Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures: IFIP WG 5.7 ... and Communication Technology, 692) [1st ed. 2023] 3031436873, 9783031436871

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IFIP AICT 692

Erlend Alfnes Anita Romsdal Jan Ola Strandhagen Gregor von Cieminski David Romero (Eds.)

Advances in Production Management Systems Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures

IFIP WG 5.7 International Conference, APMS 2023 Trondheim, Norway, September 17–21, 2023 Proceedings, Part IV

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IFIP Advances in Information and Communication Technology

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Editor-in-Chief Kai Rannenberg, Goethe University Frankfurt, Germany

Editorial Board Members TC 1 – Foundations of Computer Science Luís Soares Barbosa , University of Minho, Braga, Portugal TC 2 – Software: Theory and Practice Michael Goedicke, University of Duisburg-Essen, Germany TC 3 – Education Arthur Tatnall , Victoria University, Melbourne, Australia TC 5 – Information Technology Applications Erich J. Neuhold, University of Vienna, Austria TC 6 – Communication Systems Burkhard Stiller, University of Zurich, Zürich, Switzerland TC 7 – System Modeling and Optimization Lukasz Stettner, Institute of Mathematics, Polish Academy of Sciences, Warsaw, Poland TC 8 – Information Systems Jan Pries-Heje, Roskilde University, Denmark TC 9 – ICT and Society David Kreps , National University of Ireland, Galway, Ireland TC 10 – Computer Systems Technology Achim Rettberg, Hamm-Lippstadt University of Applied Sciences, Hamm, Germany TC 11 – Security and Privacy Protection in Information Processing Systems Steven Furnell , Plymouth University, UK TC 12 – Artificial Intelligence Eunika Mercier-Laurent , University of Reims Champagne-Ardenne, Reims, France TC 13 – Human-Computer Interaction Marco Winckler , University of Nice Sophia Antipolis, France TC 14 – Entertainment Computing Rainer Malaka, University of Bremen, Germany

IFIP Advances in Information and Communication Technology The IFIP AICT series publishes state-of-the-art results in the sciences and technologies of information and communication. The scope of the series includes: foundations of computer science; software theory and practice; education; computer applications in technology; communication systems; systems modeling and optimization; information systems; ICT and society; computer systems technology; security and protection in information processing systems; artificial intelligence; and human-computer interaction. Edited volumes and proceedings of refereed international conferences in computer science and interdisciplinary fields are featured. These results often precede journal publication and represent the most current research. The principal aim of the IFIP AICT series is to encourage education and the dissemination and exchange of information about all aspects of computing. More information about this series at https://link.springer.com/bookseries/6102

Erlend Alfnes Anita Romsdal Jan Ola Strandhagen Gregor von Cieminski David Romero Editors •

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Advances in Production Management Systems Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures IFIP WG 5.7 International Conference, APMS 2023 Trondheim, Norway, September 17–21, 2023 Proceedings, Part IV

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Editors Erlend Alfnes Norwegian University of Science and Technology Trondheim, Norway Jan Ola Strandhagen Norwegian University of Science and Technology Trondheim, Norway

Anita Romsdal Norwegian University of Science and Technology Trondheim, Norway Gregor von Cieminski ZF Friedrichshafen AG Friedrichshafen, Germany

David Romero Tecnológico de Monterrey Mexico City, Mexico

ISSN 1868-4238 ISSN 1868-422X (electronic) IFIP Advances in Information and Communication Technology ISBN 978-3-031-43687-1 ISBN 978-3-031-43688-8 (eBook) https://doi.org/10.1007/978-3-031-43688-8 © IFIP International Federation for Information Processing 2023 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Paper in this product is recyclable.

