996) have also found that labels and other types of environmental information provided by independent or public sources are trusted more than when provided by the producers and retailers themselves. In the food industry, for example, product transparency has been of importance to consumers, because it is a direct indication of food quality (Amador and Emond 2010; Bechini et al. 2008). In fact, food transparency has become a legal obligation in many jurisdictions, and companies are strategically investing in transparency in order to promote public confidence in their products and in the quality of their products (Musa et al. 2014). Morris and Bronson (1970) noted that consumers who engage in random, haphazard purchasing of products were likely to lose money, especially on expensive, nondurable goods that create an illusion of quality. Product transparency creates a window for consumers to know where their products are coming from, who is providing them, how they are transported, and most importantly, what went into their production and manufacture. Transparency is an issue at all levels of the supply chain. Supply chain product transparency may be defined as “the ability to have a view of a product’s life cycle in terms of conception, manufacturing, distribution, delivery to the end consumer, consumer’s experience and the products end-of-life activities and

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processes” (Musa et al. 2014). Basically, this is the quality of tracking and sharing the information about the product based on how each of these activities is carried out and possibly where and by whom. In recent years, there has been a wide range of product recall announcements ranging from food products to nonfood products, many outsourced. According to Lin (2011), this is because the per capita income in developing countries has grown to middle income from poverty level and the countries’ rapid industrialization. Tse and Tan (2012) pointed out that higher percentage of recalls for products manufactured in China is not surprising given the large amounts of exports from China, high consumer demand, complexity of the supply network, and extensive global sourcing. The magnitude of exporting by China increases the likelihood of quality issues with products occurring. If not recalled soon enough, some faulty products may damage health, property, and the environment, resulting in a bad reputation for the end seller, e.g., the manufacturer. Such incidents may create distrust among consumers and raise awareness of the need for ongoing transparency. However, a win-win solution exists if the supply chains develop innovative and sustainable products and processes. Some good examples of industries/products with sustainable strategies and solutions exist and more are expected to join into this effort. For example, the petroleum industry is in transition to replace petroleum products with more renewable and carbon neutral biofuels, thus avoiding issues such as oil spills that contribute to marine destruction and pollution (c.f., Poulsen and Lema 2017). Likewise, the textile industry, heeding to the calls for sustainable clothing, has introduced the concept of eco-fashion, a sustainable way of textile production that emphasized reuse, recycling, and zero waste practices. However, especially in the fast fashion industry, there seems to be reluctant uptake in purchasing sustainable apparel due to conflicting consumer expectations and behavior (McNeill and Moore 2015).

PRODUCT SUPPORT SERVICES To attract more customers, sellers (suppliers, manufacturers, or retailers) often offer products that come with services attached to the product, such as free product warranties, product delivery tracking information, ease of recycling, or travel insurance. These services come in different forms, at different times, and can be provided in various ways, either in-person or remotely. Naturally, these support services affect consumer’s product choices, especially those credence goods, such as sustainable products (c.f., Howells 2004). Product support services may also have cross-effects on the purchasing decision attributes. For example, Bindroo et al. (2020) reported that consumers’ behavioral intentions lean toward choosing product quality over service. A direct managerial implication of this is to realize that quality improvements in the product should be complemented, not substituted, by improvements in service quality. Having nuanced the complexity of cross-effects of purchasing factors for sustainable products, next we consider two other attributes that we categorize as parts of support services to accompany: ease of access to product information and special product end-of-life services such as recycling. Almost all consumers conduct searches to learn about the product they want to purchase, or to compare with other competing substitutes. The Internet, main

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enabler for this capacity especially for the products sold online, may offer the searching-consumer (the shopper) more information than traditional outlets such as mail-in catalogs, flyers, newspapers, or word-of-mouth would provide. There is no doubt that the Internet is essential to contemporary buying, and hence, a vital touch point for companies, especially e-tailers and retailers utilizing omnichannel strategies, to sell their products. At the outset, and as expected (Burke, 1997), home shopping has been convenient and profitable, and recently, a necessity during the COVID-19 pandemic lockdown. Consumers’ preference for online shopping inevitably has shifted the supply chain’s dominant power mostly from manufacturers to retailers. This has resulted in new types of logistical challenges and opportunities pertinent to consumer’s time and place utilities. Companies have been using the Internet to provide detailed information on their products, information that does not fit on a label. Also, in this digital age, company websites help inform, advertise, and signal confidence in their products through transparency. As discussed in Jarvenpaa and Staples (2000), collaborative electronic media is an effective means for information sharing. This is particularly true for the younger populations (generations Y and Z, a.k.a., “digital natives”) who are concerned with natural environment, sustainability, and social justice. Celebi (2015) noted the positive motivations of young individuals toward the Internet as well as their positive attitudes toward advertising and research. Selwyn et  al. (2003) also found that older adults (generations preceding GenY, a.k.a., “digital immigrants”) who used information and communication technology in everyday life shared information on sustainable and healthy products. As Young et al. (2017) posited, social media could be used to influence consumption behavior such as in reducing household food waste. Information campaigns and laws by central and local governments have helped change consumer habits and attitudes. Another important issue that sustainability-aware consumers are vigilant about is how companies handle end-of-life products as a value-adding support service, in enhancing the sustainability of the product throughout its life: Do they have effective sustainability programs that involve consumer engagement? Do they incentivize recycling? Do they have an effective recycling program? Of course, such programs require compliance with governmental regulations. Provision of urban infrastructure such as recycling bins and legal structures like vehicle emission-related taxes and incentives such as renewable energy and technology subsidies are resulting in greater sustainability strategies (Auld et al. 2014).

RETURN POLICY Consumers may return their purchases for a variety of reasons such as the product being the wrong size or color, malfunctioning, or simply because it does not fit their expectations or tastes. A return policy, which specifies the conditions of the return process (by whom, to where, return period, refund options, returning costs, return time window, etc.), acts as a risk reliever for the customer which may enhance market demand. It is also the main reason why many consumers comfortably purchase products they have never used or sometimes have never even heard of without hesitation. Conversely, if not managed effectively and efficiently, lenient return policies may not

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only hurt the company’s financial bottom line (Ülkü et al. 2013), they may also cause negative consumer reactions against sellers if returns are denied (Dailey and Ülkü 2018). For the supply chain partners, returns generally relate to the movement of excess stocks from retailers toward upstream channel members (Padmanabhan and Png 1995). The conditions of those commercial returns are generally included in the contracts and warranties in such B2B markets. As mentioned earlier, products can be returned for many reasons. Besides verifiable reasons such as malfunctioning or wrong color, there may nay be other reasons such product might have turned out to be different than advertised (e.g., erroneous information); the product might have arrived damaged (e.g., improper handling during shipment or poor quality); or the consumer might simply have changed his or her mind after the purchase (c.f., Ülkü and Gürler 2018). At the end of it all, many producers and retailers end up with used/unwanted items that they have to find a way to resell or recycle, or in a worst-case scenario, it becomes waste in landfills. If there were no returns in the first place, it would be safe to say that the introduction of sustainable products would not do as well because no one would be willing to take the risk. Return policies are all-in-all beneficial to the entire supply chain and in the long run, the environment, if the reverse logistics is handled efficiently and sustainably. Most studies on return policies within the supply chain have investigated the upstream dynamics between the manufacturers (suppliers) and the retailers (buyers). For instance, Yao et al. (2005) claimed that return policies encouraged retailers to order more of a product, especially those with short life cycles such as books, compact discs, and computers. Emmons and Gilberts (1998) examined the effects of a return policy on the manufacturer’s and retailer’s profits and found that a return policy could increase both parties’ profits and eventually those of the supply chain. Similarly, Choi et al. (2004) studied the whole two-stage supply chain and found that a manufacturer could sell returned products online and at even higher prices. For perishable products, Pasternack (1985) showed that full credit/refund returns, or no credit returns for all unsold goods are suboptimal, while partial credit returns can achieve channel coordination. Only a few studies have investigated the downstream relationship between suppliers and consumers, which is important in terms of consumer returns. In many businesses, consumers have the right to return the products they purchased. With the variety of shopping and buying options in an era of mass-customization, consumers expect to be able to return products as easily as they bought them. As such, a retailer’s return policy would be considered a significant factor in any purchase decision because consumer returns are unlikely to be in new condition, unlike products returned by retailers to manufacturers (Tibben-Lembke 2002), it is imperative for retailers to develop a better system for consumer returns. To that end, for example, Dailey and Ülkü (2018) studied the postpurchase, returning behavior of consumers. Results of their study revealed that positive interactions between the consumer and the service staff while returning the product were paramount. If the product return was denied, the process could have a negative impact for the retailer in the form of a fraudulent return, bad word-of-mouth, and switching the patronship to another competing retailer.

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SUMMARY In summary, there are many underlying factors that are important to attaining a sustainable future. This content analysis addressed decisions made by consumers when purchasing sustainable products as well as consumers’ pre- and postpurchase behaviors. Results of a study by Kim et al. (2014) captured the conundrum faced by consumers. They collected data from 300 consumers who indicate that “they find products more desirable when they perceive that a firm used green management systems, engaged in resource recovery efforts, and behaved in socially responsible ways.” According to Bell (2011), specific types of emotions such as sequential emotions, satisfaction emotions, and negative emotions are involved in the purchasing process. All these emotions can be linked to one’s desire to have a sustainable future. However, choosing a product is complicated when consumers are faced with an increasing number of alternatives and uncertainty about product origins, content, sustainability, environmental impact, and values (see, Bettman et al. 1998, Kaufmann et al. 2012).

7.3 A BIDIRECTIONAL SUPPLY CHAIN FRAMEWORK In our current complex and interdependent global economy, it is very rare that a company that exists in isolation can survive in the marketplace. Increasing world population, ever-changing consumer demands, and increasing risks of supply disruptions due to environmental degradation and social instabilities pose challenging problems that require innovative solutions. Therefore, developing a holistic view of sourcing, production, distribution/delivery, and recovery of products is a crucial first step. To that end, we propose in Figure  7.3, a bidirectional supply chain framework (BSCF), in which, without loss of generality, the perspective of a manufacturer, the “focal company” is taken. Note that the focal company is the closest upstream supplier of a “retailer,” which acts as intermediary between the manufacturer and the consumers and operates in an omnichannel environment (brick and mortar, online, or a mix of both, at varying stages of order fulfillment and pickup). The focal company’s eminent upstream (Tier-1) supplier is the customer of the Tier-2 supplier as the number of upstream suppliers is high, which makes it a “long” supply chain. Regarding this

FIGURE 7.3 A BSCF integrating TBL and consumer-purchasing factors.

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construct, Yang et al. (2009) stated that “as supply chains are extended by outsourcing and stretched by globalization, disruption risks and lack of visibility into the supplier’s status can both worsen.” Shortening the supply chain, if possible, has the advantage of having fewer impacts of double marginalization (i.e., additional mark-ups imposed by the intermediary agents in the supply chain increase the purchase price as seen by the consumers) and the bullwhip effect (i.e., increasing variability in order sizes higher up in the supply chain). Unlike the conventional supply chain models in which the information flows from the consumers toward the suppliers upstream and the materials (raw supplies, parts, assembled components, finished goods) flow in the opposite direction (downstream), to reflect the alignment opportunities with big data technologies and the sustainability canvas, our proposed framework considers the flow of both information and materials bidirectional. Once the customers give their orders (information moving up the chain), they can track the status of the orders (information moving downstream). Having received the orders (forward logistics), the consumers may be able to return the product to, say, the retailer, or they may return the product at the end of its life for recycling (reverse logistics). Moreover, the bidirectionality in our framework emphasizes the dynamic cocreation of products (goods plus services) and “values” by both consumers and producers (c.f., Sampson 2000, Wilkinson et al. 2009). Zhang et al. (2019) noted that consumer involvement in product design already exists, especially with mass customization. Consumers, who are the driving force for the business of the supply chain, have varying needs and expectations from the product. As such, they factor in various attributes in their purchasing-decision process: price, utility, quality, service, and return policy. Note that for one consumer, price may be the only driver, while for another customer, all of the attributes may be at work with varying decision weights. That is, consumer heterogeneity is a better reflection. Consumers may utilize mobile technology, or visit a physical store, to research information about and to place their orders. Once the order is received by the seller, they may acquire or be notified about the delivery status of the shipment. Online shopping was already on an upswing for the recent decade, and it became a standard practice during the COVID-19 pandemic lockdown, which may further increase online shopping habits, postdisruption. The BSCF is an adaption of the celebrated Supply Chain Operations Reference (SCOR) model which is a strategic planning tool applicable to supply chains across all industries (see, Huan et al. 2004) and integrates resources, processes, and key performance metrics. The SCOR model builds on plan, source, make, deliver, and return modules. We emphasize in our framework that not only the focal company but also its upstream and downstream (retailer) focus on the triple bottom line (TBL) sustainability objectives in the “planning” stage, so that those objectives are carried to other operational modules. For example, if the manufacturer is responsible for collecting the returned products and recovering value, it should gauge in its strategic plan and the “return” module how this could be achieved in the most efficient (economic), greenest (environment), and socially responsible way. All the interactions between the supply chain partners, and operations along the chain from raw material extraction to production to delivery to the consumer, and the temporal reports and contracts in-between create massive amounts of data. Add to

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this, the interactions between the consumers, and consumers informing each other about and reviewing the product through social media sites, the amount of data gets bigger. Using big data to better inform supply chain decisions foremost requires the supply chain partners to share in a transparent and timely manner their operations and product data with each other. To achieve this, the partners should work toward achieving a common TBL strategy within their value chain and maintain an information technology infrastructure, the installation, and upkeep costs of which are contractually agreed. Recently, several information technology paradigms such as the Digital Twin, Internet-of-Things, and Blockchain have emerged claiming such supply chain data coordination. Naturally, such innovations bring with them some drawbacks along with potential solutions to big problems. These mostly “energy-heavy” infrastructures, employing massive data storage and near real-time computing, may then defeat the purpose of sustainability. As well as studying possible reasons as to why supply chains are reluctant to adopt Blockchain technology, Kouhizadeh et al. (2020) noted the extreme cost and environmental burden that Blockchain placed on energy consumption. It is a formidable task to collectively respond by the supply chain capabilities to the complex and intertwined consumer attributes and their resultant demand forms. However, the evolving landscape of sustainable supply chains calls for re-examining the feasibility of centralizations, with the current advents in technology. A sustainable supply chain analytics (Ülkü and Engau 2020) approach would be best suited for sustainably matching supply and demand.

7.4

CONCLUDING REMARKS

In the last few decades, we have witnessed remarkable changes in supply chains, from restructuring of chain members for coordination to adapting a “value chain” management approach to more responsive and efficient logistics system. Whether operational (short term) or strategic (long term), those required supply chain transformations emanate, in essence, from four main trends in the marketplace: globalization and trade facilitation (e.g., Benton et al. 2016), enhancements in mobile technology and computing powers, changing consumer wants and needs, and an increased awareness of the climate change and sustainability issues surrounding it. This chapter has brought a discussion as to what factors can affect consumers behavior toward purchasing sustainable products, and how can this be captured and reinforced with the increasing use of and generation of big data in supply chains. In this chapter, the systematic content analysis of articles addressing consumer decisions to purchase products, including sustainable products, focused on five key factors. The factors examined were price, utility function, quality, services, and return policy. Findings indicated that consumers want products that are safe, environmentally friendly, sustainable, and priced within their means. Consumers also expected those involved in manufacturing products throughout the supply chain to uphold global environmental values, to make necessary products available to all, to recognize the importance of engaging in sustainable practices, and to produce quality products. To do this, manufacturers were encouraged to recognize the challenges

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facing our strained ecosystems, be transparent and communicate product content and manufacturing processes at all levels of the supply chain, and to consider consumers’ needs and concerns. With these findings in mind, a BSCF was proposed that differed from other unidirectional models. The BSCF posits that the flow of both information and material in the supply chain is bidirectional, a continuous interaction. The BSCF also considers the triple bottom line, sustainability, and consumer preferences and needs, creating large data sets. The big data sets that are generated through this framework can be used to assist supply chains in developing effective, sustainable manufacturing processes and understanding consumer decision-making and product needs. While the research process was thorough, not all databases were examined. As well, keywords entered in the search may not have uncovered all relevant articles. Perhaps, using different key words or phrases would have resulted in additional articles focused on consumer decision-making processes when purchasing sustainable products. The key product factors used in this research are well-known and researched. However, other less researched factors may enter into decision-making when choosing a product to buy. In addition, few studies touched on the complex interplay among all the variables examined in this research. Therefore, the future research could examine the existing literature related to consumer purchases with a view to uncovering more variables that impact consumer decisions rather than limiting them to a select, but relevant, few. Studies employing a qualitative research approach to address consumer understanding of the link among the supply chain, sustainability, environmental issues, and their choices are rare. Employing in-depth individual interview or focus group methods to ascertain consumers’ perspectives on their product purchasing choices, the importance of sustainability and environmental issues, and their knowledge of product content and manufacturing practices could produce unique data of the issues noted. In addition, researchers must also ensure that their findings and recommendations reach the public, as having answers and solutions without sharing them is futile. Consumers who continue to demand their wants and needs from the products, have been feeling a need to actively contribute to a sustainable environment (do Paço et  al. 2013). The supply chain framework we proposed is a starting point to identify at what stages or processes of supply chain operations (e.g., in value recovery) consumers’ voice can be further included. Amid all these fast-changing attributes and capabilities, an integrated consumer behavior and its related analytical demand model would be the next step as a research avenue. Another emerging research is related to the trilogy of supply chains, disruptions like COVID-19, and sustainable consumption. As the pressure by consumers on supply chain visibility and access on the whole life cycle of products (from sourcing to recovery) increases, so does the pressure from government. However, for example, COVID-19 has created conflicting goals between policy-makers and sustainability; a good example of this is how the ban on single-use of plastic bags has been revoked, as a precaution to prevent further contagion of the corona virus (see, Silva et al. 2020). Add to this, the exponential use of home-deliveries during the pandemic has brought again the viability of the efforts toward a sustainable world. That is, the changing consumption behavior during the times of disruptions, and thereby, the need to formulate

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innovative public policies, supply chain models, and sustainable logistics systems stand ahead of us as research problems that need immediate attention. Studying the expected salient features of consumption behavior in a circular economy model would be another future research. The amount and depth of the research on sustainability verifies its relevance to consumers, manufacturers, government, and researchers. However, balancing purchasing decisions, personal resources, and sustainability is difficult and hindered by not having a Circular Economy. Peralta et al. (2020) described a Circular Economy as one that is based on the principles of eliminating waste and pollution, developing products and materials for long-term use, and regenerating natural eco-systems. They also noted that a “transition to the Circular Economy seems to be the model of change necessary to achieve a society that is sustainably integrated into the planet.” Working together, governments, manufacturers, and consumers can produce products that embrace the principles of a Circular Economy in a manner that values human health, sustainability, controlled growth, and the environment.

REFERENCES Al-Salem, S. M., P. Lettieri, and J. Baeyens. 2009. Recycling and recovery routes of plastic solid waste (PSW): A review. Waste Management 29(10):2625–2643. Amador, C., and J. P. Edmond. 2010. Evaluation of sensor readability and thermal relevance for RFID temperature tracking. Computers and Electronics in Agriculture 73:84–90. Aschemann‐Witzel, J., and S. Zielke. 2017. Can’t buy me green? A review of consumer perceptions of and behavior toward the price of organic food. Journal of Consumer Affairs 51(1): 211–251. Auld, G., A. Mallett, B. Burlica, F. Nolan-Poupart, and R. Slater. 2014. Evaluating the effects of policy innovations: Lessons from a systematic review of policies promoting lowcarbon technology. Global Environmental Change 29: 444–458. Aydinliyim, T., and M. S. Pangburn. 2012. Reducing packaging waste and cost via consumer price discounts. Decision Sciences 43(6):1063–1089. Bartels, J., and M. C. Onwezen. 2014. Consumers’ willingness to buy products with environmental and ethical claims: The roles of social representations and social identity. International Journal of Consumer Studies 38(1):82–89. Bhavsar, A., C. Diallo, and M. A. Ülkü. 2020. Towards sustainable development: Optimal pricing and sale strategies for retailing fair trade products. Journal of Cleaner Production, (forthcoming) Bechini, A., M. G. C. A. Cimino., F. Marcelloni, and A. Tomasi. 2008. Patterns and technologies for enabling supply chain traceability through collaborative ebusiness. Information and Software Technology 50(4): 342–359. Beerling, J., and A. Sahota. 2014. Green standards, certification, and indices. In A. Sahota (ed.), Sustainability: How the Cosmetic Industry is Greening Up. London, UK: Wiley & Sons Ltd, pp. 217–237. Behe, B. K., M. Bae., P. T. Huddleston, and L. Sage. 2015. The effect of involvement on visual attention and product choice. Journal of Retailing and Consumer Services 24: 10–21. Bell, H. A. 2011. A contemporary framework for emotions in consumer decision-making: Moving beyond traditional models. International Journal of Business and Social Sciences 2(17):12–16. Benton, C. N., M. Napier, and M. A. Ülkü. 2016. On supply chain integration to free trade zones: The case of the United States of America. Global Business Review 17(4): 779–789.

128

Big Data Analytics in Supply Chain Management

Bettman, J. R., M. F. Luce, and J. W. Payne. 1998. Constructive consumer choice processes. Journal of Consumer Research 25(3): 187–217. Bindroo, V., B. J. Mariadoss, R. Echambadi, and K. R. Sarangee. 2020. Customer Satisfaction with Consumption Systems. Journal of Business-to-Business Marketing 27(1): 1–17. Borden, R. J., and J. L. Francis. 1978. Who cares about ecology? Personality and sex differences in environmental concern. Journal of Personality 46(1): 190–203. Burke, R. R. 1997. Do you see what I see? The future of virtual shopping. Journal of the Academy of Marketing Science 25(4): 352–360. Brundtland, G. H. 1987. World Commission on Environment and Development: Our Common Future: Report of the World Commission on Environment and Development. Oxford University. Celebi, S. I. 2015. How do motives affect attitudes and behaviors toward Internet advertising and Facebook advertising? Computers in Human Behavior 51: 312–324. Chahal, V. 2012. An advance lean production system in industry to improve flexibility and quality in manufacturing by implementation of FMS  & green manufacturing. International Journal of Emerging Technology and Advanced Engineering, Certified Journal 2(12): 405–408. Chang, T. Z., and A. R. Wildt. 1994. Price, product information, and purchase intention: An empirical study. Journal of the Academy of Marketing Science 22(1): 16–27. Chen, C., and L. Q. Liu. 2014. Pricing and quality decisions and financial incentives for sustainable product design with recycled material content under price leadership. International Journal of Production Economics 147: 666–677. Choi, T., D. Li., and H. Yan. 2004. Optimal returns policy for supply chain with E-marketplace. International Journal of Production Economics 88: 205–227 Choi, G., and H. G. Parsa. 2006. Green practice II: Measuring restaurant managers’ psychological attributes and their willingness to charge for the “green practices”. Journal of Foodservice Business Research 9(4): 41–63. Dailey, L. C., and M. A. Ülkü. 2018. Retailers beware: On denied product returns and consumer behavior. Journal of Business Research 86: 202–209. Degeratu, A. M., A. Rangaswamy., and J. Wu. 2000. Consumer choice behavior in online and traditional supermarkets: The effects of brand name, price, and other search attributes. International Journal of Research in Marketing 17(1): 55–78. Delmas, M. A., and V. C. Burbano. 2011. The drivers of greenwashing. California Management Review 54(1): 64–87. do Paço, A., H. Alves, C. Shiel, and W. Leal Filho. 2013. Development of a green consumer behaviour model. International Journal of Consumer Studies 37(4): 414–421. do Paço, A. M. F., M. L. B. Raposo, and W. Leal Filho. 2009. Identifying the green consumer: A segmentation study. Journal of Targeting, Measurement and Analysis for Marketing 17(1): 17–25. Dornfeld, D., and P. Wright. 2007. “Technology Wedges” for implementing green manufacturing. Transactions of NAMRI/SME 35: 193–200. Eden, S. 1994. Business, trust and environmental information: Perceptions from consumers and retailers. Business Strategy and the Environment 3(4): 1–8. Elkington, J. 1998. Partnerships from cannibals with forks: The triple bottom line of 21st‐century business. Environmental Quality Management 8(1): 37–51. Emmons, H., and S. M. Gilbert. 1998. Note: The role of returns policies in pricing and inventory decisions for catalogue goods. Management Science 44(2): 276–283. Enger, A., and R. Lavik. 1995. Eco-labelling in Norway: Consumer knowledge and attitudes. In: E. Stø (ed.), Sustainable consumption – Report from the International Conference on Sustainable Consumption. Lysaker (Norway): National Institute for Consumer Research (SIFO), pp. 479–502.