Preface

The year 2023 has undoubtedly been a year of contrasts. We are experiencing stunning developments in technology, and creating new products, services, and systems that are changing the way we live and work. Simultaneously, we are experiencing multiple conflicts around the world and the brutal effects of climate change. While many experience success and improved standards of living, others face threats to their lives and even loss. A Scientific Conference cannot change this but can be seen as a symbol for aiming for a different future. We create new knowledge and solutions, we share all our achievements, and we meet to create new friendships and meet people from all over the world. The International Conference on “Advances in Production Management Systems” (APMS) 2023 is the leading annual event of the IFIP Working Group (WG) 5.7 of the same name. At the Conference in Trondheim, Norway, hosted by the Norwegian University of Science and Technology (NTNU), more than 200 papers were presented and discussed. This is a significant step up from the first APMS Conference in 1980, which assembled just a few participants. The IFIP WG5.7 was established in 1978 by the General Assembly of the International Federation for Information Processing (IFIP) in Oslo, Norway. Its first meeting was held in August 1979 with all its seven members present. The WG has since grown to 108 full members and 25 honorary members. After 43 years, APMS has returned to the city where it started. The venue in 1980 was Lerchendal Gård, and the topic marked the turn of a decade: “Production Planning and Control in the 80s”. The papers presented attempted to look into the future – a future which at that time was believed to be fully digitalized. One foresaw that during the coming decade, full automation and optimization of complete manufacturing plants, controlled by a central computer, would be a reality. The batch processing of production plans would be replaced by online planning and control systems. No other technology can show a more rapid development and impact in industry and society than Information and Communication Technology (ICT). The APMS 2023 program shows that the IFIP WG5.7 still can make and will continue to make a significant contribution to production and production management disciplines. In 2023, the International Scientific Committee for APMS included 215 recognized experts working in the disciplines of production and production management systems. For each paper, an average of 2.5 single-blind reviews were provided. Over two months, each submitted paper went through two rigorous rounds of reviews to allow authors to revise their work after the first round of reviews to guarantee the highest scientific quality of the papers accepted for publication. Following this process, 213 full papers were selected for inclusion in the conference proceedings from a total of 224 submissions. APMS 2023 brought together leading international experts from academia, industry, and government in the areas of production and production management systems to discuss how to achieve responsible manufacturing, service, and logistics futures. This

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included topics such as innovative manufacturing, service, and logistics systems characterized by their agility, circularity, digitalization, flexibility, human-centricity, resiliency, and smartification contributing to more sustainable industrial futures that ensure that products and services are manufactured, servitized, and distributed in a way that creates a positive effect on the triple bottom line. The APMS 2023 conference proceedings are organized into four volumes, covering a large spectrum of research addressing the overall topic of the conference “Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures”. We would like to thank all contributing authors for their quality research work and their willingness to share their findings with the APMS and IFIP WG5.7 community. We are equally grateful for the outstanding work of all the International Reviewers, the Program Committee Members, and the Special Sessions Organizers. September 2023

Erlend Alfnes Anita Romsdal Jan Ola Strandhagen Gregor von Cieminski David Romero

Organization

Conference Chair Jan Ola Strandhagen

Norwegian University of Science and Technology, Norway

Conference Co-chair Gregor von Cieminski

ZF Friedrichshafen AG, Germany

Conference Honorary Chair Asbjørn Rolstadås

Norwegian University of Science and Technology, Norway

Program Chair Erlend Alfnes

Norwegian University of Science and Technology, Norway

Program Co-chairs Heidi Carin Dreyer Daryl Powell Bella Nujen Anita Romsdal David Romero

Norwegian University of Science and Norway Norwegian University of Science and SINTEF Manufacturing, Norway Norwegian University of Science and Norway Norwegian University of Science and Norway Tecnológico de Monterrey, Mexico

Technology, Technology/ Technology, Technology,

Organization Committee Chair Anita Romsdal

Norwegian University of Science and Technology, Norway

Doctoral Workshop Chair Hans-Henrik Hvolby

Aalborg University, Denmark

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Organization

Doctoral Workshop Co-chair David Romero

Tecnológico de Monterrey, Mexico

List of Reviewers Federica Acerbi Luca Adelfio Natalie Cecilia Agerskans El-Houssaine Aghezzaf Rajeev Agrawal Carla Susana Agudelo Assuad Kosmas Alexopoulos Kartika Nur Alfina Erlend Alfnes Antonio Pedro Dias Alves de Campos Terje Andersen Joakim Andersson Dimitris Apostolou Germán Arana Landín Simone Arena Emrah Arica Veronica Arioli Nestor Fabián Ayala Christiane Lima Barbosa Mohadese Basirati Mohamed Ben Ahmed Justus Aaron Benning Aili Biriita Bertnum Belgacem Bettayeb Seyoum Eshetu Birkie Umit Sezer Bititci Klas Boivie Alexandros Bousdekis Nadjib Brahimi Greta Braun Gianmarco Bressanelli Jim J. Browne Patrick Bründl Kay Burow Jenny Bäckstrand Jannicke Baalsrud Hauge Robisom Damasceno Calado Luis Manuel Camarinha-Matos Violetta Giada Cannas