Supply Chain Framework

129

Erickson, G. M., and J. K. Johansson. 1985. The role of price in multi-attribute product evaluations. Journal of Consumer Research 12(2): 195–199. Esper, T. L. 2020. Supply chain management amid the coronavirus pandemic. Journal of Public Policy & Marketing. doi:10.1177/0743915620932150. Evanschitzky, H., P. Kenning., and V. Vogel. 2004. Consumer price knowledge in the German retail market. Journal of Product and Brand Management, 13: 390–405. Florida, R. 1996. Lean and green: The move to environmentally conscious manufacturing. California Management Review, 39(1): 80–105. Furlow, N. E. 2010. Greenwashing in the new millennium. The Journal of Applied Business and Economics, 10(6): 22. Gerschenson, L. N., R. Jagus., O. Resa, and C. Patricia. 2017. Biodegradable packaging applied to dairy products. In: F. Conto, M. A. Del Nobile, M. Faccia, A. V. Zambrini, and A. Conte (eds.), Advances in Dairy Products. Hoboken, NJ: John Wiley & Sons Ltd, pp. 328–339. Hill, J., and H. H. Lee. 2012. Young generation Y consumers’ perceptions of sustainability in the apparel industry. Journal of Fashion Marketing and Management: An International Journal, 16(4): 477–491. Howells, J. 2004. Innovation, consumption and services: Encapsulation and the combinatorial role of services. The Service Industries Journal 24(1): 19–36. Huan, S. H., S. K. Sheoran, and G. Wang. 2004. A review and analysis of supply chain operations reference (SCOR) model. Supply Chain Management: An International Journal 9(1): 23–29. Ivanov, D. 2020. Viable supply chain model: Integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic. Annals of Operations Research. doi:10.1007/s10479-020-03640-6. Jarvenpaa, S. L., and D. S. Staples. 2000. The use of collaborative electronic media for information sharing: An exploratory study of determinants. The Journal of Strategic Information Systems 9(2–3): 129–154. Kang, K. H., L. Stein., C. Y. Heo, and S. Lee. 2012. Consumers’ willingness to pay for green initiatives of the hotel industry. International Journal of Hospitality Management 31(2): 564–572. Kaufmann, H. R., M. F. A. K. Panni, and Y. Orphanidou. 2012. Factors affecting consumers’ green purchasing behavior: An integrated conceptual framework. Amfiteatru Economic Journal 14(31): 50–69. Kim, B., K. Park, and M. Swink. 2014. Consumers’ preferences for facets of green supply chain management. International Journal of Services and Operations Management 18(1): 74–98. Kiron, D., N. Kruschwitz, M. Reeves, and E. Goh. 2013. The benefits of sustainability-driven innovation. MIT Sloan Management Review, 54(2): 69. Khalamayzer, A. 2017. Unilever rises above the regs on chemical transparency. Retrieved from https://www.greenbiz.com/article/unilever-rises-above-regs-chemicals-transparency. Kouhizadeh, M., S. Saberi, and J. Sarkis. 2020. Blockchain technology and the sustainable supply chain: Theoretically exploring adoption barriers. International Journal of Production Economics, 231: 107831. Kraft, T., L. Valdés, and Y. Zheng. 2018. Supply chain visibility and social responsibility: Investigating consumers’ behaviors and motives. Manufacturing & Service Operations Management 20(4): 617–636. Laaksonen, P. 1994. Consumer Involvement: Concepts and Research. London: Routledge. Laroche, M., J. Bergeron, and G. Barbaro‐Forleo. 2001. Targeting consumers who are willing to pay more for environmentally friendly products. Journal of Consumer Marketing 18(6): 503–520.

130

Big Data Analytics in Supply Chain Management

Lim, W. M., D. H. Ting, V. S. Bonaventure, A. P. Sendiawan, and P. P. Tanusina. 2013. What happens when consumers realise about green washing? A qualitative investigation. International Journal of Global Environmental Issues 13(1): 14–24. Lin, J. Y. 2011. China and the global economy. China Economic Journal 4(1): 1–14. Livingstone, S., and L. Sparks. 1994. The new German packaging laws: Effects on firms exporting to Germany. International Journal of Physical Distribution  & Logistics Management 24(7): 15–25. Macdonald, E. K., and B. M. Shar p.  2000. Brand awareness effects on consumer decision making for a common, repeat purchase product: A replication. Journal of Business Research 48(1): 5–15. MacKenzie, D. 1991. The rise of the green consumer. Consumer Policy Review 1(2): 68–75. McNeill, L., and R. Moore. 2015. Sustainable fashion consumption and the fast fashion conundrum: Fashionable consumers and attitudes to sustainability in clothing choice. International Journal of Consumer Studies 39(3): 212–222. Morris, R. T., and C. S. Bronson. 1970. The potential loss in money income to the American people by haphazard purchasing. The Journal of Consumer Affairs 4(2): 103–112. Musa, A., A. Gunasekaran, and Y. Yusuf. 2014. Supply chain product visibility: Methods, systems and impacts. Expert Systems with Applications 41(1): 176–194. Nidumolu, R., C. K. Prahalad, and M. R. Rangaswami. 2009. Why sustainability is now the key driver of innovation. Harvard Business Review 87(9): 56–64. Nzila, C., J. Dewulf, H. Spanjers, D. Tuigong, H. Kiriamiti, and H. Van Langenhove. 2012. Multi criteria sustainability assessment of biogas production in Kenya. Applied Energy 93: 496–506. Owusu, A. 2013. Influences of price and quality on consumer purchase of mobile phone in the Kumasi metropolis In Ghana a comparative study. European Journal of Business and Management 5(1): 179–198. Padmanabhan, V. I. P. L., and I. P. Png. 1995. Returns policies: Make money by making good. Sloan Management Review 37: 65–65. Peralta, M. E., P. Luna, and V. M. Soltero. 2020. Towards standards-based of circular economy: Knowledge available and sufficient for transition? International Journal of Sustainable Development & World Ecology 27(4): 369–386. Pasternack, B. A. 1985. Optimal pricing and return policies for perishable commodities. Marketing Science 4(2): 166–176. Poelman, A., J. Mojet, D. Lyon, and S. Sefa-Dedeh. 2008 The influence of information about organic production and fair trade on preferences for and perception of pineapple. Food Quality and Preference 19: 114–121. Poulsen, T., and R. Lema. 2017. Is the supply chain ready for the green transformation? The case of offshore wind logistics. Renewable and Sustainable Energy Reviews 73: 758–771. Ramus, C. A., and I. Montiel. 2005. When are corporate environmental policies a form of greenwashing? Business & Society 44(4): 377–414. Reeves, C. A., and D. A. Bednar. 1994. Defining quality: Alternatives and implications. Academy of Management Review 19(3): 419–445. Roe, B., M. F. Teisl, A. Levy, and M. Russell. 2001. US consumers’ willingness to pay for green electricity. Energy Policy 29(11): 917–925. Sachs, J. D. 2015. The Age of Sustainable Development. New York: Columbia University Press. Sampson, S. E. 2000. Customer‐supplier duality and bidirectional supply chains in service organizations. International Journal of Service Industry Management 11(4): 348–364. Schlegelmilch, B. B., G. M. Bohlen, and A. Diamantopoulos. 1996. The link between green purchasing decisions and measures of environmental consciousness. European Journal of Marketing 30(5): 35–55.

Supply Chain Framework

131

Scholz, M., V. Dorner, M. Franz, and O. Hinz. 2015. Measuring consumers’ willingness to pay with utility-based recommendation systems. Decision Support Systems 72: 60–71. Silva, A. L. P., J. C. Prata, T. R. Walker, D. Campos, A. C. Duarte, A. M. V. M. Soares, D. Barcelò, and T. Rocha-Santos. 2020. Rethinking and optimising plastic waste management under COVID-19 pandemic: Policy solutions based on redesign and reduction of single-use plastics and personal protective equipment. Science of the Total Environment 742: 140565. Selwyn, N., S. Gorard, J. Furlong, and L. Madden. 2003. Older adults’ use of information and communications technology in everyday life. Ageing & Society 23(5): 561–582. Smith, G. E., and T. T. Nagle. 2002. How much are customers willing to pay? Marketing Research 14(4): 20–25. Sutton, R. J., and P. C. Riesz. 1979. The effect of product visibility upon the relationship between price and quality. Journal of Consumer Policy 3: 145–150. Thøgersen, J. 2000. Psychological determinants of paying attention to eco-labels in purchase decisions: Model development and multinational validation. Journal of Consumer Policy 23(3): 285–313. Tibben‐Lembke, R. S. 2002. Life after death: Reverse logistics and the product life cycle. International Journal of Physical Distribution & Logistics Management 32(3): 223–244. Tobler, C., V. H. Visschers, and M. Siegrist. 2011. Eating green: Consumers’ willingness to adopt ecological food consumption behaviors. Appetite 57(3): 674–682. Tse, Y. K., and K. H. Tan. 2012. Managing product quality risk and visibility in multi-layer supply chain. International Journal of Production Economics 139(1): 49–57. Tsen, C. H., G. Phang., H. Hasan, and M. R. Buncha. 2006. Going green: A study of consumers’ willingness to pay for green products in Kota Kinabalu. International Journal of Business and Society 7(2): 40–54. Tseng, C. H. 2016. The effect of price discounts on green consumerism behavioral intentions. Journal of Consumer Behaviour 15(4): 325–334. Ülkü, M. A., L. C. Dailey, and H. M. Yayla-Küllü. 2013. Serving fraudulent consumers? The impact of return policies on retailer’s profitability. Service Science 5(4): 296–309. Ülkü, M. A., and A. Engau. 2020. Sustainable supply chain analytics. In: W. Leal Filho (ed.) Encyclopedia of the UN Sustainable Development Goals. Cham: Springer (forthcoming). Ülkü, M. A., and Ü. Gürler. 2018. The impact of abusing return policies: A newsvendor model with opportunistic consumers. International Journal of Production Economics 203: 124–133. Ülkü, M. A., and J. Hsuan. 2017. Towards sustainable consumption and production: Competitive pricing of modular products for green consumers. Journal of Cleaner Production 142: 4230–4242. Van den Oever, M., K. Molenveld, M. Van der Zee, and H. Bos. 2017. Bio-based and biodegradable plastics: Facts and figures: Focus on food packaging in the Netherlands (No. 1722). Wageningen Food & Biobased Research. Van Loo, E. J., V. Caputo, R. M. Nayga, H. S. Seo, B. Zhang, and W. Verbeke. 2015. Sustainability labels on coffee: Consumer preferences, willingness-to-pay and visual attention to attributes. Ecological Economics 118: 215–225. Wackernagel, M., J. Kitzes, D. Moran, S. Goldfinger, and M. Thomas. 2006. The ecological footprint of cities and regions: Comparing resource availability with resource demand. Environment and Urbanization 18(1): 103–112. Wilkinson, A., A. Dainty, A. Neely, M. Spring, and L. Araujo. 2009. Service, services and products: Rethinking operations strategy. International Journal of Operations  & Production Management 29(5): 444–467. Winsor, R. D., J. N. Sheth, and C. Manolis. 2004. Differentiating goods and services retailing using form and possession utilities. Journal of Business Research 57(3): 249–255.

132

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Yang, Z., G. Aydın, V. Babich, and D. R. Beil. 2009. Supply disruptions, asymmetric information, and a backup production option. Management Science 55(2): 192–209. Yao, D. Q., X. Yue, X. Wang, and J. J. Liu. 2005. The impact of information sharing on a returns policy with the addition of a direct channel. International Journal of Production Economics 97(2): 196–209. Young, W., K. Hwang, S. McDonald, and C. J. Oates. 2010. Sustainable consumption: Green consumer behaviour when purchasing products. Sustainable Development 18(1): 20–31. Young, W., S. V. Russell, C. A. Robinson., and R. Barkemeyer. 2017. Can social media be a tool for reducing consumers’ food waste? A behaviour change experiment by a UK retailer. Resources, Conservation and Recycling 117: 195–203. Zaichkowsky, J. L. 1985. Measuring the involvement construct.  Journal of Consumer Research  12(3): 341–352. Zhang, Z., X. Wang, X. Zhu, Q. Cao, and F. Tao. 2019. Cloud manufacturing paradigm with ubiquitous robotic system for product customization. Robotics and ComputerIntegrated Manufacturing 60: 12–22. Zhu, Q., and J. Sarkis. 2004. Relationships between operational practices and performance among early adopters of green supply chain management practices in Chinese manufacturing enterprises. Journal of Operations Management 22(3): 265–289.

8

A Soft Computing Techniques Application of an Inventory Model in Solving TwoWarehouses Using Cuckoo Search Algorithm Ajay Singh Yadav SRM Institute of Science and Technology

Anupam Swami Government Post Graduate College

Navin Ahlawat SRM Institute of Science and Technology

Srishti Ahlawat SRM Institute of Science and Technology

CONTENTS 8.1

Introduction................................................................................................... 134 8.1.1 Inventory Models with Two Warehouses.......................................... 134 8.1.2 Cuckoo Behavior and Lévy Flights .................................................. 134 8.2 Related Works ............................................................................................... 135 8.3 Assumption and Notations............................................................................ 136 8.4 Mathematical Formulation of Model and Analysis....................................... 136 8.5 Cuckoo Search Algorithm............................................................................. 143 8.6 Numerical Analysis....................................................................................... 145 8.7 Sensitivity Analysis....................................................................................... 146 8.8 Conclusions ................................................................................................... 147 References............................................................................................................... 148

133

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8.1 INTRODUCTION 8.1.1

inventoRy moDels with two waRehouses

Classic inventory models are usually developed using a single warehouse system. In the past, researchers have done a lot of research on management and control. Warehouse management and control systematically respond to demand and supply chains. To do this, production units (product manufacturers and terraces), suppliers, and retailers must supply raw materials and finished products for demand and supply in the market and for consumers. With the conventional model, it is assumed that the demand and the cost of storage are constant and that the goods are delivered directly if needed as part of an endless revision policy. However, over time, many researchers assume that demand can change over time from other prices and factors, and operating costs can change over time and depend on other factors. Many models have been developed without bottlenecks by understanding some of the different requirements over time. For all models that consider declining demand due to inventory, it is assumed that inventory costs remain constant throughout the inventory cycle. Unlimited storage capacity is usually considered when testing an existing model. However, in high-traffic markets such as markets, corporate markets, etc., storage space for goods can be limited. In other cases, adequate storage can be carried out if the decision is made to take up a large quantity of paper. This could be due to the attractive volume discounts available, or if the cost of purchasing the item is higher than other storage costs, or if the demand for goods is too high, or if it is a seasonal product such as reduced production or if there are frequent supply issues. In this case, these items cannot be stored in an existing warehouse (your warehouse alone, abbreviated as OW). In order to save the writing width, additional warehouses (rented warehouses, abbreviated RW) need to be set up, which can be located a short distance W or short distances there, as there is no warehouse. Not close. Rent out

8.1.2

cucKoo BehavioR anD lévy flights

Cuckoo cast birds not only because of the beautiful sounds they produce but also because of their aggressive breeding strategy. Some species, such as ani and guira cuckoo, lay eggs in community nests, although they may take eggs from other species to increase their chances of infecting their own eggs. Many species combat reproductive parasitism by laying eggs in other host bird nests (often other species). There are three types of brood parasitism: intraspecific brood parasitism, cooperative financing, and breeding. Some host birds can conflict directly and attack cuckoos. If the bird provides that the egg does not belong, it throws this foreign egg or simply leaves its nest and builds a new nest somewhere else. Some cuckoo species, such as the New World parasitic reproductive pile, have been developed so that female parasitic cuckoos are often specialized in mimicking the color and pattern of eggs of the selected host species. This reduces the likelihood that they will lay eggs and further increase their reproductive capacity. In addition, the timing of several species that fertilize the

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egg is remarkable. Parasitic cuckoos often prefer the nest where the host bird has just laid eggs. In general, cuckoo eggs are just ahead of host eggs. Once shaving the first hat, the first step is to chase the host’s egg by gradually throwing the egg out of the nest, boosting cuckoo chicken visits on the host bird. On the other hand, various studies have shown that the behavior of flight animals and insects is characteristic of Lévy flight. Recent studies by Reynolds and Frye indicate that fruit flies or Drosophila melanogaster explore your landscape in a series of 90° direct flight paths with a turn of 90°. This resulted in an intermittent and scalable Lévy-flight research model. Studies of human behavior such as the Ju/’hoansi investigation pattern for hunters and gatherers also indicate the characteristics of Lévys flights. Even light can be connected to Lévy flights. Such behaviors are then used for optimization and optimal research, and preliminary results indicate that they are promising.

8.2

RELATED WORKS

It is generally assumed that the cost of holding in the RW is higher than that because of the additional maintenance costs, material handling, etc. To reduce inventory costs, it will be cost-effective to consume RW goods first. Barthelemy et al. (2008). Lévy flight for lights. Chocolate et  al. (2007). Lévy flights at DobeJu/’hoage pattern. Pandey, et al. (2019). Marble Industry Optimization Analysis Based on Optical Genetic and Optical Algorithms. Pavlyukevich (2007a). Lévy flights, nonlocal searches and simulations of violence and Lévy flight abatement. Payne, Sorenson, and Klitz (2005). The Cuckoos. Reynolds and Frye (2007). The free-odor tracker in Drosophila is consistent with the search for the optimal intermittent free scale, PLoS. Shlesinger (2006). Search for research. Shlesinger et al. (1995). (Eds), Flight Lévy and Topics in Phyics. Yadav and Swami (2018a). The production-inventory backlogging model is a sizeable measurement with time-consuming cost and broken Weibull and the integrated Supply Chain Model for Producing Goods and the Hanging Stocks Symbol Under Impression and Inflation Environment. Yadav and Swami (2019a). The Double Flexible Model and the Demand for Upward and Inflation Costs and the inventory model for goods that are not easily interrupted by variable holding costs in two storage areas. Yadav, et al. (2019a) Supply Chain Inventory Model To Regulate Goods With Warehouse  & Inflation Distribution Center. Yadav, et  al. (2019b) Chemical Industry Manufacturer’s Chain For Warehouse And Distribution Center Using Bee Artificial Algorithms. Yadav, et  al. (2017a) Inflation Inventory Model for Moving Items in Two Storage Systems. Yadav, et  al (2016) Multi Object Optimization for Component Electron Inventory Models & Improve Items and Doubles using Genetic Algorithms. Yadav, et.al (2017b). Two Warehouse-Based Inventory Models for Immediate and Delayed Input Devices. Yadav, et al. (2020). Electronic components provide electronic Industry development management for storage and their impact on the environment using the Particle Swarm Optimization Algorithm. Yadav, et al. (2017c). Chain Supply Inventory Model for Two Warehouses and Soft Computing Optimization Yadav, et al. (2017d). Influence of inflation on inventory model of two warehouses for deteriorated goods with varying times of demand and supply.