Ayoub Chakroun Zuhara Chavez Ferdinando Chiacchio Steve Childe Chiara Cimini Florian Clemens Beatrice Colombo Federica Costa Catherine da Cunha Flávia de Souza Yüksel Değirmencioğlu Demiralay Enes Demiralay Tabea Marie Demke Mélanie Despeisse Candice Destouet Slavko Dolinsek Milos Drobnjakovic Eduardo e Oliveira Malin Elvin Christos Emmanouilidis Hakan Erdeş Kristian Johan Ingvar Ericsson Victor Eriksson Adrodegari Federico Matteo Ferrazzi Jannick Fiedler Erik Flores-García Giuseppe Fragapane Chiara Franciosi Susanne Franke Enzo Frazzon Stefano Frecassetti Jan Frick Paolo Gaiardelli Clarissa A. González Chávez Jon Gosling Danijela Gračanin Daniela Greven Eric Grosse

Organization

Zengxu Guo Christopher Gustafsson Petter Haglund Lise Lillebrygfjeld Halse Trond Halvorsen Robin Hanson Stefanie Hatzl Theresa-Franziska Hinrichsen Maria Holgado Christian Holper Djerdj Horvat Karl Anthony Hribernik Hans-Henrik Hvolby Natalia Iakymenko Niloofar Jafari Tanya Jahangirkhani Tim Maximilian Jansen Yongkuk Jeong Kerstin Johansen Björn Johansson Bjørn Jæger Ravi Kalaiarasan Dimitris Kiritsis Takeshi Kurata Juhoantti Viktor Köpman Nina Maria Köster Danijela Lalić Beñat Landeta Nicolas Leberruyer Ming Lim Maria Linnartz Flavien Lucas Andrea Lucchese Egon Lüftenegger Ugljesa Marjanovic Julia Christina Markert Melissa Marques-McEwan Antonio Masi Gokan May Matthew R. McCormick Khaled Medini Jorn Mehnen Joao Gilberto Mendes dos Reis Hajime Mizuyama Eiji Morinaga Sobhan Mostafayi Darmian

Mohamed Naim Farah Naz Torbjørn Netland Phu Nguyen Kjeld Nielsen Ana Nikolov Sang Do Noh Antonio Padovano Julia Pahl Martin Perau Margherita Pero Mirco Peron Fredrik Persson Marta Pinzone Fabiana Pirola Adalberto Polenghi Daryl John Powell Rossella Pozzi Vittaldas Prabhu Hiran Harshana Prathapage Moritz Quandt Ricardo Rabelo Mina Rahmani Slavko Rakic Mario Rapaccini R. M. Chandima Ratnayake Eivind Reke Daniel Resanovic Ciele Resende Veneroso Irene Roda David Romero Anita Romsdal Christoph Roser Nataliia Roskladka Monica Rossi Martin Rudberg Roberto Sala Jan Salzwedel Adrian Sánchez de Ocaña Kszysztof Santarek Biswajit Sarkar Claudio Sassanelli Laura Scalvini Maximilian Schacht Bennet Schulz Marco Semini

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Organization

Sourav Sengupta Fabio Sgarbossa Vésteinn Sigurjónsson Marcia Terra Silva Katrin Singer-Coudoux Ivan Kristianto Singgih Lars Skjelstad Riitta Johanna Smeds Selver Softic Per Solibakke Vijay Srinivasan Kenn Steger-Jensen Oliver Stoll Jan Ola Strandhagen Jo Wessel Strandhagen Nick B. Szirbik Endre Sølvsberg Iris D. Tommelein Mario Tucci Ebru Turanoglu Bekar Ioan Turcin Arvind Upadhyay Andrea Urbinati

Mehmet Uzunosmanoglu Bruno Vallespir Ivonaldo Vicente da Silva Kenneth Vidskjold Vivek Vijayakumar Gregor von Cieminski Paul Kengfai Wan Piotr Warmbier Kasuni Vimasha Weerasinghe Shaun West Stefan Alexander Wiesner Joakim Wikner Magnus Wiktorsson Heiner Winkler Jong-Hun Woo Thorsten Wuest Lara Popov Zambiasi Matteo Zanchi Yuxuan Zhou Iveta Zolotová Anne Zouggar Mikael Öhma

Contents – Part IV

Circular Manufacturing and Industrial Eco-Efficiency Developing Data Models for Smart Environmental Performance Management in Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mélanie Despeisse, Qi Fang, Ebru Turanoglu Bekar, Nils Ólafur Egilsson, Karolina Kazmierczak, Lena Moestam, Helena Söderberg, Dennis Andersson, Jenny Hörnlund, and Björn Molin

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Optimization of Distribution Center and Supply Chain Management with Mixable Products: A Case Study of Recycling Mixable Metal Waste in South Korea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sewon Oh, Junseok Park, Juyoung Kim, Alex Yoosuk Kim, and Ilkyeong Moon