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ASSUMPTION AND NOTATIONS

Notations & Assumption Ordering cost = WOC Ability of OW = Z1 Ability of RW = Z 2 The length of replenishment cycle = Tn Maximum inventory level per cycle to be ordered = ( Max )Q The time up to which inventory vanishes in RW = T1 The time at which inventory level reaches to zero in OW and shortages begins = Tn Definite time up to which holding cost is constant = K The holding cost in OW = OWH The holding cost in RW = RWH The shortages cost = SCW The opportunity cost = LCW The level of inventory in RW = Π R ( t ) The level of inventory in OW = Πiw ( t ) Determine the inventory level at time t in which the product has shortages = Πs ( t ) Deterioration rate in RW = D0 Deterioration rate in OW = ( D1 + 1) Purchase cost per unit of items = PCW Maximum amount of inventory backlogged = WIB Amount of inventory lost = WIL Cost of purchase = P The present worth cost of shortages = S The present worth cost of lost sale = L The present worth cost of holding inventory = H The total relevant inventory cost per unit time of inventory system = T C [ T1 ,Tn ] ⎤ ⎡(ς + 1) if t > 0 ⎥ Demand ⇒ D ( t ) = ⎢ ⎥ ⎢ ⎢⎣ (ς + 1) + Φt if t > 0 ⎥⎦

{

8.4

}

MATHEMATICAL FORMULATION OF MODEL AND ANALYSIS dΠ R ( t ) + ⎣⎡( ς + 1) + Φt ⎤⎦ = − [ D0 Π R ( t )] 0 ≤ t ≤ T1 dt

(8.1)

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dΠ1w = − ( D1 + 1) Π1w ( t ) 0 ≤ t ≤ T1 dt

(8.2)

dΠ2w ( t ) + ⎡⎣( ς + 1) + Φt ⎦⎤ = − [( D1 + 1) Π R ( t )] T1 ≤ t ≤ T2 dt

(8.3)

dΠs ( t ) = − ⎡⎣( ς + 1) + Φt ⎤⎦ T3 ≤ t ≤ Tn dt

(8.4)

Now the level of inventory at different time intervals is given by solving the equations above (8.1)–(8.4) in the boundary condition Π R ( T1 ) = 0,Π1w ( 0 ) = Z1 ,Π2w ( T2 ) = 0,Πs ( T2 ) = 0 Therefore, differential Equation (8.1) gives ⎡ ( ς + 1) Φ ⎤ + 2 { D0 T1 − 1} e D0 (T1 − t ) ⎥ ⎢ D0 ⎢ D0 ⎥ Π R (t ) = ⎢ ⎥ ⎪⎫ ⎢ ⎧⎪ ( ς + 1) Φ ⎥ ⎢ − ⎨ D + D 2 { D0 t − 1} ⎬ ⎥ 0 ⎭⎪ ⎦ ⎣ ⎪⎩ 0

(8.5)

Π1w ( t ) = Z1e −( D1 +1)T1

(8.6)

⎡ ( ς + 1) ⎤ Φ n1 ( T2 −t ) + ⎢ ⎥ 2 {( D1 + 1) T2 − 1} e ⎥ ⎢ ( D1 + 1) ( D1 + 1) Π1w ( t ) = ⎢ ⎥ ⎫⎪ ⎢ ⎧⎪ ( ς + 1) ⎥ Φ + ⎥ ⎢− ⎨ 2 {( D1 + 1) t − 1} ⎬ ⎪⎭ ⎣⎢ ⎩⎪ ( D1 + 1) ( D1 + 1) ⎦⎥

(8.7)

Φ 2 2 ⎤ Πs ( t ) = f ⎢⎡( ς + 1)( T1 − t ) + T2 − t ⎥ 2 ⎣ ⎦

(8.8)

(

)

Now at t = 0, Π R = Z2, therefore, Equation (8.5) yields ⎡⎛ Φ ( ς + 1) ⎞ ⎛ Φ (ς + 1) ⎞ ⎤ Z 2 = ⎢⎜ 2 − + ⎜ 2 ( D0 T1 − 1) e − D0 T1 + ⎥ ⎟ D0 ⎠ ⎝ D0 D0 ⎠⎟ ⎥⎦ ⎢⎣⎝ D0

(8.9)

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Maximum amount of inventory backlogged during shortages period (at t = T) is given by WIB = −Πs ( Tn ) WIB = f

{

}

(8.10)

(ς + 1)(Tn − t ) + Φ2 (Tn2 − t 2 )

Amount of inventory lost during shortages period WIL = [1 − WIB ] ⎡ WIL = ⎢1 − f ⎣

{

}

(ς + 1)(Tn − t ) + Φ2 (Tn2 − t 2 )

⎤ ⎥⎦

(8.11)

The maximum inventory to be ordered is given as

( Max )Q = [ Z1 + Π R ( 0 ) + WIB ] ⎡ ⎡⎛ Φ ( ς + 1) ⎞ ⎤⎤ ⎢ + ⎢⎜ 2 − ⎥⎥ ⎟ D0 ⎠ ⎢ ⎢⎝ D0 ⎥⎥ ⎢ Z1 + ⎢ ⎥⎥ ⎢ ⎢⎛ Φ ⎥ ⎞ 1 ς + ( ) ⎥ ⎥⎥ ( Max )Q = ⎢ ⎢⎜ 2 ( D0 T1 − 1) e − D0 T1 + D0 ⎟⎠ ⎦⎥ ⎥ ⎢ ⎣⎢⎝ D0 ⎢ ⎥ ⎢ ⎥ Φ 2 2 Tn − t ⎥ ⎢ + f ( ς + 1)( Tn − t ) + 2 ⎣ ⎦

{

(

(8.12)

}

)

Now continuity at t = t1 shows that I1w(t1) = I2w(t1); therefore, from Equations (8.6) and (8.7), we have ⎡ Φ ( D1 + 1)2 T22 − ( ς + 1)( D1 + 1)2 T2 ⎢ ⎢ 2 2 ⎢⎣ − ( D1 + 1) ( Z1 + Z ) + Φ − ( ς + 1)( D1 + 1)

(

where

)

⎤ ⎥=0 ⎥ ⎦⎥

(8.13)

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⎡ ⎧⎪ ( ς + 1) ⎫⎪ −( D1 +1)T1 ⎤ Φ Z = ⎢⎨ + ⎥ 2 (( D1 + 1) T1 − 1) ⎬ e ⎭⎪ ⎣⎢ ⎩⎪ ( D1 + 1) ( D1 + 1) ⎦⎥ which is quadratic in t2 and further can be solved for t2 in terms of t1, i.e., T2 = ϒ ( T1 )

(8.14)

where − ( ς + 1) ( D1 + 1) ± D 2

ϒ ( T1 ) =

4

2Φ ( D1 + 1)

2

And ⎤ ⎡Φ − ( ς + 1)( D1 + 1) + ⎡( ς + 1)2 ( D1 + 1)4 ⎤ ⎢ ⎥ ⎥⎢ D=⎢ ⎧ ⎫ ⎛ ⎞ 1 ς + ) + Φ ( n T − 1) e−( D1 +1)T1 ⎪ ⎥⎥ ⎢ ⎥ ⎢( D + 1)2 ⎪ Z + ( 2 1 1 ⎨ 1 ⎜ ⎬⎥ 2 ⎟ ⎣ +4 Φ ( D1 + 1) ⎦⎢ 1 ⎝ ( D1 + 1) ( D1 + 1) ⎠ ⎩⎪ ⎭⎪ ⎦ ⎣ Next, the total relevant inventory cost per cycle includes following parameters: Ordering cost per cycle = WOC Purchase cost per cycle = P × ( Max )Q The present worth holding cost = H Case − 1 When k < T    and   in RW T1 ⎡k ⎤ ⎢ RWH Π R ( t ) dt + RWH Π R ( t ) dt + ⎥ ⎢ ⎥ 0 k ⎢ ⎥ H1 = ⎢ T1 ⎥ T2 ⎢ ⎥ ⎢ OWH Π1w ( t ) dt + OWH Π2 w ( t ) dt ⎥ ⎢⎣ 0 ⎥⎦ T1

∫

∫

∫

∫

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Holding cost for Case − 1 ⎡ ⎧ Φk 2 ΦT12 ⎫ ⎤ 2 ⎢ ⎪( ς + 1) T1 k + ΦT1 k − 2 D − 3 D ⎪ ⎥ 0 0 ⎢ ⎪ ⎪ ⎥ ⎢ ⎪ ⎪ ⎥ ⎢ ⎪+ ( ς + 1) T13 + ΦT14 − ( ς + 1) k ⎪ ⎥ ⎢ ⎪ ⎪ ⎥ D 0 ⎬ +⎥ ⎢ RWH ⎨ ⎪ ⎪ ⎥ ⎢ 2 2 2 2 ⎪−ΦT1 k − ( ς + 1) T1 k − ΦT1 k ⎪ ⎥ ⎢ ⎪ ⎪ ⎥ ⎢ 2 H1 = ⎢ Φk 3 ⎪ ΦT1 k 2 ( ς + 1) k ⎪ ⎥ + ⎪+ D + ⎪ ⎥ ⎢ 3D0 D0 0 ⎩ ⎭ ⎥ ⎢ ⎢ ⎥ ⎧ ς + 1) T22 ( ς + 1) T1T2 ⎫ ( ⎢ ⎥ − ⎪ Z1T1 + ⎢ ⎥ ( D1 + 1) ( D1 + 1) ⎪⎪ ⎢OW ⎪ ⎥ H ⎨ ⎬ ⎢ ⎥ ⎪ ( ς + 1) T12 ( ς + 1) T22 ⎪ ⎢ ⎥ − + ⎪ ⎪ ⎢ ⎥ 2 1 2 1 + + D D ( ) ( ) 1 1 ⎩ ⎭ ⎣ ⎦

(8.15)

Case − 2 : When k > T ⎡ T1 ⎤ ⎢ RWH Π R ( t ) dt + ⎥ ⎢0 ⎥ ⎢ ⎥ H2 = ⎢ T1 ⎥ T2 ⎢ ⎥ ⎢ OWH Π1w ( t ) dt + OWH Π2 w ( t ) dt ⎥ ⎥⎦ ⎢⎣ 0 T1

∫ ∫

∫

Holding cost for Case − 2 ⎤ ⎡ ⎧( ς + 1) T12 + ΦT13 ⎫ ⎥ ⎢ ⎪ ⎪ ⎢RWH ⎨ ΦT 2 ΦT 2 ⎬ + ⎥ 1 ⎢ ⎥ ⎪+ 1 − ⎪ 2D0 ⎪⎭ ⎪⎩ D0 ⎢ ⎥ ⎢ ⎥ H2 = ⎢ ⎧ ς + 1) T22 ( ς + 1) T1T2 ⎫ ⎥ ( ⎢ − ⎪ Z1T1 + ⎪⎥ 1 1 + + D D ( ) ( ) ⎢ 1 1 ⎪ ⎪⎥ ⎢OWH ⎨ ⎬⎥ ⎢ ⎪ ( ς + 1) T12 ( ς + 1) T22 ⎪⎥ ⎢ ⎪+ 2 D + 1 − 2 D + 1 ⎪⎥ ( 1 ) ⎢⎣ ⎩ ( 1 ) ⎭ ⎥⎦

(8.16)

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The present worth of shortages cost ⎡ ⎢ ⎢ ⎢ ⎢ ⎢ S = SCW f ⎢ ⎢ ⎢ ⎢ ⎢ ⎣⎢

⎧ ( ς + 1) Tn2 ( ς + 1) T22 ⎫ ⎤ − ⎪ ⎪⎥ 2 2 ⎪ ⎪⎥ ⎪ ⎪⎥ 3 3 ⎪⎥ ⎪+ ΦTn − ΦT2 − ⎪ 6 ⎪⎥ 6 ⎨ ⎬⎥ ⎪ 2 ⎪⎥ ⎪( ς + 1) T1Tn + ( ς + 1) T2 ⎪ ⎥ ⎪ ⎪⎥ 2 2 ⎪ ΦT2 Tn ( ς + 1) T3 ⎪ ⎥ ⎪⎩− 2 + ⎪⎭ ⎦⎥ 2

(8.17)

The present worth opportunity cost / Lost sale cost ⎡ ⎢ ⎢ L = ⎢ LCW ⎢ ⎢ ⎣

⎧ ⎧ ( ς + 1) Tn2 ( ς + 1) T22 ΦTn3 ΦT23 ⎫⎫⎤ − + − − ⎪ ⎪ ⎪⎪⎥ 2 2 6 6 ⎪ ⎪ ⎪⎪⎥ ⎨1 − ⎨ ⎬⎬ ⎥ 2 ⎪ ⎪ ΦT22 Tn ( ς + 1) T3 ⎪⎪ ⎥ 2 ⎪⎪ ⎥ ⎪ ⎪( ς + 1) T1Tn + ( ς + 1) T2 − 2 + 2 ⎭⎭⎦ ⎩ ⎩

(8.18)

Present worth purchase cost ⎡ ⎢ ⎢ ⎢ ⎢ P = ⎢ PCW ⎢ ⎢ ⎢ ⎢ ⎢ ⎣

⎧ ⎫⎤ ⎧⎪ Φ ( ς + 1) ⎫⎪ ⎪ Z1 + ⎨ 2 − ⎪⎥ ⎬+ D0 ⎭⎪ ⎪ ⎪⎥ ⎩⎪ D0 ⎪ ⎪⎥ ⎪⎪⎪⎧ Φ ⎫ ς + 1 ( ) ⎪ ⎪⎪ ⎥ − D0 T1 + ⎨⎨ 2 ( ς + 1) T1 − 1 e ⎬ + ⎬⎥ D0 ⎪⎭ ⎪ ⎥ ⎪⎩⎪ D0 ⎪ ⎪⎥ Φ 2 ⎪ ⎡ ⎪⎥ 2 ⎤ ⎪ f ⎢⎣( ς + 1)( Tn − T2 ) + 2 Tn − T2 ⎥⎦ ⎪ ⎥ ⎪⎩ ⎪⎭ ⎥⎦

(

)

(

(8.19)

)

Therefore, the total relevant inventory cost per unit per unit of time is denoted and given by Case 1: ⎧ [WOC + P + S + L + H1 ] ⎫ T C (T1, Tn ) = ⎨ ⎬ T ⎩ ⎭

(8.20)

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⎧ ⎡ ⎤⎫ ⎡ ⎧ ⎫⎤ ⎧⎪ Φ ( ς + 1) ⎫⎪ ⎪ ⎢ ⎥⎪ ⎢ ⎥ + ⎪ Z1 + ⎨ 2 − ⎪ ⎬ ⎪ ⎢ D0 ⎪⎭ ⎥⎪ ⎢ ⎪⎩ D0 ⎪ ⎪⎥ ⎪ ⎢ ⎥⎪ ⎢ ⎪ ⎪⎥ ⎪ ⎢ ⎥⎪ ⎢ ⎥ ⎪⎪⎪⎧ Φ ⎪ ⎫ (ς + 1) ⎪ + ⎪ ⎥ + − D0T1 ⎪ ⎪ ⎢WOC + ⎢ PCW ⎨⎨ ⎥ 1 1 ς + T − e + ( ) 1 ⎬ ⎬ 2 ⎥⎪ ⎪ ⎢ D0 ⎪⎭ ⎪ ⎥ ⎢ ⎪⎩⎪ D0 ⎥⎪ ⎪ ⎢ ⎢ ⎪ ⎪⎥ ⎥⎪ ⎪ ⎢ ⎢ ⎥ Φ ⎪ ⎪ ⎡ ς + 1 (T − T ) + 2 2 ⎤ ⎥ T f T − ⎪ ⎢ ⎪ ⎢ ( ) ⎥ 2 2 n n ⎪ ⎢⎣ 2 ⎦⎥ ⎪ ⎥ ⎥⎪ ⎪ ⎢ ⎢ ⎪⎩ ⎪⎭ ⎦ ⎣ ⎥⎪ ⎪ ⎢ ⎥⎪ ⎪ ⎢ 2 2 ⎧ ( ς + 1) Tn ( ς + 1) T2 ⎫ ⎤ ⎥⎪ ⎪ ⎢⎡ − ⎪ ⎪⎥ ⎥⎪ ⎪ ⎢⎢ 2 2 ⎥⎪ ⎪ ⎪⎥ ⎪ ⎢⎢ ⎥⎪ ⎪ ⎪⎥ 3 3 ⎪ ⎢⎢ T Φ ΦT 2 ⎥⎪ ⎪+ n − ⎪⎥ − ⎪ ⎢⎢ ⎪ ⎪⎥ ⎥⎪ 6 ⎪ ⎢ ⎢ SCW f ⎨ 6 ⎬⎥ + ⎥⎪ ⎪ ⎢⎢ ⎪ 2 ⎪⎥ ⎥⎪ ⎪ ⎢⎢ ⎪( ς + 1) T1Tn + ( ς + 1) T2 ⎪ ⎥ ⎢ ⎢ ⎥⎪ ⎪ ⎢ ⎪ ⎪ ⎥ 2 ⎥⎪ 2 ⎪ ⎢⎢ 1 T ς + ( ) T ΦT 3 ⎪ ⎪ 2 n ⎥ ⎥⎪ ⎪ ⎢⎢ ⎪⎩− 2 + ⎪⎭ ⎥⎦ ⎥⎪ 2 ⎪ ⎢⎣ ⎥⎪ ⎪ ⎢ ⎥⎪ 2 2 ⎪ 1 ⎢⎡ 3 3 ⎤ C ⎧ ⎫ ⎫ ⎧ 1 T 1 T ς + ς + ( ) ( ) T T Φ Φ 2 n T (T1, Tn ) = ⎨ ⎢ 2 n (8.21) ⎢ − + − − ⎪ ⎪ ⎪⎥ ⎥ ⎬ ⎪ T ⎪ ⎢⎢ 2 2 6 6 ⎪⎪⎥ ⎥ ⎪ ⎪ ⎪ ⎪ ⎢ ⎢ LCW ⎨1 − ⎨ ⎬⎬ ⎥ +⎥⎪ 2 2 ⎪ ⎢⎢ 1 T ⎪ ⎪ ⎥ ⎥⎪ ⎪ ⎪ ς + ( ) T ΦT 3 2 n 2 + ⎪ ⎢⎢ ⎪⎪ ⎥ ⎥ ⎪ ⎪ ⎪( ς + 1) T1Tn + ( ς + 1) T2 − 2 2 ⎭ ⎭ ⎦ ⎥⎪ ⎩ ⎩ ⎪ ⎢⎣ ⎥⎪ ⎪ ⎢ 2 2 ⎥ ⎪ ⎢⎡ ⎪ ⎤ ⎧ ⎫ T Φk Φ 1 2 ⎥⎪ ⎪ ⎢⎢ ⎪( ς + 1) T1 k + ΦT1 k − 2 D − 3 D ⎪ ⎥ 0 0 ⎥⎪ ⎪ ⎢⎢ ⎪ ⎪ ⎥ ⎥⎪ ⎪ ⎢⎢ ⎪ ⎪ ⎥ ⎥⎪ ⎪ ⎢⎢ ⎪+ ( ς + 1) T 3 + ΦT 4 − ( ς + 1) k ⎪ ⎥ 1 1 ⎥⎪ ⎪ ⎪ ⎥ ⎪ ⎢⎢ D0 ⎬ +⎥ ⎥⎪ ⎪ ⎢ ⎢ RWH ⎨ ⎥⎪ ⎪ ⎪ ⎥ 2 2 2 2 ⎪ ⎢⎢ 1 k k T k −ΦT − ς + − ΦT ( )1 1 1 ⎥⎪ ⎪ ⎪ ⎥ ⎪ ⎢⎢ ⎥⎪ ⎪ ⎪ ⎥ ⎪ ⎢⎢ 2 ⎥⎪ ⎪ ΦT1 k 2 ( ς + 1) k ⎪ ⎥ Φk 3 ⎪ ⎢⎢ + ⎥⎪ ⎪+ D + ⎪ ⎥ ⎪ ⎢⎢ 3 D0 D0 0 ⎩ ⎭ ⎥ ⎥⎪ ⎢ ⎢ ⎪ ⎥⎪ ⎥ ⎪ ⎢⎢ 2 ⎧ ς + 1) T2 ( ς + 1) T1T2 ⎫ ⎥⎪ ( ⎥ ⎪ ⎢⎢ − ⎪ Z1T1 + ⎥⎪ ⎥ ⎪ ⎢⎢ ( D1 + 1) ( D1 + 1) ⎪⎪ ⎪ ⎥⎪ ⎢ ⎢ ⎥ ⎪ OW H ⎨ ⎬ ⎥⎪ ⎥ 2 2 ⎪ ⎢⎢ ⎢ ⎪ ( ς + 1) T1 ⎪ (ς + 1) T2 ⎥⎪ ⎥ ⎪ ⎢⎢ − ⎪+ 2 ⎪ ⎥ ⎢ ⎥ 2 1 1 D D + + ( ) ( ) 1 1 ⎪⎩ ⎣ ⎣ ⎩ ⎭ ⎦ ⎦ ⎪⎭

(

)

(

)

Case 2: ⎧ [WOC + P + S + L + H 2 ] ⎫ T C (T1, Tn ) = ⎨ ⎬ T ⎩ ⎭

(8.22)

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⎧ ⎡ ⎤⎫ ⎡ ⎧ ⎫⎤ ⎧⎪ Φ ( ς + 1) ⎫⎪ ⎪ ⎢ ⎥⎪ ⎢ ⎪ Z1 + ⎨ 2 − ⎪⎥ ⎬+ ⎪ ⎢ D0 ⎪⎭ ⎥⎪ ⎢ ⎪⎩ D0 ⎪ ⎪⎥ ⎪ ⎢ ⎥⎪ ⎢ ⎪ ⎪⎥ ⎪ ⎢ ⎥⎪ ⎢ ⎥ ⎪⎪ ⎧⎪ Φ ⎪ ⎫ (ς + 1) ⎪ + ⎪ ⎥ + − D0T1 ⎪ ⎪ ⎢WOC + ⎢ PCW ⎨⎨ ⎥ ς + 1 T − 1 e + ( ) 1 ⎬ ⎬ 2 ⎥⎪ ⎪ ⎢ D0 ⎪⎭ ⎪ ⎥ ⎢ ⎪ ⎪⎩ D0 ⎥⎪ ⎪ ⎢ ⎢ ⎪ ⎪⎥ ⎥⎪ ⎪ ⎢ ⎢ ⎥ Φ 2 ⎪ ⎪ ⎡ 2 ⎤ ⎥⎪ ⎪ ⎢ ⎢ ⎪ f ⎣⎢( ς + 1)( Tn − T2 ) + 2 Tn − T2 ⎦⎥ ⎪ ⎥ ⎥⎪ ⎪ ⎢ ⎢ ⎥ ⎩⎪ ⎭⎪ ⎦ ⎣ ⎥⎪ ⎪ ⎢ ⎥⎪ ⎪ ⎢ 2 2 ⎧ ( ς + 1) Tn ( ς + 1) T2 ⎫ ⎤ ⎥⎪ ⎪ ⎢⎡ − ⎪ ⎪⎥ ⎥⎪ ⎪ ⎢⎢ 2 2 ⎥⎪ ⎪ ⎪⎥ ⎪ ⎢⎢ ⎥⎪ ⎪ ⎪⎥ 3 3 ⎪ ⎢⎢ ⎥⎪ ⎪+ ΦTn − ΦT2 − ⎪⎥ ⎪ ⎢⎢ ⎪ ⎪ ⎥ ⎥⎪ 6 ⎪ ⎢ ⎢ SCW f ⎨ 6 ⎬⎥ + ⎥⎪ ⎪ ⎢⎢ ⎪ ⎪ 2 ⎥ ⎥⎪ T T T 1 1 ς + + ς + ( ) ( ) 1 2 n ⎪ ⎢⎢ ⎪ ⎪⎥ ⎥⎪ ⎪ ⎢ ⎢⎢ ⎪ ⎪⎥ ⎥⎪ 2 ⎪ 1 ⎢⎢ ς + 1) T32 ⎪ ⎥ ( T ΦT C ⎪ 2 n ⎥⎬ T (T1, Tn ) = ⎨ ⎢ − + ⎪⎭ ⎦⎥ ⎥⎪ 2 2 ⎩⎪ ⎪ T ⎢ ⎣⎢ ⎥⎪ ⎪ ⎢ ⎪ ⎢⎡ ⎧ ⎧ ( ς + 1) Tn2 ( ς + 1) T22 ΦTn3 ΦT23 ⎫⎤ ⎥ ⎪ ⎫ ⎪ ⎢⎢ − − − + ⎪ ⎪ ⎪ ⎪ ⎥ ⎥⎥ ⎪ 2 2 6 6 ⎪ ⎢⎢ ⎪⎪⎥ ⎪ ⎪ ⎪ ⎬⎬ ⎥ + ⎥ ⎪ ⎪ ⎢ ⎢ LCW ⎨1 − ⎨ 2 ⎪ ⎪ ΦT22 Tn ( ς + 1) T3 ⎪⎪ ⎥ ⎥ ⎪ ⎪ ⎢⎢ 2 + ⎪⎪ ⎥ ⎥ ⎪ ⎪ ⎪( ς + 1) T1Tn + ( ς + 1) T2 − ⎪ ⎢⎢ 2 2 ⎭⎭⎦ ⎥ ⎩ ⎩ ⎪ ⎢⎣ ⎪ ⎢ ⎥⎪ ⎪ 2 3 ⎥⎪ ⎤ ⎧( ς + 1) T1 + ΦT1 ⎫ ⎪ ⎢⎡ ⎥⎪ ⎥ ⎪ ⎪ ⎪ ⎢⎢ ⎥⎪ ⎥ ⎪ ⎢⎢ ⎢ RWH ⎨ ΦT12 ΦT12 ⎬ + ⎥⎪ ⎥ ⎪ ⎪+ − ⎪ ⎢⎢ ⎥ D D 2 ⎪ ⎪ ⎢ ⎥ 0 0 ⎭ ⎩ ⎪ ⎢ ⎥⎪ ⎥ ⎪ ⎢⎢ 2 ⎥⎪ ⎢ ⎥ ⎧ ς + 1) T2 ( ς + 1) T1T2 ⎫ ( ⎪ ⎢⎢ ⎪ ⎥ ⎥ − ⎪ Z1T1 + ⎪ ⎪ ⎢⎢ ⎪ ⎥ D + D + 1 1 ( ) ( ) ⎥ 1 1 ⎪ ⎪ ⎢ ⎢OW ⎪ ⎥⎪ H ⎨ ⎬⎥ ⎪ ⎢⎢ 2 2 ⎥⎪ ⎥ T ς + T ς + 1 1 ⎪ ⎪ ( ) ( ) 1 2 ⎪ ⎢⎢ ⎥⎪ + − ⎥ ⎪ ⎪ ⎪ ⎢⎢ D + D 2 1 2 + 1 ( ) ( ) ⎥⎦ ⎪⎭ 1 1 ⎥ ⎩ ⎭ ⎦ ⎩ ⎣⎣

(

)

(

8.5

)

(8.23)

CUCKOO SEARCH ALGORITHM

Cuckoo Search (CS) is a new heuristic algorithm inspired by parasite reproduction behaviors that are mandatory in certain cuckoo species that lay eggs in nest nests. Some cuckoos specialize in mimicking the color and pattern of the eggs of several selected hosts. This reduces the likelihood of leaving the egg. If the host bird detects a foreign egg, it is either left behind or eliminated. Parasitic cuckoos prefer a nest where the host bird lays eggs. Cuckoo eggs hatch early than host eggs, and when absorbed, they chase host eggs away from the nest. For example, cuckoo chickens receive a lot of food, and sometimes, they mimic the sound of a rooster in

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order to eat more. Most of the time, cuckoos search for a simple, random street that becomes a Markov chain, the next position based on the current position, and the possible transition from the next. The use of Lévy flights instead of simple random routes improves search capabilities. Lévy’s flight is a random walk on stage after spreading heavy probability. Each cuckoo is a possible solution to the problem under consideration. The main goal is to come up with a new and possibly better (cuckoo) solution to replace it with a less efficient solution. Every nest has eggs, but as the problem progresses, some eggs can be used to give a number of solutions. There are three basic rules customized for CS. The first rule is that every cuckoo lays eggs and throws them at random nests. The second rule states that the nest with the longest physical form is transmitted to the next generation, while the latter rule indicates that the number of host nests is recorded and that the eggs that have been hatched by the cuckoo are found by the host bird with a probability of m [0, 1], and according to m, the host bird throws its eggs or leaves. It is assumed that only m fraction of the nest is replaced by the new nest. Cuckoo hunters have been implemented on the basis of three rules. In order to generate a new solution Pit +1 for the cuckoo clock, a Lévy flight is performed. This step is called Global Random Walk and is given by Pit+1 = Pit + δ ⊗ Lévy(ν ) ( Pbest − Pit ) The local random walk is given by: Pit+1 = Pit + δ ⊗ L(m − ) ⊗ ( Pjt − Pkt ) where Pit is the previous solution, δ > 0 is the step size with respect to the scales of the problem, and ⊗ is the multiplication based on the input. Here, Pjt and Pkt are solutions chosen at random and Pbest is the best solution for the moment. In this work, the length of random meals in Lévy flights because of the more efficient exploration of the search space by Lévy flights is considered and derived from the Lévy distribution with limited variants and meanings.

κν ⎧ νΓ(ν )sin ⎜⎛ ⎞⎟ ⎝κ ⎠ 1 ⎪⎪ Lévy ∼ ⎨ ( A  A0 > 0 ) A1+ν κ ⎪ ⎩⎪ Because of the recent effects of the new solution on Lévy flights, local browsers are accelerating. Here, some of the solutions that must be generated by remote field randomization, which prevents the system from optimally functioning, are gamma functions, m is the probability switch.  is a random number and (1 < ν ≤ 3). The stride length in the cuckoo hunt is very limited, and every big step is possible due to large-scale randomization.

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The Pseudocode for CS is Given in Algorithm 1 Algorithm : − Pseudo − code of Cuckoo Search (CS ) algorithm.

Begin: U U Initialize cuckoo population: n U U Define d -dimensional objective function, f (x ) do Until iteration counter < maximum number of iterations U U Uglobal Search: U U generate new nest Pit +1 using Eq. (A) U U evaluate fitness of Pit +1 U U choose a nest j randomly from n initial nests.  

U U Uif the fitness of Pit +1 better than that of Pit U U Ureplace j by Pit +1 U U Uend if U U local search: U U abandon some of the worst nests using probability switch. U U create new nest using Eq. (B) U U Evaluate and find the best. Uend until Uupdate final best End

8.6

NUMERICAL ANALYSIS

The following data were randomly selected on units that have been used to find the optimal solution and model validation for the three-player manufacturers,

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­TABLE 8.1 The Computational Optimal Solutions of the Models are Total Relevant Cost and CS S.N.

Cost Function

1 2 3 4

T1* T2* T n* Total relevant cost

­Case-1

­Case-2

TC(T1, Tn)

TC(T1, Tn)

2.47477 22.7053 74.2487 43.5249

7.31247 35.7420 37.9892 21.0422

­Case-1

Case-2 ­

Algorithm

TC(T1, Tn)

TC(T1, Tn)

CS of T1* CS of T2* CS of Tn* CS

2.47477 42.7053 74.2487 035249

7.30247 35.7440 37.9894 40042.2

distributors, and retailers. The data are given as ( ς + 1) = 500, C = 1500, Z =2000, Φ = 0.50, OWH = 60, RWH = 75, PCW = 1500, D0 = 0.013, ( D1 + 1) = 0.014, SCW = 250, k = 1 = 0.06, and LCW = 100. The value of the decision variable is calculated for the model for the two separate cases. The optimal solution of model computing is shown in ­Table 8.1. Actual values should be called by CS specifically through experience and trial-and-error. However, some standard settings are reported in the literature. • • • • • •

Population Size of Cuckoo Search =150 Number of generations of Cuckoo Search = 3000 Crossover type of Cuckoo Search = two Point Crossover rate of Cuckoo Search =1.8 Mutation types of Cuckoo Search = Bit flip Mutation rate of Cuckoo Search = 0.003 per bit

If single cross-over points instead of Cuckoo Search two cut-point cross-over of Cuckoo Search employed by rate of crossover of Cuckoo Search can be down to a maximum of 1.50.

8.7 SENSITIVITY ANALYSIS ­TABLE 8.2 Sensitivity Analysis with Respect to Parameter Rate (ς + 1) Φ WOC Cs

550 450 0.55 0.45 3650 750 475 345

T1*

T2*

Tn*

4.49638 4.3944 4.46488 4.50943 4.47477 4.47475 4.47605 4.46435

64.3930 54.4334 59.4764 90.9446 64.7064 64.7009 63.0333 59.5406

74.6398 74.3336 73.6847 98.3064 74.4498 74.4433 73.6067 80.9334

TC(T1, Tn) 340800 336654 343436 300468 337644 337634 338484 333457 (Continued)

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TABLE 8.2 (continued) Sensitivity Analysis with Respect to Parameter Rate LCW

330 50 3.773 0.805 3650 750 66 30 84.5 37.5 0.0343 0.0065 0.0354 0.007

K Cp OWH RWH D0 (D1 + 1)

T1*

T2*

Tn*

TC(T1, Tn)

4.47468 4.47543 4.54073 4.33043 4.59484 3.76330 4.46344 4.53653 4.37334 3.39647 4.48564 4.33759 4.46534 4.54740

64.6849 64.8369 68.6383 39.5857 63.9583 55.3799 59.6488 88.4048 64.3004 54.6439 63.0470 78.6439 66.0434 44.0303

74.4604 74.3899 83.7033 44.5368 75.4053 67.8856 73.9630 95.3834 76.3954 60.8334 74.3373 94.4048 76.9065 58.7006

337574 337868 353730 84565.8 340438 343983 343077 303374 344304 309304 333744 374563 334683 375946

TABLE 8.3 Sensitivity Analysis with Cuckoo Search (CS) Algorithm Function WOC RWH OWH SCW LCW PCW

8.8

Algorithm

Best

Worst

Mean

Standard Deviation

CS CS CS CS CS CS

0.49608 0.46488 0.47477 0.47468 0.47605 0.50073

64.0900 59.4764 60.7060 60.6809 63.0333 68.6083

74.6098 70.6807 74.0498 74.0600 73.6067 80.7003

040800 043436 037604 037570 038080 050730

CONCLUSIONS

According to the scope of the research, the current studies can be divided into two categories: the first category uses the two-inventory system to model economic order volumes at a constant demand ratio using soft computing techniques in a single firm, and the second category studies the inventory of deteriorating items in the two-warehouse inventory management system for the economic order quantity model with a constant demand ratio, using soft computing. In terms of quantity, there are far fewer studies in the second category than in the first category. The study of inventory problems of deteriorating items is a new area of research compared to the research of inventories of ordinary objects, so that the total amount of research is much smaller than that of traditional ones. In this paper, we propose a two-storey inventory model for determinants of broken goods with linear time-dependent demand and a variety of costs to call cycle lengths

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with the goal of minimizing total inventory costs using the cuckoo search algorithm: Lack of permission and partial withdrawal using the cuckoo search algorithm. Two different cases have been discussed with one variable holding cost during the cycle period and another with the cost of holding for a total cycle length, and it is noted that while the variable captures the total cost of inventory more than in other cases using the cuckoo search algorithm. Further, the proposed model is very useful for deteriorating goods, as it increases in both the warehouses where the total cost of inventory decreases using the cuckoo search algorithm. This model can be improved by combining other setbacks, probabilistic demand patterns, and other realistic combinations using the cuckoo search algorithm.

REFERENCES Barthelemy, P., Bertolotti, J., and Wiersma, D. S. (2008). A Lévy flight for light, Nature, 453, 495–498. Pandey, T., Yadav, A.S., and Malik, M. (2019). An analysis marble industry inventory optimization based on genetic algorithms and particle swarm optimization, International Journal of Engineering and Advanced Technology, 7(6S4), 369–373. Pavlyukevich, I. (2007a) Lévy flights, non-local search and simulated annealing, Journal of Computational Physics, 226, 1830–1844. Pavlyukevich, I. (2007b). Cooling down Lévy flights, Journal of Physics A: Mathematical and Theoretical, 40, 12299–12313. Payne, R. B., Sorenson, M. D., and Klitz, K. (2005). The Cuckoos, Oxford University Press, Oxford. Reynolds, A. M., and Frye, M. A. (2007). Free-flight odor tracking in Drosophila is consistent with an optimal intermittent scale-free search, PLoS One, 2, e354. Shlesinger, M. F. (2006). Search research, Nature, 443, 281–282. Shlesinger, M. F., Zaslavsky, G. M. and Frisch, U. (Eds.) (1995). Lévy Flights and Related Topics in Phyics, Springer, Berlin. Yadav, A. S., and Swami, A. (2018a). A partial backlogging production-inventory lot-size model with time-varying holding cost and weibull deterioration, International Journal Procurement Management, 11(5), 639–649. Yadav, A. S., and Swami, A. (2018b). Integrated supply chain model for deteriorating items with linear stock dependent demand under imprecise and inflationary environment, International Journal Procurement Management, 11(6), 684–704. Yadav, A. S., and Swami, A. (2019a). A volume flexible two-warehouse model with fluctuating demand and holding cost under inflation. International Journal Procurement Management, 12(4), 441–456. Yadav, A. S., and Swami, A. (2019b). An inventory model for non-instantaneous deteriorating items with variable holding cost under two-storage. International Journal Procurement Management, 12(6), 690–710. Yadav, A. S., Bansal, K. K., Kumar, J. and Kumar, S. (2019a). Supply chain inventory model for deteriorating item with warehouse & distribution centres under inflation. International Journal of Engineering and Advanced Technology, 8(2S2), 7–13. Yadav, A. S., Kumar, J., Malik, M., and Pandey, T. (2019b). Supply chain of chemical industry for warehouse with distribution centres using artificial bee colony algorithm, International Journal of Engineering and Advanced Technology, 8(2S2), 1–6. Yadav, A. S., Mahapatra, R. P., Sharma, S., and Swami, A. (2017a). An inflationary inventory model for deteriorating items under two storage systems. International Journal of Economic Research, 14(9), 29–40.

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Yadav, A. S., Mishra, R., Kumar, S., and Yadav, S. (2016). Multi objective optimization for electronic component inventory model & deteriorating items with two-warehouse using genetic algorithm. International Journal of Control Theory and applications, 9(2), 15–35. Yadav, A. S., Sharma, S., and Swami, A. (2017b). A fuzzy based two-warehouse inventory model for non instantaneous deteriorating items with conditionally permissible delay in payment. International Journal of Control Theory and Applications, 10(11), 107–123. Yadav, A.S., Swami, A., Ahlawat, N., Bhatt, D. and Kher, G. (2020). Electronic components supply chain management of Electronic Industrial development for warehouse and its impact on the environment using Particle Swarm Optimization Algorithm International Journal Procurement Management. Optimization and Inventory Management (Book Chapter), Springer. Yadav, A. S., Swami, A., Kher, G. and Sachin Kumar, S. (2017c). Supply chain inventory model for two warehouses with soft computing optimization. International Journal of Applied Business and Economic Research, 15(4), 41–55. Yadav, A. S., Taygi, B., Sharma, S. and Swami, A. (2017d). Effect of inflation on a two-warehouse inventory model for deteriorating items with time varying demand and shortages. International Journal Procurement Management, 10(6), 761–775.

9

An Overview of the Internet of Things Technologies Focusing on Disaster Response Reinaldo Padilha França Communications Department (DECOM) – Faculty of Electrical and Computer Engineering (FEEC), State University of Campinas (Unicamp)

Ana Carolina Borges Monteiro Communications Department (DECOM) – Faculty of Electrical and Computer Engineering (FEEC), State University of Campinas (Unicamp)

Rangel Arthur Department of Telecommunications Engineering – Faculty of Technology (FT), State University of Campinas (Unicamp)

Yuzo Iano Communications Department (DECOM) – Faculty of Electrical and Computer Engineering (FEEC), State University of Campinas (Unicamp)

CONTENTS 9.1 Introduction................................................................................................... 152 9.2 Artificial Intelligence.................................................................................... 153 9.3 Internet of Things ......................................................................................... 154 9.4 The Use of IoT and AI for Risk and Disaster Management.......................... 156 9.5 The IoT Relationship in the Supply Chain During Disaster ......................... 158 9.6 Discussion ..................................................................................................... 159 9.7 Future Trends ................................................................................................ 163 9.8 Conclusions ................................................................................................... 164 References .............................................................................................................. 164

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9.1 INTRODUCTION The big change that the Internet of Things (IoT) brings to people’s lives is beyond the ease of preparing a coffee, scheduling a hot bath or saving on electricity, giving objects connectivity, and making room for intelligent commands on countless everyday tasks. But not only that, IoT means increasing work productivity, improving urban mobility and public and private safety conditions, streamlining processes, as these technologies range from sensors to algorithms capable of reading and analyzing the information captured, providing reliable data for risk management and disasters [1]. There are several types of natural phenomena in which they configure normal events generated by movement, be it water, earth, air, space, which interfere in the vital structure of society; however, these disasters are usually not responsible, often caused by the reaction of nature, since they are natural phenomena and represent the change of cycle on Earth, and nowadays, these occurrences have increased significantly, leading to further studies regarding statistics and research on the environment [2]. Although many disasters have occurred because the planet Earth is suffering more and more, with global warming and the greenhouse effect, it leads to an increase in natural disasters, caused by the imbalance of nature, which generate several impacts on society. There are also environmental disasters caused by humans, which are responsible for causing damage to the environment, the damage that does not only affect plants and animals but also causing a negative impact on soil, water, and air. Since due to the evolution of society, the human being when carrying out an activity must be aware that it can have a negative impact on the environment, which must take necessary accident prevention measures; however, the problem occurs when these measures fail or are not considered important [3]. In turn, with regard to nature and natural disasters, they have an effect on helping to renew and maintain ecosystems, supply natural water sources, and create relief, among others. Some common examples are storms, earthquakes, and tsunamis, hurricanes, cyclones and typhoons, floods, landslides, endemics, epidemics, pandemics, erosion, volcanic eruption, tropical cyclone, fires (when not caused by human action), flood, fall of a meteor, volcanic eruptions, among others. However, as much as man is able to evolve, accumulate wealth, develop technologies, build cities, nothing is capable of overcoming nature. Thus, Natural Disasters represent a set of phenomena that are part of terrestrial geodynamics; therefore, the nature of the planet, which can bring catastrophic consequences for people and as much as the technology in the area, is advanced; many natural disasters can be predictable [3,4]. Natural disasters are a major problem for industries, and until recently, there was no way to deal with them. Backups to ensure access to data, building secure structures to install business machinery, and purchasing products such as insurance were all that could be done to reduce risks, now with the use of drones, it is possible to capture very high-resolution images, adding the use of computer vision and artificial intelligence (AI), which can help in identifying structural faults with superhuman precision [5]. Technology is making an impact and lets us know about risks and disasters long before they can happen, which is a competitive advantage, with the help of devices that work under less than optimal conditions, where so there is little or very little

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wireless connection and energy sources used to screen and accurately detect the onset of tectonic activities, for example. It is also with IoT technology that we can now accurately monitor earthmoving on sloping hills and slopes. Rainfall data collection platforms and humidity sensors gather information on the amount of accumulated rainfall and water in the soil. Equipment transfers data over the Internet directly to control centers, where decisions are made to mitigate the impacts of natural disasters [2,6]. AI is associated with intelligent objects to make disaster prevention a much more accurate task, where new technologies are being used in organizations so that they can identify situations and act preventively, preventing the loss of assets and resources. Using robots, sensors, and drones is a way to better coordinate actions in hazardous areas and ensure that service providers can understand the situations in which they are placed faster. The fire department, for example, can process heat areas in a building with computer vision and intelligent software to map out more efficient routes and reach victims [7]. This chapter is motivated to provide a scientific contribution related to an overview and current discussion about the essentiality of IoT technologies focusing on disaster response and management, addressing their key points and their importance, which are a complex and heterogeneous concept but which through technology’s potential showing integrations and successful relationship. This research aims to show the importance of IoT technologies with a focus on disaster response and management, having as specific objectives to discuss and analyze its essentiality in a modern context exemplifying how certain natural disasters could be mitigated or avoided through their applicability. Contributing to enhancing the view of the novice reader about this technology, in this sense, the authors of this chapter hope that this work will be useful to the novice reader by learning the pillars and technology’s potential and stimulating their interest in research and approaches to advanced thematic. Therefore, this chapter aims to provide an updated overview of IoT technologies as well as focusing on disaster response and management, showing its relationship and integrations approaching, with a concise bibliographic background, categorizing, and synthesizing the potential of both technologies.

9.2

ARTIFICIAL INTELLIGENCE

AI is a branch of technology that aims to simulate the capacity of human thinking, that is, it makes the machines are programmed with algorithms that learn and modify according to the analyzed data and, from that, manage to “think” logically. AI is also known as machine learning, since based on it, devices are able to adapt according to the data they receive, carrying out a process that diverges directly from the system of common computers which always follow the same logical commands [8]. Through AI, cognitive computing and deep learning algorithms collect data, analyze, learn, and make information available for decision-making, making recommendations for more efficient use, and this advance has enabled already routine applications, such as word processing, facial, and voice recognition [9].

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With regard to natural language processing, it is an arm of AI that is dedicated to the translation of human languages for the machine, going beyond the simple transcription of voice by words, this area studies details of human dialog, such as the double meaning in words, the tone of voice, among other aspects that involve the diversity of natural language. Considering that this linguistic processing is present in the analysis of large volumes of voice data, in which virtual intelligence machines learn to identify accents, expressions, vocabulary patterns, with double meaning, tones, and other properties of speech[10]. Big Data is a resource focused on describing the huge amount of data that are generated by current technologies, taking into account that most of the time, these data are unstructured, which at first glance does not make sense. Besides derived from the importance of data for obtaining information, collecting references about users’ routines and data, extracting useful knowledge without AI can be time-consuming in some cases, which would be counterproductive, in this sense through the internet which brought the ease of collecting this data, and together with resources such as processing in Cloud Computing and AI, since Big Data is a computing model focused on processing and storing information in high volume, the solution that stores and processes data automatically and in practically real time; being allied with AI with a primary focus on data and image processing, with the aim of making the device or technology more intelligent and capable of reproducing human skills; that is, there is an intersection between AI and Big Data in relation to the AI algorithms that run in the Big Data environment and establish an effective communication between these two fields, which at the same time are different and complementary, that is, AI would be as a powerful digital human brain, which is able to store and process the information it receives from human experience such as reading, travel, crisis situations, among others, and based on this processing, it can suggest solutions on its own [11]. Today, the main advantage of using AI to capture information in Big Data is to be able to identify insights and patterns faster than human analysis, reducing the time spent on this procedure, being crucial in guaranteeing competitive advantages to any type of analysis of scenarios and contexts [12,13].