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A Stochastic Frontier Analysis (SFA)-Based Method for Detecting Changes in Manufacturing Energy Efficiency by Sector and Time . . . . . . . . . . . . . . . . Ga Hyun Lee and Hyun Woo Jeon

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Analyzing Emerging Circular Economy Business Models in the E-waste Sector Through the Business Model Canvas . . . . . . . . . . . . . . . . . . . . . . . . . W. Tirufat Dejene, Moreno Muffatto, and Francesco Ferrati

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Gap Analysis for CO2 Accounting Tool by Integrating Enterprise Resource Planning System Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Martin Perau, Dogukan Seker, Tobias Schröer, and Günther Schuh

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How Can Digitalisation Support the Circular Economy? An Empirical Analysis from the Manufacturing Industry . . . . . . . . . . . . . . . . . . . . . . . . . . Beatrice Colombo, Albachiara Boffelli, Jacopo Colombo, Alice Madonna, and Simone Villa Assessment Framework for Circular Supply Chains Management Towards Net Zero Targets in Limburg, the Netherlands . . . . . . . . . . . . . . . . . . . . . . . Verena Zielke and Adriana Saraceni

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Stakeholder Management in Circular Economy Product Development in the Mining Industry – A Case Study. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 Juhoantti Köpman, Vesa-Matti Leiviskä, Harri Haapasalo, Petteri Annunen, and Jukka Majava

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Contents – Part IV

Understanding the Implications of Circular Business Models for Businesses and Supply Chains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 Melissa Marques-McEwan and Umit Sezer Bititci Exploiting Information Systems for Circular Manufacturing Transition: A Guiding Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 Federica Acerbi, Claudio Sassanelli, Mélanie Despeisse, and Marco Taisch Circularity Impact on Automotive Assembly – What Do We Know? . . . . . . . . 144 Kerstin Johansen, Marie Jonsson, and Sandra Mattsson Circular Production Equipment – Futuristic Thought or the Necessity of Tomorrow? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 Malin Elvin, Jessica Bruch, and Ioanna Aslanidou Systematic Green Design in Production Equipment Investments: Conceptual Development and Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 Seyoum Eshetu Birkie, Zuhara Zemke Chavez, Emma Lindahl, Martin Kurdve, Jessica Bruch, Monica Bellgran, Lotta Bohlin, Mikael Bohman, and Malin Elvin Towards a Circular Manufacturing Competency Model: Analysis of the State of the Art and Development of a Model. . . . . . . . . . . . . . . . . . . . . . . . 189 Marta Pinzone and Marco Taisch Implications of Improving Resource Efficiency When Utilizing Residual Raw Material on Trawlers Producing Head and Gutted Fish . . . . . . . . . . . . . . 200 Per Solibakke Selective Complexity Determination at Cost Based Alternatives to Re-manufacture. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215 Sotirios Panagou, Giuseppe La Cava, Fabio Fruggiero, and Francesco Mancusi Towards a Green Transition: Preliminary Steps of a Quantitative Model . . . . . 229 Federica Costa and Alberto Portioli-Staudacher Rapid Sorting of Post-consumer Scrap Aluminium Alloys Based on LaserInduced Breakdown Spectroscopy (LIBS) . . . . . . . . . . . . . . . . . . . . . . . . . . 241 Md Ali Akram, Ragnar Holthe, and Geir Ringen Understanding Sustainability: Cases from the Norwegian Maritime Industry . . . 256 Olena Klymenko and Lise Lillebrygfjeld Halse

Contents – Part IV

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Smart Manufacturing to Support Circular Economy Assessing the Interplay Between Circular Economy, Industry 4.0, and Lean Production: A Bibliometric Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273 Violetta Giada Cannas, Riccardo Fabris, Rossella Pozzi, Matteo Ridella, Nicolò Saporiti, and Andrea Urbinati Adopting Circular Economy Paradigm to Waste Prevention: Investigating Waste Drivers in Vegetable Supply Chains. . . . . . . . . . . . . . . . . . . . . . . . . . 288 Madushan Madhava Jayalath, R. M. Chandima Ratnayake, H. Niles Perera, and Amila Thibbotuwawa Product Information Management and Extended Producer Responsibility Opportunities and Challenges of Applying Internet of Things for Improving Supply Chain Visibility of Incoming Goods: Results from a Pilot Study . . . . . 305 Ravi Kalaiarasan, Malin Ducloux, Tarun Kumar Agrawal, Jannicke Baalsrud Hauge, and Magnus Wiktorsson A Review on Design for Repair Practices and Product Information Management. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319 Nataliia Roskladka, Gianmarco Bressanelli, Giovanni Miragliotta, and Nicola Saccani Approach on How to Handle Digital Thread Information in Manufacturing with a Human-Centric Perspective Taking into Account a Didactic Factory . . . 335 Kay Burow, Patrick Klein, Karl Hribernik, and Klaus-Dieter Thoben Textile Industry Circular Supply Chains and Digital Product Passports. Two Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 350 Bjørn Jæger and Sivert Myrold Product and Asset Life Cycle Management for Sustainable and Resilient Manufacturing Systems Green Design: Introducing a New Methodology to Increase Environmental Sustainability in Capital Investments at AstraZeneca . . . . . . . . . . . . . . . . . . . 367 Filip Magnusson, Mikael Bohman, and Monica Bellgran Comparative Analysis of Sustainability and Resilience in Operations and Supply Chain Management. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 382 Piotr Warmbier