9.3

INTERNET OF THINGS

IoT is the term created that explains the fact that several objects used in daily life are connected through the internet, generating data and facilitating daily tasks, being a vast, complex, and adaptive network of devices with sensors, microchips, with processing and communication capabilities that interconnect people, machines, infrastructure, computing capacity, and systems over the Internet with security and privacy [1,14]. In addition to being an important resource for collecting information in Big Data, allowing accessories, cameras, sensors, appliances, phones, drones, and many more devices and things to connect to the network, facilitating the daily life of human life, such as residential security that alert residents in real time, via smartphone, in case of break-in and invasion, are forms of IoT, just as this technology is economically and socially impacting different sectors and contexts, making cities smarter, rationalizing, and flexibilities logistics and transportation of goods, production, even providing

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remote monitoring of patients, in the context of health, the use of wristwatches that measure the number of steps in the day, heart rate, and many other data on the quality of life and health of the patient-user. In the same sense as allowing agribusiness, the optimized use of inputs, improving energy efficiency, reducing the risks of work and operation in an industrial plant, expanding access to services in the financial sector, enabling new business models based on use for insurers and rental companies [15]. IoT sensors can assure the population of the quality of the water supply, the levels of pollution and radiation in each location, among other applications of equal economic and social impact, since its advent there is already more equipment connected to the internet than people on the planet, where the internet stops connecting only people and starts to connect the things that surround them [16]. The information that IoT devices generate, they also make available a large amount of them, from a smoke detector generating sporadic events to a video surveillance system generating several events per minute, which instead of issuing a movement alert in an area, the vision system can recognize shapes, objects, and people and also inform by voice messages regarding the masked individual, where users cannot deal with a large number of events or alert messages, which can cause the human error and increase the level of risk, since combined with the resources of AI, a system can make analyses and learn in a fraction of a second the behaviors of the device, making recommendations to the user, or even taking specific actions instead of just sending alerts to someone who will make the decision [17]. The technology that uses sensors in fire prevention evolves considerably, given its extreme importance and that this type of tragedy represents a great risk to life and the environment, where one of the main causes of desertification in many countries is related to this type of incident which is also the cause of many tragedies in buildings and factories, since low humidity and high temperatures are two factors that when combined contribute significantly to the beginning and spread of forest fires, which these and other factors can be monitored through intelligent sensors helping to create more efficient prevention strategies [18]. From the collection of data from sensors or geographically distributed third parties, it is possible to transform this large volume of data, which can be handled with Big Data, into intelligent and accurate predictive recommendations and strategic information, leveraging the best of both worlds in terms of processing, analysis, and learning much faster than the human dimension. IoT sensors can be installed on almost any imaginable object, where through a mesh of mobile sensors and fixed sensors, they can be located in vehicles, enabling the construction of complete coverage to monitor the climate and atmospheric data of a given region. This data can be sent to cloud-based analytical software that performs real-time processing based on intelligent predictive forecasting algorithms, the main data being transmitted to a system that can trigger alerts when highly dangerous fire conditions occur, managing teams or smart devices, such as drones, to fight outbreaks of fire before they cause destruction and spread [15]. In very large areas, it can be difficult to take preventive actions; however, with the use of sensors, this is a possible task, identifying the fire spots in advance to prevent the fire from spreading and causing greater tragedies, even considering that certain types of sensors in the ability to identify the abnormal rise in temperature or even

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the presence of CO and CO2. The great advantage is that the sensors can indicate the location of the start of the fire allowing the firefighting teams to be much more efficient and the control to be carried out even in its initial phase. Still considering the possibility of integrating AI solutions that make the prevention systems safer and more efficient in the management of fire risks, derived from the continuous data collection of the IoT devices and the use of sensors that communicate automatically allowing the emission of messages and alerts that indicate the conditions of the monitored area, as well as the number of people who are in the location. In buildings and industries, these devices can be connected to alarm systems and firefighting systems, which can be vital in saving lives, since knowing where a fire is occurring is invaluable, which through sensors IoT confirm this accurately, showing not only where the fire started but where it is spreading and how quickly [18,19]. In earthquake scenarios, drones can be used in mapping-affected areas, assessing damage, and assisting in the search for missing persons, also using drones to record damage, covering areas where hours would be spent traveling on the ground [19]. More than registration, drones can be used to support rescue teams and to monitor structures, registering cracks and cracks in a wall, still considering functions such as radars and piezometers for the remaining daily monitoring. In the same sense that drones assist in search and rescue activities, being essential due to the difficulty of locomotion in a certain type of terrain, serving as an extension of the vision of the rescuers [20]. More than to register environmental accidents, sensors and drones can be used for inspections and monitoring of structures with a focus on reducing the risks of material and personal accidents, both for the populations surrounding the developments and for the workers involved in the productive activities In this sense, IoT technology is in constant technical evolution, opening up new possibilities for future uses, contributing to the prevention of accidents, reducing the response time to a disaster, and minimizing the effects on those affected [21].

9.4 THE USE OF IOT AND AI FOR RISK AND DISASTER MANAGEMENT The technological change that IoT generates for people’s lives goes beyond the daily facilities as seen in the automation of a smart house, providing connectivity to objects and making room for intelligent commands in the various daily tasks, in addition to significantly increasing productivity at work, improving public and private security conditions, improving urban mobility as well as streamlining industrial processes, among others [22]. With IoT, it is possible to obtain data on human trends that were previously unimaginable, from generic information on behaviors that make up the volume of data known as Big Data, making human difficulty regarding the translation of all this data accumulation with traditional methods, since, in addition to this large amount, it is difficult to guess what information will be found, which by AI is possible due to natural language processing, generating valuable values and insights. Combining these aspects, the computer is shaped according to the information it finds, where throughout the process, it changes with the results delivered, through machine learning [23].

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This process of generating, analyzing data, and creating insights can be used by several sectors, such as agribusiness, commerce, industry, health, transportation, public services, among others. Just as the IoT together with AI contribute to disaster management to be qualified by employing new technologies to identify situations and act preventively, avoiding the loss of assets and resources, as well as people’s lives [24]. These technologies range from sensors to algorithms capable of reading and analyzing the information captured, making it possible to offer reliable data for the management of these disasters, provided that the use of wireless sensor networks, adopting sensor networks based on IP as well as using emerging standards for IoT, for data collection and the use of machine learning techniques oriented on top of the information collected from these sensors for the prediction of natural disasters are viable options, due to such technological trends have shown over time to be promising aggregating in the forecast natural disasters and environmental monitoring task [25]. In the industrial context, natural disasters are a serious problem since until a few decades ago, there was no way to deal with them, since everything that could be done to reduce risks was related to backup ensuring access to data, the construction of safe structures for installing companies’ machinery and purchasing insurance, ensuring possible material damage [26]. Thus, with the advent of IoT, it generated an impact allowing for the prior knowledge of risks and disasters, which represents a competitive advantage, from an industrial and business point of view, since IoT devices work under less than ideal conditions, in relation to scenarios where there is a very weak wireless connection or few sources of energy, with the need to use technologies such as LoRa (Low Power) to use and perform triage and detect the beginning of tectonic activities with precision, for example, with the IoT changing how to face problematic situations and can even save lives [27]. As the batteries of these devices do not need to be replaced frequently, these sensors can be installed in remote areas of difficult access, in a scenario of a flood or tsunami, the pressure and humidity sensors perform detection of a submerged bridge, giving a preview of the situation and transmit the information to an environmental monitoring center, which will have knowledge of the time that occurred, still being able to warn the population about possible alternative escape routes as well as having the intensity of the damage [28]. AI is associated with intelligent objects that can transform disaster prevention into a much more accurate task, using sensors and drones, and even robots, being a way to better coordinate actions in areas of risk in the same way as ensuring that service providers may have the necessary capabilities to understand the situations in which they are inserted more quickly [29]. The fire brigade can process the heat areas in a building with intelligent software to trace more efficient routes through computer vision and can reach victims faster, since these resources allow professionals to be placed at less risk and can do their job with greater chances of success. The response to AI disasters is a trend with a concern for getting a complete view of the problems before taking action, since this complete view can help in the definition of smarter plans by assisting in the preparation with the right equipment and training aiming to mitigate the effect of disasters [30].

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As with the use of machine learning, which is an aspect of AI, in online mining with a focus on identifying tweets made in emergencies, forwarding this information to networks of volunteers and governments, acting on the verification of which areas were most affected and what were the needs of the people in those places, promoting actions of teams that are able to send resources, such as water and food, in the same sense as addressing problems related to damaged infrastructure more quickly, nurturing the latest information about the case [31]. In addition, it is important to highlight the great importance of investing in methodologies capable of transmitting a large flow of information with low memory consumption of the devices as well as low abstraction. These characteristics are essential for sending and receiving a massive flow of data in disaster situations [32–37]. In the same sense, at times of the year, such as summer, tropical storms are approaching, with tropical countries like Brazil in certain locations causing the population and public service agencies to be on the alert, with relations to heavy rains common at this time that cause numerous inconveniences, from interruptions in the supply of crucial services such as water and electricity, causing cities to considerably alter traffic in relation to floods that destroy neighborhoods, to power outages after a summer storm cause ripple effects and contribute to the feeling of chaos in the general population. However, this type of chaos scenario can be avoided with organization, planning, and preparation in advance, with respect to the use of IoT sensors that can be used to monitor a variety of climatic conditions such as raindrops or snow, determining and anticipating possible situations of chaos with the help of a system that processes data histories providing forecasts, in the same way as the use of an AI system that helps to identify and order the services that will be performed and the professionals that need to be scaled assessing their skills and determining your location relative to the emergency, prioritizing calls, sending the right people closest to the location and resolving the problem as quickly as possible [38,39]. Thus, the union of IoT with AI for disaster management is a way to qualify these processes, ensuring that a company or even the government or state is prepared for unforeseen events, since they have technological resources such as geological risk sensors, which can predict landslides, sinking and burning in the field, forecasting storms and snowfalls, helping to prevent these activities from causing damage to society. Still considering the need for a tool that allows managers to efficiently organize emergency tasks, following the parameters of the institution itself or regulatory agencies and selecting what should be prioritized, based on clear and predefined rules.

9.5

THE IOT RELATIONSHIP IN THE SUPPLY CHAIN DURING DISASTER

IoT can be applied throughout the supply chain, bringing benefits to the various logistical functions such as storage, inventory management, transportation, meeting demand, and customer service, for example. IoT resources can help promote a digital ecosystem of connected systems, providing users with relevant and up-to-date data to make a more accurate decision at any given time. These benefits range from reducing costs by reducing waste, reducing resource consumption, and better use of assets to improving the service level by adding time, place, quality, and information [40].

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IoT enables a new level of operational efficiency, in addition to creating leverage automation and IoT solutions for intelligent manufacturing operations to mitigate the dependence on labor-intensive processes made possible by digital technology, allowing the standardization of daily work and assisting it, relieving the pressure of relying on specific individuals to make an operation work [40]. The idea of the Intelligent Supply Chain is yesterday a digital perception and vision through the IoT regarding environmental crimes, minor and moderate disasters, such as floods, volcanic eruptions, and earthquakes and social losses, which forces the optimization of the Supply Chain requiring an independent preparation in relation to climate and natural disasters. With recent weather patterns too unusual around the world, companies need to assess the risk of downtime and interruptions by updating their risk management strategies to deal with global climate change [41]. After all, through the virtualization of processes and automation brought by IoT, it is possible to completely transform the production process, in a short time, since these practices together with environmental responsibility and concern for the customer create a network of strategies to optimize the process productive, reducing losses, failures, and negative impacts [41]. IoT and visual recognition technology through installed sensors are able to better manage demand in refrigerators installed in convenience stores, restaurants, and supermarkets, since it is possible to increase the visibility of the inventory and to respond better to an event such as the coronavirus outbreak. COVID-19 currently plaguing the world, even though distributors cannot provide forecasts [42–44]. Considering that logistics in crises is an area applicable in situations of disasters and catastrophes, in which the strategic objectives of the supply chain, classically aimed at reducing operating costs and investments together with improving the service level, in a context emergency, seeks to maximize the service level with the shortest possible delivery times. Considering IoT-based business intelligence related to the fleet of vehicles, sensors, equipment, cameras, and many other “things” exchanging information in real time [45,46]. Since the ability through IoT technology to integrate all this data, and transform it into intelligence with Data Analytics tools, for example, is that it guarantees the efficiency of the logistics chain, reducing costs, making routes more flexible, among other actions that generate more value for the business. Bearing in mind that modern supply chains are vulnerable to natural disasters, weather, which causes transport delays and unexpected quality problems that can interrupt the flow of cargo, generating short-term costs, and delivery challenges, which can be circumvented by IoT, AI through sensors, drones, and other autonomous devices, which made the logistics area a change to a strategic and no longer operational role in companies [47].

9.6

DISCUSSION

The historical records that describe the failure of mining dams and derivatives show the occurrence of several tragic events in the last decades, pointing to a tendency of changing events from developed countries to developing countries, since in these countries, the mining activity is very significant, as, in Brazil, it is, therefore, essential to rely on the use of technology to prevent this type of tragedy.

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The recent socio-environmental disaster in Brumadinho-MG [48–60], Brazil, in January 2019, turns on a yellow light, making an analogy with traffic, in relation to how to prevent this type of disaster from occurring again, in the same direction in which ways should be implemented and developed together with several alerts of possible new dam disruptions in regions like this, still taking into account the occurrence of similar episodes such as the one in Mariana[61–63], in November 2015, corresponding to 4 years before with respect to the Brumadinho tragedy, in the same Brazilian state; however, particularly technology could have helped to prevent or at least minimize catastrophes of these proportions. Which in this sense is evidently clear why disaster response is needed with IoT structure, since this type of disaster can be avoided with the use of IoT devices and sensors, as previously mentioned in previous sections, which capture proximity, accelerometer, temperature, humidity, and several other properties of the environment in which they are installed, carrying out monitoring and supporting the prevention of natural disasters of this type. In addition to the flaws in the dam structure itself, analysts point out that there was a failure to immediately communicate the situation to employees and those close to them. Initially, when there is some kind of failure at the dam that could cause life-threatening, a strong beep would be released, and entertainment would not occur. This reality, although undeniably tragic, brings us lessons and makes us think of new technological solutions that are not so active that prevent disasters such as instantaneous communication of risks like the next people. Thinking of a context that never expects disasters to occur and that fewer people suffer or inhabit the use of applications that use disaster notification notifications, it is necessary to think of technologies that stick together in the human body and within people’s field of vision. Thus, one of the alternatives would be the free distribution of wearable and functional devices from companies subject to disasters. One of the alternatives would be the real time of the monitoring data of a bracelet or smartwatch that would be delivered to employees at the time of their admission to the company, being its mandatory daily use. In addition, these devices can be used periodically for maintenance. If this proposal is already in reality in the context of the city of Brumadinho, the alerted employees capture, through vibration, light signals released by their wearable devices, in order to encourage people to read on the screen of their watches or who were as well as what is the escape protocol must have been adopted. Failures in quality and safety systems in companies are not new, as the 20th century was marked by the Chernobyl disaster. This catastrophic nuclear accident occurred between April 25 and 26, 1986, in nuclear reactor No. 4 at the Chernobyl Nuclear Power Plant, near the city of Pripyat, in northern Soviet Ukraine, close to the border with Soviet Belarus. The plant had suffered a power overload during a capacity test. The cooling system stopped working, which generated an overheating of the core, which reached very hot temperatures. If at that time, wearable technologies were already as common as today, the proposal presented here, where the warnings are triggered in real time and in order to attract attention, perhaps alive ones could have been spared.

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Unfortunately, there is no evidence that Brumadinho’s social, environmental, and ecological disaster will be the only one to occur in this century or the next. Thus, the best solution is to invest in intelligent technologies, which can also be applied in other contexts such as mining, oil and natural gas platforms, chemical industries, thermal nuclear plants, among others. Nowadays, there are technological solutions based on sensors integrated with IoT devices that help prevent sliding on hillsides and hills from multiple angles, considering that people are surrounded by technologies that have been revolutionizing and developing in several areas, pointing out solutions for assistance and management of numerous activities. Thus, landslides are ruptures of a rock or landmass that are always preceded by various types of “preruptures,” since the magnitude of these prerupture deformations depends on the type of rock or soil involved, which employing the current sensor technology that would be spread over the dam forming a network that would be able to measure and detect any type of abnormality, considering the most different vibrations from truck traffic to dynamite explosions, being measurable, which allows managing this type of incident. In other words, the secret to managing landslide risks is forecasting; however, it can only be done if the earth movement or, more importantly, the acceleration of the landmass can be measured millimetrically constantly. Looking at the specific case of the broken barriers in the Brazilian state of Minas Gerais (MG), drone technology could have been a great potential, enabling the inspection of assets quickly and efficiently and allowing the detection of nonconformities and risks in advance, which would open premise to find and implement effective solutions. Considering that the traditional method of measurement involves sending inspectors or engineers to the field frequently, or even monitoring by piezometers and water level indicators, with two meters generally being installed at points far from each other, where only it is possible to see the water level in the places where these sensors are located; however, between one instrument and another, something unusual may be happening, or even daily, for measuring the movement of the earth with theodolites, which is a manual and expensive process. What led and until today, these tailings dams are managed through manual monitoring systems, involving periodic visits to selected points along the dam, performing manual measurements with a variety of instruments, most of which are level meters of water, piezometers, or inclinometers, which concerns that this type of measurement does not produce enough data to carry out evaluations in a consistent manner with respect to the performance of the embankment dam. As some advantages in the use of drones capture images with very high resolution, adding the use of computer vision and AI that help in the identification of structural flaws with superhuman precision, even considering that the IoT present solutions that can make the process of preventing more dynamic, inexpensive, and effective slips with respect to the use of geotechnical sensors that emit an infrared signal, which is reflected in mirrors strategically installed in the monitored area, making these intelligent sensors manage to capture even small earth movements, covering an area 360 degrees and up to kilometers long. With the respective approach that monitors dams effectively, daily and accurate measurements must be carried out, and in this philosophy, the technology provides without exhaustion.

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In addition to speed, centimeter accuracy, and image processing efficiency, drones can use the most varied and accurate sensors, performing in the context of dams, routine inspections with thermal sensors pointing out areas with signs of leakage and being able to generate alerts for employees and nearby communities, since failures in tailing dam structures can be caused by different factors, in addition to the most common ones related to anomalous behavior of the material used to build the dam, overloads, or even problems with the drainage mechanisms resulting increase in water pressure and consequently loss of resistance. In this sense, piezometric sensors provide accurate information about water pressure in tubes and tanks, and it is still possible to use sensors to measure the horizontal displacement of the soil in-depth, in an automated way using an inclinometer, for example. Still taking into account that after the tragedy of a rupture, in addition to the thermal sensors pointing out areas with signs of leakage-generating alerts for security measures to be taken, such as the evacuation of places and cities, preventing the loss of human lives, these sensors could be used to support finding victims, based on the identification of temperatures that are not homogeneous with clay. Another possibility of using sensors to prevent dam ruptures is the waste meters which measure the water level in the dam monitoring the settlement processes in the surroundings and walls, as well as the use of IoT devices with wireless GPS sensors, which can be installed at strategic points on hills and slopes, enabled with cloud-based algorithms performing calculations with respect to the exact location of each device informing its movement, based on its initial fixed location, allowing daily measurements to be made in several points in a risk area, with no line of sight or cabling requirements, or even human intervention on the site, setting up alerts for significant movements that may indicate a greater risk of landslides. In addition, with the advancement of connected sensors, originating from initiatives linked to IoT, other continuous monitoring devices for both dams and other types of assets can be adopted, linked to warning systems, with audible and visual signals, as long as there are technologies that mix the use of inclinometer and accelerometer sensors in combination with drones capturing centimeter-precision images linked to intelligent management and control systems pointing out even the slightest signs of terrain movement. Which shows that nowadays with the technological advent of IoT, there is an infinity of applications available for use in different segments, considering the smart devices for weather forecasting, since heavy rains are one of the main causes of this type of incident, this type of sensors are useful in preventing slippage. At a public administration level, sensors at meteorological stations are able to measure variables such as wind speed and rain, since when predicting a large volume of rain in a risk area, it is possible to make decisions regarding evacuation of the area or other security measures, in order to avoid major tragedies, for example, considering that this type of data obtained by these sensors is critical in relation to the monitoring and planning of climatic scenarios in dam maintenance actions with respect to the context of changes in increasingly unstable weather patterns. Thus, the combination of technologies is essential for a careful assessment of risk scenarios, especially those involving greater complexity.

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9.7 FUTURE TRENDS The connectivity of IoT devices will reach its full potential when 5G technologies allow high speed and low latency, where 5G will make IoT much more efficient and effective considering a spectrum of efficiency where each device and network created based on this technology it will use only what is necessary and when it is necessary, in the exact measure [64]. As well as the ‘NarrowBand’-IoT (also known as LTE-M2) that provides long-range coverage with low battery consumption for devices that are present in all day-to-day environments, where it is considered the driver of communications current and future wireless, with narrowband specification (NB-IoT) as a new physical layer, whose application offers and shows the ease of making measurements with sensors using LPWAN technology that does not operate in the licensed LTE construction. Operating in ways independent of the unused 200 kHz bands that were previously used for GSM (Global Mobile Communications System), on LTE base stations, a block of resources is allocated for NB-IoT operations or in their guard bands [65]. Since IoT works only when devices are connected to the internet, in this sense, the importance of 5G for the advancement of IoT in the coming years is fundamental, being the first network planned to operate with a response time n times shorter than the network 4G, and with low latency, 5G is the solution to more efficiently connect programmed appliances, autonomous cars, the most diverse types of sensors, among countless other possibilities, so 5G appears as a solution capable of absorbing simultaneous connections, efficiently and with less energy consumption, being a network programmed to respond in real time, in addition to being scalable and versatile [47,64]. The evolution of technologies such as IoT, big data, wearables, in addition to the increase in the use of mobile devices has required a greater performance of the internet, and the connection to the web, however, has not yet reached its full potential, which 5G broadband will provide more stability and more capacity to meet the needs of mobile users, leading the Internet to tend to become increasingly faster to meet these new demands that are emerging over the years with respect to IoT devices [66]. With the fifth generation of wireless technology (5G), it will reach the IoT revolution, evolving and enabling the entire context and management of devices with a focus on disaster management, enabling their link and even greater creation of interconnected and intelligent cities, since the new network tends to respond to critical needs, which need extremely short response times [67]. However, although the advantages are many, with the increase in requirements coming from 5G and the densification of the network, the security problems inherited from previous generations will become more complex, one of them can be related to authentication which is an essential operation in mobile networks requiring hundreds of milliseconds to be performed; however small that time may seem may be incompatible with the low latency and mobility of 5G, considering that the new generation of networks has ultralow latency characteristics to cope with the growth associated with IoT [66]. In this way, the 5G network should increase the speed of the internet and the connected smart devices, creating an environment with the organization for the coexistence between mobile devices and millions of connected objects, which, if well

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planned, could become a fully structured environment regarding the prediction of risk scenarios and disaster management, in addition to helping in the development of smart cities with a focus on efficiency [68].

9.8 CONCLUSIONS One of the main uses of IoT for disaster management is the possibility of monitoring with real-time location systems; along with other tools and approaches, the administration is able to ensure more care and prevention with the main objective of ensuring that these smart devices send information collected in the field for mobile devices, assisting and facilitating the response and necessary human decisions put into practice. After a natural disaster, a drone provides a quick means of navigating through debris with portable technology and information collection that can be used by teams working in a specific area, provided that this device is equipped with high-definition radars and cameras, supporting rescuers by providing access to a higher field of view, and due to its small size provides an approximate view of areas where larger air vehicles would prove to be inefficient or dangerous, disregarding the need to use resources with manned helicopters. There are different types of applications that can be used in the monitoring of dams, for example, achieving a higher level of safety, since the use of sensors is very effective as it provides accurate and continuous measurements, without the need for the displacement of specialized professionals, enabling an effective cost reduction, combining the use of different equipment in order to obtain a comprehensive view of possible modifications that increase the risk of breakage, provided that with thermal sensors, being especially useful at night or in difficult terrains, achieving quickly discover the location of lost people, and can also be used in delivering supplies to an inaccessible location, taking into account that time is a crucial factor in search and rescue missions, particularly in adverse conditions, still considering the ease of deployment of those devices. Drones can also reach hard-to-reach places, such as mountains and eroded coastline, by acquiring high-resolution data by creating 3D maps, allowing data collection, and instant download of images. In this way, the importance of interrelating different technologies and knowledge areas, such as IoT and AI in the context of disaster management are based on reducing the damage caused by climate change as well as being at the forefront of natural disasters with the possibility of predicting an event due to the acquisition of data and monitoring, assisting and feeding through these data obtained the functional communication systems, applied in cases of the rescue of victims and sharing of information. Although, the smart devices in the context of developing countries, although it seems something distant, from studies and collaborations of these different areas of knowledge, this idea already showing signs of being a reality.

REFERENCES 1. Tran, C., and Misra, S. The technical foundations of IoT. IEEE Wireless Communications, 26(3), 8, 2019.

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2. Garrido, J., and Saunders, W. S. A. Disaster risk reduction and land use planning: Opportunities to improve practice. In: IAEG/AEG Annual Meeting Proceedings, San Francisco, California, 2018-Volume 5 (pp. 161–165). Springer, Cham, 2019. 3. Westra, L., Bosselmann, K., Gray, J., and Gwiazdon, K. (Eds.). Ecological Integrity, Law and Governance. Routledge, London, 2018. 4. Shi, P. Disaster Risk Science. Springer, Cham, 2019. 5. Kilmer, R. P., Gil-Rivas, V., Tynan, J. M., and Larson, J. C. Natural disasters, 2019. 6. Hostettler, S., Besson, S. N., and Bolay, J. C. (Eds.). Technologies for Development: from innovation to Social Impact. Springer, Cham, 2018. 7. Brynjolfsson, E., and Mcafee, A. N. D. R. E. W. The business of artificial intelligence. Harvard Business Review, 1–20, 2017. 8. Ransbotham, S., Gerbert, P., Reeves, M., Kiron, D., and Spira, M. Artificial intelligence in business gets real. MIT Sloan management review, 60280, 2018. 9. Flasinski, M. Introduction to artificial intelligence. Springer, Berlin, 2016. 10. Seyedmahmoudian, M., Horan, B., Soon, T. K., Rahmani, R., Oo, A. M. T., Mekhilef, S., and Stojcevski, A. State of the art artificial intelligence-based MPPT techniques for mitigating partial shading effects on PV systems–A review. Renewable and Sustainable Energy Reviews, 64, 435–455, 2016. 11. Erl, T., Khattak, W., and Buhler, P. Big Data Fundamentals: Concepts, Drivers  & Techniques. Prentice-Hall Press, Upper Saddle River, NJ, 2016 12. Zomaya, A. Y., and Sakr, S. (Eds). Handbook of Big Data Technologies. Springer, Berlin, 2017. 13. Russell, S. J., and Norvig, P. Artificial Intelligence: A Modern Approach. Pearson Education Limited, Malaysia, 2016. 14. Adryan, B., Obermaier, D., and Fremantle, P. The Technical Foundations of IoT. Artech House, Norwood, MA, 2017. 15. Solanki, V. K., Díaz, V. G., and Davim, J. P. (Eds.). Handbook of IoT and Big Data. CRC Press, Boca Raton, FL, 2019. 16. Pacheco, J., and Hariri, S. Anomaly behavior analysis for IoT sensors. Transactions on Emerging Telecommunications Technologies, 29(4), e3188, 2018. 17. Yadav, N., and Badal, N. Introduction to IoT technologies and its applications. In: P. K. Malik, V. Jain, A. Gehlot, R. Singh (Eds.) Handbook of Research on the Internet of Things Applications in Robotics and Automation (pp. 238–264). IGI Global, Hershey, PA, 2020 18. Rosas, J. C., Aguilar, J. A., Tripp-Barba, C., Espinosa, R., and Aguilar, P. A. Mobile-sensor fire prevention system based on the internet of things. In: International Conference on Computational Science and Its Applications (pp. 274–283). Springer, Cham, 2017. 19. Cui, F. Deployment and integration of smart sensors with IoT devices detecting fire disasters in huge forest environment. Computer Communications, 150, 818–827, 2020. 20. Motlagh, N. H., Bagaa, M., and Taleb, T. UAV-based IoT platform: A crowd surveillance use case. IEEE Communications Magazine, 55(2), 128–134, 2017. 21. Savitha, N., and Malathi, S. A survey on fire safety measures for industry safety using IoT. In: 2018 3rd International Conference on Communication and Electronics Systems (ICCES) (pp. 1199–1205). IEEE, Coimbatore, India, 2018. 22. Benis, A., Notea, A., and Barkan, R. Risk and disaster management: From planning and expertise to smart, intelligent, and adaptive systems. In: MIE 2018 European Federation for Medical Informatics (EFMI) (pp. 286 –290). IOS Press, Gothenburg, Sweden, 2018. 23. Ray, P. P., Mukherjee, M., and Shu, L. Internet of things for disaster management: Stateof-the-art and prospects. IEEE Access, 5, 18818–18835, 2017. 24. Saha, H. N., Auddy, S., Pal, S., Kumar, S., Pandey, S., Singh, R., and Saha, S. Disaster management using Internet of Things. In: 2017 8th Annual Industrial Automation and Electromechanical Engineering Conference (IEMECON) (pp. 81–85). IEEE, Bangkok, Thailand, 2017.

166

Big Data Analytics in Supply Chain Management

25. Wellington, J. J., and Ramesh, P. Role of Internet of Things in disaster management. In: 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS) (pp. 1–4). IEEE, Coimbatore, India, 2017. 26. Alcaraz, C. Security and Privacy Trends in the Industrial Internet of Things. Springer, Cham, 2019. 27. Sinha, R. S., Wei, Y., and Hwang, S. H. A survey on LPWA technology: LoRa and NB-IoT. ICT Express, 3(1), 14–21, 2017. 28. Krytska, Y., Skarga-Bandurova, I., and Velykzhanin, A. IoT-based situation awareness support system for real-time emergency management. In: 2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) (Vol. 2, pp.  955–960). IEEE, Bucharest, Romania, 2017. 29. Li, B. H., Hou, B. C., Yu, W. T., Lu, X. B., and Yang, C. W. Applications of artificial intelligence in intelligent manufacturing: A review. Frontiers of Information Technology & Electronic Engineering, 18(1), 86–96, 2017. 30. Goodfellow, I., Bengio, Y., and Courville, A. Deep learning. MIT Press, Cambridge, MA, 2016. 31. Buduma, N., and Locascio, N. Fundamentals of Deep Learning: Designing NextGeneration Machine Intelligence Algorithms. O‘Reilly Media, Inc, Newton, MA, 2016. 32. Fança, R. P., Iano, Y., Monteiro, A. C. B., and Arthur, R. Improvement for channels with multipath fading (MF) through the methodology CBEDE. In: H. Hussein, and S. M. A. El-Kader (Eds.) Fundamental and Supportive Technologies for 5G Mobile Networks (pp. 25–43). IGI Global, Hershey, PA, 2020. 33. França, R. P., Iano, Y., Monteiro, A. C. B., and Arthur, R. A proposal of improvement for transmission channels in cloud environments using the CBEDE methodology. In: B. B. Gupta (Eds.) Modern Principles, Practices, and Algorithms for Cloud Security (pp. 184–202). IGI Global, Hershey, PA, 2020. 34. França, R. P., Iano, Y., Borges Monteiro, A. C., and Arthur, R. A proposal to improve channels with rician fading through the methodology CBEDE. International Journal of Simulation--Systems, Science & Technology, 20, 1–5, 2019. 35. Padilha, R., Iano, Y., Monteiro, A. C. B., Arthur, R., and Estrela, V. V. Betterment proposal to multipath fading channels potential to MIMO systems. In: Y. Iano, R. Arthur, O. Saotome, V. Vieira Estrela, H. Loschi (Eds.) Brazilian Technology Symposium (pp. 115–130). Springer, Cham, 2018. 36. França, R. P., Iano, Y., Monteiro, A. C. B., Arthur, R., Estrela, V. V., Assumpção, S. L. D. L., and Razmjooy, N. Potential proposal to improvement of the data transmission in healthcare systems. Deep Learning Techniques for Biomedical and Health Informatics, 2020, 267–283, 2019. 37. Padilha, R. F. Proposta de um método complementar de compressão de dados por meio da metodologia de eventos discretos aplicada em um baixo nível de abstração= Proposal of a complementary method of data compression by discrete event methodology applied at a low level of abstraction, 2018. 38. Ueyama, J., Faiçal, B. S., Mano, L. Y., Bayer, G., Pessin, G., and Gomes, P. H. Enhancing reliability in wireless sensor networks for adaptive river monitoring systems: Reflections on their long-term deployment in Brazil. Computers, Environment and Urban Systems, 65, 41–52, 2017. 39. Wilson, H. J., Daugherty, P., and Bianzino, N. The jobs that artificial intelligence will create. MIT Sloan Management Review, 58(4), 14, 2017. 40. Hugos, M. H. Essentials of Supply Chain Management. John Wiley & Sons, Hoboken, NJ, 2018. 41. Mangan, J., Lalwani, C. Global Logistics and Supply Chain Management. John Wiley & Sons, Hoboken, NJ, 2016.

Overview of the IoT Technologies

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42. Karnik, T. Technology trends, requirements and challenges for ubiquitous self-powered IOT systems deployment. In 2018 Ninth International Green and Sustainable Computing Conference (IGSC) (p. 1). IEEE, 2018. 43. Ting, D. S. W., Carin, L., Dzau, V., Wong, T. Y. Digital technology and COVID-19. Nature Medicine, 26(4), 459–461, 2020. 44. Singh, R. P., Javaid, M., Haleem, A., Suman, R. Internet of things (IoT) applications to fight against COVID-19 pandemic. Diabetes  & Metabolic Syndrome: Clinical Research & Reviews, 14, 521–524, 2020. 45. Tsubaki, T., Ishibashi, R., Kuwahara, T., Okazaki, Y. Effective disaster recovery for edge computing against large-scale natural disasters. In: 2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC) (pp.  1–2). IEEE, Las Vegas, USA, 2020. 46. Reddy, D. R., Kumar, K. S., Malasri, A., Praveena, B., & Nikitha, M. Flood monitoring & detection system using internet of thing (IOT). Clio: An Annual Interdisciplinary Journal of History, 6(2), 590–597, 2020. 47. Malarvizhi, N., Annamalai, S., Raj, P., Neeba, E. A.,  & Lin, J. W. Industrial IoT Application Architectures and Use Cases, 2020. 48. Watson, K. ‘Vale ended our lives’: Broken Brumadinho a year after dam collapse. https://www.bbc.com/news/world-latin-america-51220373 (Accessed January 27, 2020) 49. Lopes, M. After Brazil’s mining disaster, a wasteland is left behind. https://www.washington post.com/graphics/2019/world/amp-stories/ brazil-dam/ (Accessed January 27, 2020) 50. Monde, L. Brésil: après la rupture d’un barrage minier, les chances « minimes » de retrouver des survivants. https://www.lemonde.fr/international/article/2019/01/25/ rupture-d-un-barrage-minier-au-bresil-environ-200-disparus_5414724_3210.html (Accessed January 27, 2020) 51. Brito, R., Stargardter, G. Brazil dam disaster likely had same cause as previous one: official. https://www.theglobeandmail.com/world/article-brazils-vale-knew-of-risk-toarea-hit-by-deadly-mine-disaster-report/ (Accessed January 27, 2020) 52. The New York Times. Why Did the Dam in Brazil Collapse? Here’s a Brief Look. https:// www.nytimes.com/ 2019/ 02/ 09/ world/americas/ brazil-dam-disaster.html (Accessed January 27, 2020) 53. The New York Times. As 2nd Brazil Dam Threatens to Collapse, Death Toll Rises to 58. https://www.nytimes.com/2019/01/27/world/americas/ brazil-dam-brumadinho. html (Accessed January 27, 2020) 54. BBC. Brazil dam collapse: The crucial questions. https://www.bbc.com/news/worldlatin-america-47111908 (Accessed January 27, 2020) 55. MacDonald, A. Brazil Dam Collapse Prompts Potential Rule Shakeup for Miners. https://www.wsj.com/articles/industry-group-recommends-new-rules-for-miners-afterbrazil-dam-collapse-11573829109 (Accessed January 27, 2020) 56. BBC. Brumadinho dam collapse in Brazil: Vale mine chief resigns. https://www.bbc. com/news/ business-47432134 (Accessed January 27, 2020) 57. The New York Times. A mining dam collapsed and buried more than 150 people. Now Brazil is casting an anxious eye on dozens of dams like it. https://www.nytimes.com/ interactive/2019/02/09/world/americas/ brazil-dam-collapse.html (Accessed January 27, 2020) 58. Sá, G. Brazil’s deadly dam disaster may have been preventable. https://www.nationalgeographic.com/environment/2019/01/ brazil-brumadinho-mine-tailings-dam-disastercould-have-been-avoided-say-environmentalists/ (Accessed January 27, 2020) 59. Plumb, C., and Costa, L. Vale report blames water level for Brazil mining waste dam disaster. https://www.reuters.com/article/us-brazil-vale-brumadinho/vale-report-blames-waterlevel-for-brazil-mining-waste-dam-disaster-idUSKBN1YG1DK (Accessed January 27, 2020)

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Big Data Analytics in Supply Chain Management

60. Boadle, A., and Slattery, G. Brazil’s Vale knew Brumadinho dam was unsafe as early as 2003: internal report. https://www.reuters.com/a rticle/us-vale-dam-disaster/ brazils-vale-knew-brumadinho-dam-was-unsafe-as-early-as-2003-internal-reportidUSKBN20F058 (Accessed January 27, 2020) 61. Kiernan, P.; Jelmayer, R., and Johnson, R. Mining company samarco’s dam bursts in Brazil. https://www.wsj.com/articles/mining-company-samarcos-dam-bursts-inbrazil-1446763579 (Accessed January 27, 2020) 62. Bochove, D. Mining disasters show cost of cheap waste-storage solutions. https:// www.theglobeandmail.com/report-on-business/industry-news/energy-and-resources/ brazilian-dam-collapse-prompts-review-of-mining-waste-storage/article27323796/ (Accessed January 27, 2020) 63. Romero, S. Authorities assess toll of burst dam in Brazil. https://www.nytimes. com/2015/11/06/world/americas/authorities-assess-toll-of-burst-dam-in-brazil.html (Accessed January 27, 2020) 64. Mavromoustakis, C. X., Mastorakis, G., and Batalla, J. M. (Eds.) Internet of Things (IoT) in 5G Mobile Technologies (Vol. 8). Springer, Berlin, 2016. 65. Ratasuk, R., Mangalvedhe, N., Zhang, Y., Robert, M., and Koskinen, J. P. Overview of narrowband IoT in LTE Rel-13. In: 2016 IEEE Conference on Standards for Communications and Networking (CSCN) (pp. 1–7). IEEE, Berlin, Germany, 2016. 66. Firouzi, F., Farahani, B., Ye, F., and Barzegari, M. Machine learning for IoT. In: F. Firouzi, K. Chakrabarty, and S. Nassif (Eds.) Intelligent Internet of Things (pp. 243–313). Springer, Cham, 2020. 67. Holma, H., Toskala, A., Nakamura, T. 5G Technology: 3GPP New Radio. John Wiley & Sons, Hoboken, NJ, 2020. 68. Misra, S., Panwar, G. 5G spectrum and standards (Book Reviews). IEEE Wireless Communications, 24(1), 4–5, 2017.

10

Closing the Big Data Talent Gap Curtis Breville DM/IST, Dell Technologies, Denver, Colorado, USA

CONTENTS 10.1 10.2 10.3 10.4 10.5 10.6 10.7 10.8 10.9 10.10 10.11 10.12 10.13 10.14 10.15 10.16 10.17 10.18 10.19

Research Benefits | What’s in It for Me?................................................... 171 The State of Big Data Education .............................................................. 171 Data Scientist vs Data Analyst ................................................................. 172 A Qualitative Approach ............................................................................ 173 Dependability and Trustworthiness.......................................................... 176 Data Analysis ............................................................................................ 176 Big Data Initiatives ................................................................................... 177 Years of Big Data Initiatives ..................................................................... 178 Size of Big Data Teams ............................................................................ 178 Big Data Resources Needed ..................................................................... 178 Where Are Organizations Finding Big Data Resources? ......................... 179 Challenges Finding Big Data Resources .................................................. 180 Qualities Most Difficult to Find in Candidates ........................................ 180 The Ideal Big Data Specialist Candidate.................................................. 181 Number of Candidates Interviewed .......................................................... 181 Easing the Big Data Hiring Process ......................................................... 181 IT Manager Interviews ............................................................................. 182 Specialist Interviews ................................................................................. 182 Key Analysis & Findings.......................................................................... 183 10.19.1 Theme 1: “Lacking” .................................................................. 183 10.19.2 Theme 2: “Passion” ................................................................... 183 10.19.3 Theme 3: Soft Skills .................................................................. 184 10.19.4 Theme 4: Technical Skills......................................................... 184 10.20 Conclusion ................................................................................................ 184 10.21 Discussion ................................................................................................. 185 References .............................................................................................................. 185 Manufacturing aside, predictive analytics made from big data is affecting, and will continue to affect, every industry on earth. Artificial/Augmented Intelligence (AI) has demonstrated an ability to improve quality and efficiencies across multiple industries as well as create new business offerings. Despite the incredible promise big data brings, there has been a lack of available specialists in this discipline, leaving many businesses to lose market share, customers, and to miss opportunities. Eighty-four 169

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percent of organizations believe that insights generated from their data provide them with a competitive advantage (Columbus, 2014). Let’s get to it: If there’s a lack of big data specialists, where are businesses’ finding their big data resources? Are they recruiting from universities, training existing employees, hiring consultants, or searching other resource pools for qualified big data specialists? The answer to this question will help businesses and schools identify opportunities to close this gap of specialists and bring new discoveries and insights, faster, to the world. Back in 2014, a lack of available big data expertise left the business world with a shortage of 1.5 million people needed to analyze a massive amount of data being generated every minute (Manyika et al., 2011; “SAP”, 2014). In 2018, almost five years later, the gap continued, with IDC reporting that just the US alone had a shortage of 181,000 people to fill deep analytic rolls and over 900,000 data management and interpretation roles. A LinkedIn Workforce Report written in 2018 supports the IDC numbers with a similar gap of 151,717 people with data science skills (LinkedIn, 2018). On a global scale, in 2017, PricewaterhouseCoopers (PwC) estimated that 2.7 million data science and analytics positions would be available with a shortage of 40%–60% qualified people to fill them (Rivett, 2017). Technical challenges have traditionally been met with a surplus of new college graduates, armed with the latest technology knowledge. Perhaps, it is the relative newness of big data, but it has been a general acceptance that most undergrad-level college graduates lack the domain or business experience to step into a true data scientist position. Big data analytics involves the application of new technology to businesses across multiple industries to provide new insights. This requires experience with an understanding of businesses and industries, which most recent graduates have yet to develop. The lack of explanation of what companies are doing to cope with this resource shortage can be deafening, especially to those businesses desperate to gain insights. If students are not graduating with the business knowledge needed, the new technologies including programming languages, hardware, and software tools are not standardized nor common across businesses, where are organizations finding qualified specialists with the ability to apply big data to gain new insights to benefit from? A person tasked with an understanding of how to capture, store, manipulate, cleanse, model, analyze, identify patterns, and communicate the benefits of big data is referred to as a “data scientist.” Such a person must be fluent in statistics, computer programming/coding, and have strong interpersonal and communication skills. People who possess all of these qualities are often referred to as “unicorns” because of how rare it is for a person possessing all of these qualities (Bakhshi, Mateos-Garcia,  & Whitby, 2014). People who work in the big data analytic space who may only have one or two of those qualities are often referred to as a big data specialist, and “data scientist” is reserved for those who have all of them. Throughout this chapter, both titles will be used interchangeably. Desperation drives innovation, right? A lack of available qualified big data specialists has forced IT Managers to find innovative ways to get the value of big data for their respective businesses. Prior to my research, knowledge around how they are doing this, what methods are successful, and what methods are not was not

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well-known. In response to this lack of knowledge, a qualitative exploratory case study using surveys and interviews was used to answer how US organizations were meeting their data science needs and explore the insights and perceptions of IT Managers in the United States about the big data resource gap. With these results, the first academic research conducted to answer where organizations are finding their big data resources; businesses’ needing such specialists now have research-backed information available to justify recruiting efforts. Colleges or other educational institutions have a primary source of information on the big data resource gap to make more informed decisions about how to address the demand for these highly sought-after specialists.

10.1 RESEARCH BENEFITS | WHAT’S IN IT FOR ME? If you have been looking for, or will be looking for, big data specialists, you now have the answer to the question of how to find the resources to derive value from the massive amounts of data generated internally and externally that you currently are not able to analyze. Must you look to new places to find external resources? Should you invest in developing resources internally? Should you hire consultants? Where is the best place to invest to get what you are looking for? This research provides clarity on the state of the big data specialist resource availability and the effectiveness of the practices being followed by IT Managers today. New degree programs in universities require investments in resources: Instructors, literature, technology, infrastructure, and university publications just to name a few. Such investments often require justification for a new program. Prior to this research, such justifying data for the need for data scientists by businesses have been provided through anecdotal comments and nonacademic surveys. These research-based results, defining and elaborating on the problem of a lack of qualified resources, are the seminal scholarly supporting data academic institutions have to justify the expense needed to develop data science programs.

10.2

THE STATE OF BIG DATA EDUCATION

The use of the term, “unicorn,” to describe a data scientist, has been done so because of how difficult it currently is to find an individual with the analytical, technical, and business skills the role requires (Bakhshi et  al., 2014, p.  32). Dwoskin (2014) stated that the two websites, LinkedIn™ and SimplyHired.com, listed approximately 30,000 openings for positions with “Data Scientist” in their titles. Though a data scientist does not always require a postgraduate degree, it is quite common that they do. In 2012, only 2500 doctoral degrees were awarded in statistics or computer science (Dwoskin, 2014). Jeonghyun Kim (2016) showed that only twenty-five schools in the United States provide postgraduate classes that were data analytic-specific. Table 10.1 lists the number of schools offering data-centric programs: With over 4726 universities in the United States offering multiple program degrees in a multitude of disciplines, the number of advanced degrees offered in data-centric programs, as listed in Table 10.1, is tiny, but growing.

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TABLE 10.1 2016 Data-Centric Programs by Academic Level Level

10.3

Schools

Bachelor’s Master’s Doctoral

67 299 25

TOTAL

391

DATA SCIENTIST VS DATA ANALYST

The title, “Data Scientist,” is a relatively new title that differs from the traditional data analyst role provided to those who find trends and model results of traditional business intelligence systems. Where data analysts will use structured query language (SQL) to pull information from relational databases, data scientists use SQL as well as the machine language tools to use statistical models to find correlations between different variables (Harris, Shetterley, Alter, & Schnell, 2014). SAS (2015) differentiates data scientists from even statisticians by explaining that data scientists move beyond descriptive statistics and the reporting of past results to predictive modeling of what is likely to happen in the future. Statistician and data analyst roles are traditionally processing type positions. Business leaders ask for supporting statistics or diagrams and ask for the information from these roles. To be a data scientist, one must be front and center in the business goal discussion (Redman, 2013). Chen, Chiang, and Storey (2012) expand on the unique responsibilities of the data scientist by listing knowledge of accounting rules, finance, management practices, marketing approaches, logistic methods, and operations administration inside of the domain the data scientist is working. This results in an individual with strong math and statistic skills, excellent communication abilities, programming and software expertise, and great business awareness. Data scientists do not just help run software against the data, they find and pull in the data sets themselves to find out if correlations exist that can be beneficial to the organization. This process of bringing in data sets requires a data filtering process, or cleansing, to make sure the data are trustworthy. Business skills are as critical as technical skills for data scientists (Debortoli, Müller, & Vom Brocke, 2014). It is because of this as well as the previously mentioned skill set attributed to data scientists that has made finding individuals with all of these skills so difficult and the reason such an individual with such a broad array of skills is referred to as a “unicorn.” When it comes to studies addressing how businesses are meeting their big data needs with such a lack of data scientists, Gupta and George (2016) reveal that there is little known about how organizations are achieving their big data capabilities. In relation to so many other topics with decades, if not centuries, of background history, big data analytics is less than two decades old and still in its infancy as a business practice. By nature, big data is a quantitative discipline, using mathematics and statistics to identify patterns in mountains of data sets of different sizes and

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shapes. The business value of the data scientists and the business practice of finding these big data specialists require a qualitative approach to understand the decisions and actions IT Managers are taking to find them. To date, no scholarly articles have been published around this specific hiring practice, making this the seminal scholarly work to answer what IT Managers are doing to fill their big data resource needs as well as what types of skills are most valuable to IT Managers for these positions. Going back almost a decade, in 2011, the lack of big data specialists was identified as a major hurdle to business’ ability to exploit the benefits big data analytics could bring to them (Manyika, et al., 2011). Five-years later, in 2016, the demand for big data resources continued to grow faster than the supply of data scientists capable of meeting the demand; in the United Kingdom, Kim (2016) reported a continued increase in the market demand for individuals with big data skills starting in 2013 and continuing through 2020. The data also stated that most (77%) of the roles requiring big data skills were considered difficult to fill. Five years later, Manyika et al. (2011) reported on the lack of big data resources available. Henke, Bughin, and Chui (2016) claimed that most businesses are still nowhere near realizing their potential benefits from big data. A critical reason for this is due to data scientists still being in high demand. In a 2018 report by ITPRO, Europe needs 346,000 more data scientists by 2020 (IT Pro Team, 2018).

10.4

A QUALITATIVE APPROACH

The interviews with US-based IT managers involved asking them about their hiring processes and were designed to understand how they approached the challenge of meeting their big data initiatives in lieu of a shortage of qualified big data specialists. We know there are data scientists employed. Shan, Wang, Chen, and Song (2015) conducted interviews with data scientists who work for businesses such as LinkedIn™, Yahoo!™, Uber™, and others. Is there a standard, most successful manner to fill these positions? Has the lack of available qualified big data specialists forced IT Managers to find innovative ways to get the value of big data for their respective businesses? What exactly has been done to fill these roles? The research was limited to the United States. What processes are US-based business leaders taking to meet the big data demands of their businesses? What quality criteria are business leaders in the United States considering in big data specialist candidates? IT Managers are the people in organizations most commonly tasked with filling technology-centric roles. Because of this, they were the best suited, considering their efforts in finding resources to fill those roles. In the event they could not find a person to fill the role, they would be responsible for attempting other methods to meet the big data demands, such as outsourcing to consultants, sending existing employees to be trained, identifying multiple people due to not finding one with all of the skills, etc. While “IT Manager” is the name being used throughout the research, that title may be either a specific or a general term for the person responsible for filling IT-related positions. In some businesses, this may be the Vice President of IT, Director of IT, Information Systems Lead, etc. The choice of this was me asking myself, “Self, who would best know the big data resource hiring process?”

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The number of IT managers hiring big data resources numbers into the thousands. However, due to the homogenous nature of the IT managers, Boddy (2016) made claim that a sample size of 20–30 is standard and is often enough to reach saturation in a case study. Sample subjects were identified through LinkedIn™. A message was posted to the Big Data and Analytics group asking for volunteers or for someone to nominate a manager they know who acts as a big data resource hiring manager. Potential subjects were also reached out directly based on their profile information identifying them as a qualified subject. Once the subjects were identified with their contact information, a letter of consent was sent to them. I used open-ended surveys and semistructured interviews to gather my data. While survey questions gathered information about specific qualities, technologies, and locations of search efforts, the interview question allowed the author to gain insight into the decision-making processes, challenges, and behaviors of the subjects in relation to the phenomenon. These interviews provided me with rich and in-depth information. To understand what types of projects each subject was working on, the technologies they have employed, and the requirements they are looking for in their big data specialists, the following survey questions were developed: Q1: What are your top big data analytic initiatives? Q2: Why did you start your big data initiative? Q3: How large is your big data specialist team? Q4: What big data resources are still needed and why? Q5: Where have you looked to find big data specialists? Q6: What challenges have you had finding big data specialists? Q7: What qualities are most difficult to find in your big data specialist candidates? Q8: Describe your ideal big data specialist. Q9: How many candidates have you interviewed for your big data resource roles? Q10: What are two or three factors that would make the big data resource hiring process easier or more successful? Next, to investigate the “big picture” of what the big data specialist shortage means to the subjects, what processes these IT Managers are taking to meet the big data demands of their business, and what quality criteria are being considering in big data specialist candidates, the following list of semistructured interview questions was created to create a narrative to learn more about this big data resource phenomenon: Q1: Where are you on your big data analytic path? Was the subject in the beginning stages of collecting data? Building out the infrastructure? Designing the data flow architecture? Fully mature and analyzing data? This helped me to understand how far along their data analytics journey they were. Q2: Describe how your big data path has evolved. Are the subjects sticking with their original plan or have new questions or priorities required modifications to their data analytic plans?

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Q3: Describe what your big data specialists you have working for you do on daily basis. No two roles are ever exactly the same. This helped me to understand what their resources are asked to do. Q4: Tell me if you are looking for more of the same types of big data skills or are you looking for new and different skills and why? Are resources needed for more of the same tasks or to do additional tasks? Q5: Describe the challenges you have experienced in finding big data resources. Q6: What qualities in a data scientist are most valuable to your organization and why? Q7: Describe the search process to find big data resources. This was an open-ended question to gather both functional and sentiment data. Q8: How do you determine if someone is qualified or not? I was looking for their decision-making process for determining qualified applicants. Q9: How many people have you interviewed for your big data positions? Q10: What quality tends to be lacking the most in the applicants? While Dicicco-Bloom and Crabtree (2006) argue that semistructured interviews are commonly the sole data source for a qualitative study, I included three different data sources: Surveys and Interviews with IT Managers as well as Interviews with big data specialists. To validate the data collected from the IT managers and understand the responsibilities and expectations of the big data specialists, the following five interview questions were asked of specialists: Q1: Describe what you do in your role. Q2: How did you get the position you currently have? Q3: How did you gain the knowledge needed to be successful in your role? Q4: What skill sets do you feel you need to strengthen to be more effective? Q5: Where do you go to improve your skill set? The two different data types for IT managers differed in that the surveys provided static information about the current state of the subjects’ environments, and the semistructured interviews provide a view of the big picture of the world the subjects’ work. Identifying anticipatory areas in the interview discussion and points of frustration or excitement provide a richness and insight above the information gathered from surveys alone. So that others can evaluate, compare, and synthesize the research, it is important that they understand how I approached the data analysis and what assumptions were made (Braun & Clarke, 2006). The two analysis methods used included thematic and structural. Thematic analysis, according to Vaismoradi, Turunen, and Bondas (2013), looks deeper than the direct answers to the questions about where IT Managers are finding their big data resources. By identifying themes, the researcher learns the

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“why” of the processes taken, which will help readers to comprehend the journey taken to get to the solution IT Managers have decided on. Structural analysis is intended to identify and provide insight into areas of frustration, of excitement, and look for voice fluctuations or nonverbal cues (Herz, Peters, & Truschkat, 2014). The method of analysis followed the five steps proposed by Yin (2008), which include compiling, deassembling, reassembling, interpreting, and concluding. This method, applied to each of the data samples, provides a disciplined approach, allowing for easier reproduction of the research. Along with the researcher’s own manual technique, the use of computer software, NVivo, a qualitative research support software, was used to identify patterns.

10.5

DEPENDABILITY AND TRUSTWORTHINESS

Trustworthiness in qualitative research, according to Graneheim and Lundman (2004), consists of credible, dependable, and transferable findings. Credibility addresses the focus of the research study and pertains to confidence in how appropriate the data and analysis processes were in addressing the study’s purpose (Graneheim & Lundman, 2004; Thomas & Magilvy, 2011). Dependability refers to the stability and consistency of the data and whether changes made in the data collection or analysis of it exist due to some factors or phenomena (Graneheim & Lundman, 2004). Transferability is explained by Thomas and Magilvy (2011) as research findings being able to be carried over to another group or environment. It is argued that it is up to the reader to determine if they believe the results are transferrable or not (Graneheim & Lundman, 2004). Triangulation in the form of comparing the different data and/or analysis methods was one method used to maximize the trustworthiness of the data analysis. Member checking was applied for this purpose as well. Member checking, according to Thomas and Magilvy (2011), requires ensuring with the subjects that the interpretations made by the researcher are accurate representations of the participant’s experiences. Each step in the data collection, coding, and analysis was explained in detail to increase the dependability of the study results. Each technique was communicated to justify the reason the researcher believed it was required and to increase the study’s credibility. This type of reflexivity allows the readers to understand how and why the researcher interpreted the data and see the “big picture” of the study (Thomas & Magilvy, 2011, p. 154).

10.6 DATA ANALYSIS Thematic data analysis is a flexible research tool, which provides a rich, complex, and detailed account of the data (Vaismoradi, Turunen,  & Bondas, 2013). It is well-understood in the qualitative research realm as it is one of the most common qualitative analysis methods. Braun and Clarke (2006) describe thematic analysis as an approach to recognize, analyze, and report patterns identified within the data. These identified patterns/themes provide insight of the phenomenon being studied. Thematic analysis pulls out the patterns to generate the theory (Braun  & Clarke,

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2006). This type of analysis is intended to provide a transparent view of the data and what the data represent in terms of the real world of IT hiring managers trying to fill data scientist/ big data specialist roles. Structural analysis provides a sequential methodology which is particularly applicable to interviews (Herz, Peters,  & Truschkat, 2014). Similarities exist between structural analysis and thematic analysis in that they both employ condensing data, coding, and analyzing them. Structural analysis aims to analyze both the inside perspective and the external attributes those perspectives reveal. This approach is chosen to reveal structured/sequential insight into the data, which can be applied against the patterns identified in the thematic analysis approach. A key point of structural analysis is theoretical sensitivity. Theoretical sensitivity involves the researcher’s capability to distinguish pertinent from nonpertinent data to develop a theory that is pragmatic and well-integrated. This is a disciplined approach to help create theories from data. Where thematic analysis can identify several patterns in the data, structured analysis aims to find sequences, or structured processes, in the data (Herz et al., 2014). Data saturation assures that the themes throughout the study are well-supported, and no new information exists over each topic that would change the researcher’s understanding of the concept. While the survey questions were designed to collect information about the big data resource achievement steps, the interview questions for IT Managers were designed to gather additional details to understand similarities and differences between the same or similar survey responses and confirm if saturation was, indeed, reached.

10.7 BIG DATA INITIATIVES A key area to understand about the big data landscape is what are businesses doing in the big data space. As explained earlier, predictive analytics can provide a new level of benefit to individuals and organizations. While still in its early stages, organizations are still building their big data environments and working on achieving new insights from the new types of data they are collecting. With 63 big data initiatives captured in the survey from 20 research subjects, Table 10.2 lists these initiatives in order of number of responses.

TABLE 10.2 Big Data Initiatives Initiatives Predictive analytics Data management Application development Infrastructure building Security

Responses 26 19 8 6 4

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10.8 YEARS OF BIG DATA INITIATIVES To learn how long the subjects’ organizations have been working on big data initiatives, it was asked of them to share the year they began, in whatever capacity, their path down the road of big data analytics. Table  10.3 shows the time frame of the beginning of the big data initiatives from the twenty research subjects by listing the years that the big data efforts, most current first, as well as the number of responses for those years.

10.9

SIZE OF BIG DATA TEAMS

To understand how large the big data specialist teams are in the organizations being represented in the research, Table 10.4 reflects the size of the big data teams at the subjects’ organizations.

10.10 BIG DATA RESOURCES NEEDED The term “Data Scientist” is ambiguous in that it may encompass someone who deals with big data in a limited or larger number of ways. A data scientist can be someone who knows and can perform one or more of the following: collect and manage data, build the infrastructure to hold the data, understand and apply math and statistics, develop algorithm and code, analyze results, and communicate with business leaders to gather priorities and report findings. Some organizations have different titles for each of those individual roles, such as developers, data miners, engineers, architects, evangelists, etc. Table 10.5 illustrates the resources that the organizations in the research subjects represent are still need to be found. “Data Scientist” was specifically mentioned, as TABLE 10.3 Time-frame Respondents Started Their Big Data Initiative Year Started

Responses

2017–2018 2015–2016 2013–2014 2012 or longer

4 3 9 4

TABLE 10.4 Size of Respondents’ Big Data Teams Size of Team 3 People 4 People 5 or more

Responses 1 3 16

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were roles that the subjects pointed out that were particularly needed. Integrators, data architects, and engineers were categorized under the heading, “technologists.” Needed roles that were specified in the data with the terms “Chief,” “Lead,” or “Head” were counted under the category titled, “Leadership.” The distinction between “Technologists” and “Developers” may not be clear. While the technologist’s roles are to make the hardware and software function, developers create the code that addresses the data sets to find value. Technologists are often considered part of the infrastructure or operations group, and developers write code that will run on that infrastructure.

10.11

WHERE ARE ORGANIZATIONS FINDING BIG DATA RESOURCES?

The key question of the research is “Where are organizations finding their big data resources?” The 20 research subjects provided a total of 57 answers of the places they turned to find big data resources. Table 10.6 breaks down these places based on the frequency, which they were answered:

TABLE 10.5 Big Data Resources Still Needed Resource Type

Number Needed

Data scientists Technologists Leadership Developers No resources needed

9 6 3 2 3

TABLE 10.6 Where Respondents Found Big Data Resources Location Found Internal Referral Social media sites Job posting responses External job sites Placement agencies Conferences Big data events Consultants Hackathons Periodicals College recruits

Number of Responses 12 9 8 7 6 4 4 2 2 1 1 1

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The 57 answers summarized into twelve categories for Table  10.6 came from four separate questions, asking the subjects to rank their answers in terms of most effective and list the most effective first, followed by the second most effective, the third, then the fourth. Nine of the “most effective” responses from each subject were “Internal” and five were “Referral”; the two most successful areas where the subjects’ organizations found their big data resources.

10.12 CHALLENGES FINDING BIG DATA RESOURCES The challenges in finding big data resources listed by the 20 research subjects fell into five different areas. Do the candidates have the skills or experience? If so, can the organization afford them? Are they looking in the right places? Table 10.7 lists each of those areas along with the number of responses:

10.13 QUALITIES MOST DIFFICULT TO FIND IN CANDIDATES Identifying the qualities that are most difficult for organizations to find in their big data specialist candidates fell into four different categories. A category titled “Personality Qualities” included those soft skills with comments that included the ability to communicate effectively, work well in a team environment, drive, tenacity, and an ability to understand the big picture of what the use of data science is all about in the context of the business. Table 10.8 presents the qualities IT managers are seeking in the order in which they were most identified by the survey subjects:

TABLE 10.7 Challenges Finding Big Data Resources Challenges Candidates’ Lack of Skills/Experience Not Enough Candidates Cost Location Poor Recruiting

Number of Responses 12 6 2 1 1

TABLE 10.8 Qualities Most Difficult to Find in Big Data Candidates Qualities Most Difficult to Find Personality Qualities Experience Business Knowledge Technology Expertise

Number of Responses 9 6 6 5

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10.14

181

THE IDEAL BIG DATA SPECIALIST CANDIDATE

Research subjects were asked to provide a description of their ideal big data specialists candidate. Most of the answers included multiple characteristics. As described above, there were personality qualities several of the respondents mentioned, including ability to teach others, desire to learn new things, emotionally intelligent, ability to tell a story, can deduce, or has leadership skills. These have been grouped into a category titled “Personality Qualities.” Table 10.9 lists the ideal attributes and the number of responses to each:

10.15 NUMBER OF CANDIDATES INTERVIEWED The number of candidates interviewed by the businesses represented in the research number into the thousands. Not every company though has a large number of big data specialists/data scientists. Table 10.10 lists the number of big data candidates interviewed listed by range of interviewees followed by the responses from the research subjects:

10.16 EASING THE BIG DATA HIRING PROCESS The final question of the survey sought to learn what could be done to make the big data resource hiring process easier or more successful. Six categories were identified out of 40 responses. Table 10.11 lists those categories, sorted by the number of responses that mention them. “Better Sources of Candidates” covered such areas as improved networking, more qualified resumes, prevalidated candidates, improved use of internal candidates, etc. This was the largest area identified by the research subjects to improve the hiring of big data candidates. TABLE 10.9 The Ideal Big Data Candidate’s Qualifications Ideal Characteristics Personality Qualities Mix of Business and Technical Skills Big Data Knowledge and Experience

Number of Responses 8 7 4

TABLE 10.10 Number of Candidates Interviewed Range of Candidates Interviewed 1–19 20–50 100+

Number of Responses 8 3 6

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TABLE 10.11 Areas to Improve the Big Data Hiring Process Area of Improvement Better Sources of Candidates Proof of Experience Skills Identification/Testing Adaptability Better Education Availability of Qualified Resources

Number of Responses 11 7 7 7 5 3

The term “Adaptability” covered an assortment of areas ranging from allowing employees to work remotely, greater flexibility on behalf of the hiring company with compensation, or less limitations due to non-US Citizen candidates. Seven of the subjects mentioned that regardless if it was the candidates themselves or the organization that was looking to hire someone, being more adaptable in these areas would make the hiring process easier.

10.17 IT MANAGER INTERVIEWS Eight interviews took place with subjects responsible for, or part of, the hiring of big data resources. The intent was to gather depth around the big data hiring process, identify themes in the recorded conversations, compare the data collected to the survey data, and to understand what the experience is in this phenomena through the words of those living it. The interview questions were similar to the survey questions but were worded to draw out greater detail. This allowed the researcher to compare the answers, as a form of triangulation, and capture more detail around those questions. An example of this was where the survey question asked what challenges the subjects had finding big data specialists. While a survey response may have been, “gap on skill sets,” the interview question asked the subjects to describe their challenges, which resulted in a longer explanation and examples of candidates who had limited training or experience with technologies or interpersonal skills important to the organization that was hiring. These examples added to a greater understanding of the challenge to find big data specialists, which the survey questions were valuable for identifying themes but lacked in clarity of understanding what was specifically meant by “skill sets.”

10.18 SPECIALIST INTERVIEWS Five big data specialists were interviewed as well. This third source of data added to the triangulation by verifying the data collected of the hirers of big data specialists by those who were the seekers of big data specialist positions. New structures and themes came out of these interviews, particularly around the attainment and

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development of skills to be successful. The answers to what each of the big data specialists did were specific to the business and industry they worked in, but similar in nature, with some having a wider breadth of responsibility and others being very focused on a couple of roles. Each verified that they came to the businesses they are currently working for with a limited amount of knowledge in either their big data technical skills or the industry the business was in. All five grew their skill sets while on the job and received access to the software they wanted or needed to learn more about from their current employers.

10.19 KEY ANALYSIS & FINDINGS The purpose of this study was to learn what IT Managers are doing to meet the big data demands of their business as well as what quality criteria they are considering in big data specialist candidates. With ambiguity of the term, “big data,” and a growing number of technologies, methods, and approaches to big data analytics, trying to understand exactly what businesses are looking for and how they are meeting their big data demands was not understood from the previous literature. The findings also explain what hiring managers are looking for, what types of big data initiatives they are engaged in, what are the qualities difficult to find in big data specialists, and what they still need.

10.19.1 THEME 1: “LACKING” “Lacking,” “Dearth,” “Shortage,” “Scarcity”: The key finding from this study is that there are deficiencies in nearly every component of big data resources. Through thematic analysis, the research identified, in general, candidates either lack experience with the big data technologies or they lack business acumen. There is a lack of industry familiarity, and a lack of interpersonal, communication, or social skills often referred to as emotional intelligence. Advanced math and statistic skills are hard to find, but to find that data scientist looking for a job who can provide the strong math, coding, strong communication skills, and emotional intelligence that someone with that title is expected to have is nearly impossible. Table  10.7 highlighted the shortage of candidates with the skills or experience businesses are looking for as well as just a lack of candidates overall. A lack of effective recruiting was another that added to 19 of the 22 responses around the challenge of finding qualified candidates to fill the big data roles they have open as due to something lacking. As listed back in Table 10.1, there is a lack of educational institutions providing specific big data or data analytic degrees. There are limited certification programs and insufficient skill validation avenues.

10.19.2 THEME 2: “PASSION” If there was ever a trend that espoused the phrase, “Where there is a will, there is a way,” big data and the people involved in it definitely have the will. Though many are lacking in either skill or experience, big data specialists are passionate about learning and improving. Identified through structural analysis, the emotions and level of

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excitement around solutions to the big data talent gap were evident across the board. More data were volunteered in this area than any other topic area. Big data specialists are tasked with providing answers and solving problems, so it is no surprise that when discussing a solution to the data scientists’ shortage, they were engaged and ready to provide their opinions. Passion is also a key quality that hiring managers are looking for. One subject with a job opening listed “highly motivated” as the very first quality he is looking for in a candidate with “deep interest,” and “obsession” listed high on the candidate qualification list.

10.19.3 THEME 3: SOFT SKILLS While “big data” is defined by the data and the technologies surrounding it, knowing how to work with big data; managing, cleansing, developing statistical algorithms to run against the data, and analyzing the findings are only parts of what qualifies a data scientist for his or her role. When asked what qualities are most difficult to find and most ideal in big data specialist candidates, the answers that were provided included: story tellers, people who can speak in business terms, good communication skill, emotional intelligence, teaching skills, and leadership skills.

10.19.4

THEME 4: TECHNICAL SKILLS

The list of hardware, software, and services being added to the market around big data analytics does not look to be slowing down anytime soon. During the data collection process, many technologies were mentioned as valuable to the subjects’ big data initiatives. Candidates with such technology experience and expertise are in high demand: Java programming, Hadoop, machine learning, Scala, DevOps concepts, cloud computing, Python, R, Hive, Hbase, and Spark.

10.20

CONCLUSION

We are in the infancy of this fast-growing trend. The promise of such great insights and life-changing breakthroughs has organizations moving forward before a pool of trained and experienced experts are able to fill the desired roles. Education is by far the top opportunity but not just for technical information. Communication, statistics, coding, technology infrastructure, software, industry knowledge, business acumen, and critical thought are individual and collective areas for growth that educational institutions can focus on to develop more qualified big data candidates to the work force. AI is another area of opportunity for businesses to consider. If someone with coding, communicating, and statistic expertise is not able to be found, perhaps computers can be programmed to do the coding and statistics, leaving the communication of the results left to those who can verify and validate them. Hundreds of businesses have started to provide software to the big data analytic world to provide answers faster and easier than manual activities. Big data has forced a change in the traditional separation between technologyfocused specialists and business leaders. The untraditional makeup of the data, from

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which businesses are trying to find value, requires someone who has a strong understanding of the business, its industry, and the type of information that can provide new benefits. Having that business-level understanding then requires the technical knowhow to process the data and glean the value from it. Understanding, collecting, and processing that data then require the ability to communicate it to business leaders so better qualified decisions can be made and actions can be taken. This study reveals the primary answer given around filling big data positions has been to find and train internal resources to become data scientists. With what is in demand, this makes sense: They know the business since they work for it, they have a familiarity with their employees, and many already have a proven maturity in a couple of the big data disciplines, whether that is coding, mathematics, or communication skills. The second most common response to the question of where are IT Managers finding their big data resources is referrals. If internal resources fall under the umbrella of people known to the business, referrals are people trusted by people known to the business. In the beginnings of a new revolutionary change in how businesses make decisions, it is not a far-fetched concept that those who will be easiest to find, most trusted, and valuable will be those best known to the business’ leadership. Is the data scientist someone who has a business acumen and picks up the technology skills allowing him or her to find business insights, or is the data scientists someone who has a technical focus and then learns the business? This question will be pondered by those who wish to be a part of developing future data scientists. Do universities make big data a part of their business programs, their information systems programs, or math and statistics programs? Perhaps, this is a new program all together. Regardless of where the big data specialists are developed, the communication, critical thinking, and emotional intelligence skills must be central to the development of these future data scientists.

10.21 DISCUSSION Big data continues to grow in popularity and adoption. While this study answers questions about the recruitment practice of big data resources, there are a number of areas that this study did not address, which includes sources of big data training. There are new university programs being introduced and training over big data topics being offered by technology vendors. Are they hitting the mark? Are they producing qualified big data specialists capable of stepping into data science roles and being effective? That is an area worthy of additional study to help improve the quality of education. Technology will continue to change, and new technologies will come into play. Technologies mentioned in this study may continue for years to come or they may be replaced by new ones. Additional research on effective tools used in the big data analytics space will provide insight into areas of focus for the next generation of big data specialists.

REFERENCES Bakhshi, H., Mateos-Garcia, J., & Whitby, A. 2014. Model workers: How leading companies are recruiting and managing their data talent. http://www.nesta.org.uk/sites/default/files/ model_workers_web_2.pdf.

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Boddy, C. R. 2016. Sample size for qualitative research. Qualitative Market Research: An International Journal, 19(4), 426–432. http://dx.doi.org/10.1108/QMR-06-2016-0053. Braun, V., & Clarke, V. 2006. Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. http://dx.doi.org/10.1191/1478088706qp063oa. Chen, H., Chiang, R. H., & Storey, V. C. 2012. Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188. Columbus, L. 2014. 84% Of Enterprises See Big Data Analytics Changing Their Industries’ Competitive Landscapes in the Next Year, Forbes. Debortoli, S., Müller, O., & Vom Brocke, J. 2014. Comparing business intelligence and big data skills. Business & Information Systems Engineering, 6(5), 289–300. http://dx.doi. org/10.1080/12460125.2016.1171610 DiCicco-Bloom, B.,  & Crabtree, B. F. 2006. The qualitative research interview. Medical Education, 40(4), 314–321. https://doi.org/10.1111/j.1365–2929.2006.02418.x Dwoskin, E. 2014. Big-data scientists are in big demand. Wall Street Journal. http://search. proquest.com/docview/1552324544?accountid=458 Graneheim, U. H.,  & Lundman, B. 2004. Qualitative content analysis in nursing research: Concepts, procedures and measures to achieve trustworthiness. Nurse Education Today, 24(2), 105–112. http://dx.doi.org/10.1016/j.nedt.2003.10.001 Gupta, M.,  & George, J. F. 2016. Toward the development of a big data analytics capability. Information & Management, 53(8), 1049–1064. http://dx.doi.org/10.1016/j.im.2016. 07.004 Harris, J. G., Shetterley, N., Alter, A. E.,  & Schnell, K. 2014. The team solution to the data scientist shortage. https://acnprod.accenture.com/us-en/_acnmedia/Accenture/ Conversion-Assets/ DotCom/ Documents/Global/ PDF/ Dualpub_22/Accenture-TeamSolution-Data-Scientist-Shortage#zoom=50 Henke, N., Bughin, J., & Chui, M. 2016. Most industries are nowhere close to realizing the potential of analytics. https:// hbr.org/2016/12/most-industries-are-nowhere-close-torealizing-the-potential-of-analytics Herz, A., Peters, L.,  & Truschkat, I. 2014. How to do qualitative structural analysis: The qualitative interpretation of network maps and narrative interviews. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 16(1). http://www.qualitativeresearch.net/index.php/fqs/article/view/2092/3904. IT Pro Team. 2018. Data scientist jobs: Where does the big data talent gap lie? https://www. itpro.co.uk/careers/28929/data-scientist-jobs-where-does-the-big-data-talent-gap-lie. Kim, J. 2016. Who is teaching data: Meeting the demand for data professionals. Journal of Education for Library and Information Science, 57(2), 161–173. http://dx.doi. org/10.12783/issn.2328-2967/57/2/8. LinkedIn. 2018. LinkedIn Workforce Report, United States. https://economicgraph.linkedin. com/resources/ linkedin-workforce-report-august–2018. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. 2011. Big data: The next frontier for innovation, competition, and productivity. Retrieved September 16, 2015, from http://www.mckinsey.com/insights/ business_technology/ big_data_the_next_frontier_for_innovation. Redman, T. C. 2013. What separates a good data scientist from a great one. https:// hbr. org/2013/01/the-great-data-scientist-in-fo. Rivett, C. 2017. PwC report forecasts 2.7m new data science and analytic jobs will be created by 2020. https://www.gigabitmagazine.com/ big-data/pwc-report-forecasts-27m-newdata-science-and-analytic-jobs-will-be-created-2020. SAP and the German football association turn big data into smart decisions to improve player performance at the world cup in Brazil. 2014. http://www.news-sap.com/sap-dfb-turn -big-data-smart-data-world-cup-brazil/.

Closing The Big Data Talent Gap

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SAS. 2015. Predictive analytics: What it is and why it matters. http://www.sas.com/en_us/ insights/analytics/predictive-analytics.html Thomas, E., & Magilvy, J. K. 2011. Qualitative predicting data saturation in qualitative surveys with mathematical models from ecological research.Rigor or Research Validity in Qualitative Research. Journal For Specialists In Pediatric Nursing, 16(2), 151–155. http://dx.doi.org/10.1111/j.1744-6155.2011.00283.x. Vaismoradi, M., Turunen, H., & Bondas, T. (2013). Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study. Nursing & Health Sciences, 15(3), 398–405. http://dx.doi.org/10.1111/nhs.12048. Yin, R. K. (2008). Case Study Research: Design and Methods (4th ed.). Thousand Oaks, CA: SAGE Publications, Inc.

Index Note: Bold page numbers refer to tables; italic page numbers refer to figures. AI see artificial intelligence (AI) Allen, H. 80 Arsad, R. 82 artificial intelligence (AI) 11, 24, 152, 153–154, 169 response to AI disasters 157 for risk and disaster management 156–158 assisted cognition systems 57 attributes impacting consumer’s purchasing behavior 113–123 Augmented Intelligence (AI) 169 Aydinliyim, T. 116 BA see Business Analysis (BA) Balezentis, A. 86 Banker-Charnes-Cooper models 80, 83 barriers to BDA 31–33, 33, 36 in LSCM 34–35, 39 and challenges of BDA 31 main barriers vs. sub-barriers 40 political 41 weights and ranks of the main and sub-barriers 40 Bartels, J. 114 Barthelemy, P. 135 Batran, A. 48 BDA see Big Data Analytics (BDA) BDBA see Big Data Business Analytics (BDBA) behaviour, consumer 112, 113 Behe, B. K. 110 Bell, H. A. 123 best-worst method (BWM) 38 consistency rate (CR) 38, 39 bidirectional supply chain framework (BSCF) 123, 123–126 Bienhaus, F. 48, 49 Bigaret, S. 85 Big Data (BD) barriers of 33 candidate’s qualifications 181 definition of 10 hiring process 181, 182 importance of 32 resources needed 178–179 Big Data Analytics (BDA) 30–31, 53, 54 applications of 11, 30 barriers and challenges of 31 barriers to 31–33, 33 BDA in LSCM 34–35

BDA usage and acceptance 41, 41 hierarchical structure of 36 effective and efficient use of 30 importance of 32 models 10 process specific applications of 12–15, 13, 13 purpose of 30, 31 supply chain challenges in adopting 22–24 organisational challenges 23 technical challenges 23–24 Big Data Business Analytics (BDBA) 30, 31, 49 big data education, state of 171, 172 big data initiatives 177, 177, 178 years of 178 big data process 10, 10 big data resources challenges finding 180, 180 organizations finding 179, 179–180 big data teams, size of 178 Bindroo, V. B. 120 blockchain 125 solutions 57–58, 60 technologies 24 Boddy, C. R. 174 Bondas, T. 175 Borden, R. J. 116 Brauers, W. K. M. 85, 86 Braun, V. 176 Brennan, M. J. 81 Bronson, C. S. 119 BSCF see bidirectional supply chain framework (BSCF) Bughin, J. 173 Business Analysis (BA) 30 business banks 80 business performance 79 evaluation 97 business-to-business (B2B) relationship 110 business-to-consumer (B2C) relationship 110 buying factors 114–115 BWM see best-worst method (BWM) Cabanda, E. 82 Celebi, S. I. 121 CF see collaborative filtering (CF) Chahal, V. 117 Chang, T. Z. 115, 118 Chaotic Storage mechanism 14 Charnes-Cooper-Rhodes models 80, 83

189

190 Chen, C. 82, 115 Chen, H. 172 Chen, W. 173 Chiang, R. H. 172 Chocolate 135 Choi, G. 114 Choi, T. 122 Chui, M. 173 CI see consistency index (CI) Circular Economy 127 Clarke, V. 176 classic inventory models 134 cloud-based network 11 CNC see computer numerical controls (CNC) cognition systems, assisted 57 cognitive technologies 11 collaborative filtering (CF) 66, 68, 72 computer numerical controls (CNC) 57 consistency index (CI) 38 consistency rate (CR) 38 in best-worst method (BWM) 38, 39 determination 38–39, 39 consumer behaviour 112, 113 consumer’s purchasing behavior attributes impacting 113–123 derived utility 115–118 product quality 118–120 product support services 120–121 purchase price 113–115 return policy 121–122 consumers’ purchasing decisions 115 content-based (CB) filtering product 68 content-based (CB) infers 66 content-based (CB) system 72 conventional model 134 conventional supply chain models 124 Cooper, W. 83 CR see consistency rate (CR) Crabtree, B. F. 175 Critical Success Factors (CSFs) 47, 59, 60 for procurement 4.0 49–50, 56 procurement processes and 55, 56 CS alogithm see Cuckoo Search (CS) alogithm CSFs see Critical Success Factors (CSFs) Cuckoo behavior and Levy flights 134–135 Cuckoo Search (CS) alogrithm 143–145 customer of products, recommendation system for ratings and reviews of 75, 75, 76 customer value-based pricing 116 cyber aware 12 cybernetics 51, 56 Dailey, L. C. 122 data quality of 23 scalability of 23

Index data analysis, thematic 176 data analyst vs. data scientist 172–173 data collection 2, 3 Data Envelopment Analysis (DEA) 80 correlations between inputs and Os in DEA analysis 102 empirical results best and worst ranked companies 93, 95 data description and preprocessing 86, 87–90, 90–91 main DEA results 91–93, 92, 93, 94 robustness checking – MCDM 95–96 models in financial analysis 82 possible integrations of 96 previous related research 81–83 data-related barriers 34, 39 data saturation 177 data scientist 178 vs. data analyst 172–173 data sets, one-sided and two-sided T-test of 71, 71, 72 DEA see Data Envelopment Analysis (DEA) Decision-Making Units (DMUs) 83, 85, 93, 94, 97 decision trees, perspective analytic method 20, 21 defuzzification 22 Degeratu, A. M. 115 DEMATEL-ANFIS hybrid approach 32 Demerjian, P. R. 82 Dempsey, M. 81 dependability 176 and trustworthiness 176 descriptive analytics 17, 17 functions 17–18 DHL 53 Dicicco-Bloom, B. 175 digital transformation 47 digital twin 125 digitization 60 Ding, D. 82 disaster management, Internet of Things (IoT) for 164 disasters IOT relationship in the supply chain during 158–159 natural 152, 157 DMUs see Decision-Making Units (DMUs) Dornfeld, D. 117 Double Flexible Model 135 Doyle, J. 83 Drop Shipping Management Tool (DSM) 16 Dwoskin, E. 171 dynamic changes, in stock markets 80 earnings per share (EPS) 81, 95 e-commerce company 65–66

Index Edirisinghe, N. C. P. 82 Efficient Market Hypothesis 81 Emmons, H. 122 Emrouznejad, A. 82 enterprise resource planning (ERP) 57 EOQ model 135 e-procurement 54 approach 52–53 EPS see earnings per share (EPS) Erickson, G. M. 115 ERP see enterprise resource planning (ERP) expertise and human-related barriers 34 Fang-Ming, L. 82 filtering technique 66 financial ratios 87–90 “5V” concept 1 Florida, R. 117 Francis, J. L. 116 Frederico, G. F. 48 Frye, M. A. 135 full-time equivalents (FTEs) 58, 59 Fuzzy Rule-Based Systems 20–22, 22 benefits of 22 Gardijan, M. 82 Geissbauer, R. 48 generalized method of moments (GMM) 82 George, J. F. 172 Gilberts, S. M. 122 Ginevicius, R. 86 GMM see generalized method of moments (GMM) Graneheim, U. H. 176 Granger, C. J. 80 Green, R. 83 Gregoriou, G. N. 81 Grey Systems Decision (GSD) 82 Gupta, M. 172 Haddud, A. 48, 49 Handfield, R. B. 52 Harris, J. G. 135 HBWM see hierarchical best-worst method (HBWM) Henke, M. 48 Henke, N. 173 hierarchical best-worst method (HBWM) 31, 33, 35, 39, 41, 42 programming model of 37 steps of 35–37, 36 Ho, S. 80 Hsuan, J. 115 Huang, C. 82 Huang, Y. Y. 52 Hurson, C. H. 85

191 IA see integrated analytics (IA) ICT see Information and Communication Technology (ICT) ideal big data specialist candidate 181 industry 4.0 46, 47, 52–54 industry and procurement, generations of 50 industry n.0 50 Informatica tool 16 Information and Communication Technology (ICT) 52 integrated analytics (IA) 58 intelligence artificial intelligence (AI) see artificial intelligence (AI) augmented Intelligence (AI) 169 Intelligent Supply Chain 159 Internet of Things (IoT) 57, 125, 152, 154–156 devices 155 for disaster management 164 future trends 163–164 importance of 153 narrowband specification (NB-IoT) 163 relationship in supply chain during disaster 158–159 sensors 155 use of 156–158 interviews IT manager 182 specialist 182–183 inventory models classic 134 with two warehouses 134 IoT see Internet of Things (IoT) IT managers interviews 182 key analysis & findings 183 lacking theme 183 passion theme 183–184 soft skills theme 184 technical skills theme 184 Jarvenpaa, S. L. 121 Jayakrishna, K. 49 Johansson, J. K. 115 Kang, K. H. 116 Kim, B. 123 Kim, J. 171, 173 Klitz, K. 135 Kothari, S. P. 81 Kuosmanen, T. 85, 90 Lee, C. 81 Lee, W. H. 81 Levy-flight research model 135 Lim, S. 82

192 linear regression model 72, 73 LINGO software 16, 39 Lin, J. Y. 120 LinkedInTM 174 LinkedIn Workforce Report 170 Lintner, J. 81 Lin, W.-C. 82 liquidity indicators 81 Litzenberger, R. 81 Liu, L. Q. 115 Livingstone, S. 118 Logistics and Supply Chain Management (LSCM) 31, 39 barriers to BDA in 34–35, 39 Lopez, F. J. 85 LPWAN technology 163 LSCM see Logistics and Supply Chain Management (LSCM) Lundman, B. 176 Macdonald, E. K. 118 macroenvironment 47 Magilvy, J. K. 176 Manavalan, E. 49 Manyika, J. 173 Map Reduce tool 16 Markov chain 144 material costs 49 Matthias, O. 80 MCDM see multiple criteria decision-making (MCDM) McMullen, P. 82 MongoDB tool 16 MOORA (multiobjective optimization by ratio analysis) model 85 Morris, R. T. 119 multiple criteria decision-making (MCDM) 85–86 approach 41 model 80, 95–96 possible integrations of 96 Nagle, T. T. 115 NASDAQ Computer Components Index 86 natural disasters 152, 157 Nzila, C. 118 Oberholzer, M. 82 one-sided T-test of data sets 71, 71, 72 Onwezen, M. C. 115 Operations Research (OR) 80 organizational barriers 35 Owusu, A. 115, 118 Pandey, T. 135 Pangburn, M. S. 116

Index Parsa, H. G. 116 Pasternack, B. A. 122 Pavlyukevich, I. 135 Payne, R. B. 135 PCA see principal component analysis (PCA) Pentaho tool 16 Peralta, M. E. 127 Perona, M. 50 personality qualities 180 PERT vs. CPM 21 Poelman, A. 115 political barriers 35, 41 Powers, J. 82 predictive analytics 18, 18–19 prescriptive analytics 15–16, 19–22, 20, 21, 21 PricewaterhouseCoopers (PwC) 170 pricing, customer value-based 116 principal component analysis (PCA) 72, 73, 74 Procurement 1.0 46 Procurement 2.0 46 Procurement 3.0 47 Procurement 4.0 46–50 adoption of 60 application of the model 58–59 critical success factors for 51 cognition 53–54 collaboration 52–53 communication 52 confidence 55 connection 53 controllership 52 coordination 54–55 cybernetics 51 development of 49 management of 51 practical implications 59–60 scope of 59 supporting solutions 55–58, 56 procurement costs 49 procurement cycle, critical success factors and 55, 56 procurement n.0 50 product quality 118–120 product recommendation system 68, 68–69, 69, 70 attributes of the test data set 69, 69 flowchart of 66, 67 keyword classification 69 user’s preferences/choices 69 products implementation of statistical analysis for 69, 70, 71 experimental assessment 72, 74 linear regression model 72, 73 one-sided and two-sided T-test of data sets 71, 71, 72 product support services 120–121

Index proposed system, contribution of 66 proposed work, literature survey 66, 68 purchase price 113–115 purchasing behavior attributes impacting consumer 113–123 derived utility 115–118 product quality 118–120 product support services 120–121 purchase price 113–115 return policy 121–122 purchasing decisions, consumer 116 qualitative approach 173–176 qualitative research 50 qualities most difficult to find in candidates 180, 180 personality 180 quality-based approach 115 quality of data 23 Ramaswamy, K. 81 recommendation system advantages of 75–76 effects of 72, 74, 74–75 for ratings and reviews of customer of products 75, 75, 76 traditional 66 regression model, linear 72, 73 relative financial strength indicator (RFSI) 82 research benefits 171 retailers 15 return on assets (ROA) 81 return on invested capital (ROIC) 81 return on investment (ROI) 95 issues on 23 profitable 24 return policy 121–122 Reynolds, A. M. 135 RFSI see relative financial strength indicator (RFSI) Riesz, P. C. 119 ROA see return on assets (ROA) Robotic Process Automation (RPA) 56, 58 Roe, B. 116 ROI see return on investment (ROI) ROIC see return on invested capital (ROIC) RPA see Robotic Process Automation (RPA) R programming tool 16 Sachs, J. D. 110 Sarkis, J. 117 SBM model see slacks-based measure (SBM) model scalability of data 23 Scholz, M. 117 Schulte, A.T. 48

193 scientometric analysis co-author analysis 4, 5 co-citation 5, 6, 7 countries and affiliations 3, 4, 4, 5 keywords 2, 3, 4 on sources 4–5, 6 Selwyn, N. 121 Several Delivery Organisations 15 Sexton, T. R. 83 Shan, C. 173 Shanken, J. 81 Sharp, B. M. 118 Shlesinger, M. F. 135 Sickles, R. C. 82, 83 Siew, L.W. 82 Skrinjaric, T. 82 slacks-based measure (SBM) model 80, 84, 91, 96 Smith, G. E. 115 socio-environmental disaster, in BrumadinhoMG 160 soft computing techniques application assumption and notations 136 Cuckoo behavior and Levy flights 134–135 Cuckoo Search (CS) alogrithm 143–145 inventory models with two warehouses 134 mathematical formulation of model and analysis 137–143 numerical analysis 145–146, 146 sensitivity analysis 146–147 Song, M. 173 Sorenson, M. D. 135 Sparks, L. 118 specialist interviews 182–183 SQL see structured query language (SQL) Staples, D. S. 121 stock markets, dynamic changes in 80 stocks 99–101 Storey, V. C. 172 STP see Straight Through Processing (STP) Straight Through Processing (STP) 52 structured data 9 structured query language (SQL) 172 supply chain 10–11 activities of 11 complex intricacy of 23 performs 12 for process specific application 12–15, 13, 13 manufacturing 14 plan 13–14 point of sale 15 source 14 transportation 15 warehousing 14 supply chain analytics (SCA) 49 features of 11–12 future of 24 methods 15–16

194 supply chain analytics (SCA) (Cont.) descriptive analytics 17, 17–18 predictive analytics 18, 18–19 prescriptive analytics 19–22, 20, 21, 21 necessity for 11 opportunities for 12 technology 11, 25 tools for 15, 16 supply chain challenges in adopting big data analytics 22–24 organisational challenges 23 technical challenges 23–24 supply chain framework, bidirectional 123, 123–125 supply chain management, hierarchical structure of barriers to BDA in 36 supply chain models, conventional 124 Supply Chain Network 23 Supply Chain Operations Reference (SCOR) model 124 supply chain product transparency 119–120 sustainability 110 triple bottom line (TBL) 124 sustainability-driven innovators 111 sustainable development 110 sustainable product 113 Sutton, R. J. 119 Swami, A. 135 Tabatabaei, M. H. 31 Tan, K. H. 120 Taylor, M. 80 TBL see triple bottom line (TBL) technological barriers 34 thematic data analysis 176 theoretical sensitivity 177 Thomas, E. 176 Thorgesen, J. 119 three V’s 10 Tobler, C. 119 TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) 82 traditional recommendation system 66 transferability 176 triple bottom line (TBL) 110 sustainability 124 trustworthiness, dependability and 176 Tsen, C. H. 116

Index Tseng, C. H. 116 Tse, Y. K. 120 T-test of data sets, one-sided and two-sided 71, 71, 72 Turunen, H. 175 two-sided T-test of data sets 71, 71, 72 two-storey inventory model 147 Ülkü, M. A. 115, 122 United Parcel Service (UPS) 2 unstructured data 9 user-based systems 68 Vaismoradi, M. 175 “value-action” gap 113 “value chain” management approach 125 variety 10 velocity 10 volume 10 VOSviewer 2, 7 Wackernagel, M. 118 Wallis, K. F. 80 Wal-Mart 2 Wang-Ching, C. 82 Wang, G. 49 Wang, H. 173 Wildt, A. R. 115, 118 Winsor, R. D. 117 Wright, P. 117 Wu, L. S. 81 Xidonas, P. 86 Yadav, A. S. 135, 136 Yang, Z. 124 Yao, D. Q. 122 Young, W. 121 Zaichkowsky, J. L. 111 Zamani, L. 82 Zavadskas, E. K. 85 Zelenyuk, V. 83 Zhang, X. 82 Zhu, Q. 117 Zielke, S. 116 Zopounidis, C. 85