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Contents – Part IV

Capturing Value by Extending the End of Life of a Machining Department Through Data Analytics: An Industrial Use Case . . . . . . . . . . . . . . . . . . . . . 398 Federica Acerbi, Davide Pasanisi, Valerio Pesenti, Luca Verpelli, and Marco Taisch The Role of Asset Ownership in PSS Theory: An Insight from Expert Interviews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 412 Oliver Stoll, Shaun West, Fabiana Pirola, and Roberto Sala Identifying Customer Returns in a Printed Circuit Board Production Line Using the Mahalanobis Distance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 426 Endre Sølvsberg, Simone Arena, Fabio Sgarbossa, and Per Schjølberg Sustainable Mass Customization in the Era of Industry 5.0 A Systematic Literature Review on the Developments in the Field of Flexible and Fully Automated Assembly Stations Within the Automotive Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441 Bennet Schulz, Katja Klingebiel, and Daniel Reh Mixed Integer Programming for Integrated Flexible Job-Shop and Operator Scheduling in Flexible Manufacturing Systems . . . . . . . . . . . . . . . . . . . . . . . 460 Reza Ghorbani Saber, Pieter Leyman, and El-Houssaine Aghezzaf Food and Bio-manufacturing Towards More Sustainable Food Processing: A Structured Tool for the Integration and Analysis of Sustainability Aspects of Processing Equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 473 Sara Esmaeilian, Anita Romsdal, Eirin Skjøndal Bar, Bjørn Tore Rotabakk, Jørgen Lerfall, and Anna Olsen Transforming Food Production: Smart Containers for Sustainable and Transparent Food Supply Chains. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 489 Peter Burggräf, Tobias Adlon, Fabian Steinberg, Jan Salzwedel, Philipp Nettesheim, and Henning Tschauder Produce It Sustainably: Life Cycle Assessment of a Biomanufacturing Process Through the Ontology Lens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 504 Ana Nikolov, Milos Drobnjakovic, and Boonserm Kulvatunyou

Contents – Part IV

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Battery Production Development and Management Battery Production Systems: State of the Art and Future Developments . . . . . . 521 Mélanie Despeisse, Björn Johansson, Jon Bokrantz, Greta Braun, Arpita Chari, Xiaoxia Chen, Qi Fang, Clarissa A. González Chávez, Anders Skoogh, Johan Stahre, Ninan Theradapuzha Mathew, Ebru Turanoglu Bekar, Hao Wang, and Roland Örtengren Assessment of the Main Criticalities in the Automotive Battery Supply Chain: A Professionals’ Perspective. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 536 Valérie Botta-Genoulaz and Giulio Mangano Integration of Hydropower and Battery Energy Storage Systems into Manufacturing Systems - A Discrete-Event Simulation . . . . . . . . . . . . . . . . . 549 Carla Susana Agudelo Assuad, Lennart Deike, Zhicheng Liao, and Md Ali Akram Operations and SCM in Energy-Intensive Production for a Sustainable Future A Digital Twin–Based Approach to Reinforce Supply Chain Resilience: Simulation of Semiconductor Shortages . . . . . . . . . . . . . . . . . . . . . . . . . . . . 563 Phu Nguyen, Dmitry Ivanov, and Fabio Sgarbossa Integrating Closed-Loop Supply Chain Design-Planning into Product Development: A Systematic Literature Review . . . . . . . . . . . . . . . . . . . . . . . 577 Sobhan Mostafayi Darmian, Fabio Sgarbossa, and Torgeir Welo Life Cycle Assessment of Red Mud-Based Geopolymer Production at Industrial Scale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 593 Luca Adelfio, Fabio Sgarbossa, Rosanna Leone, and Giada La Scalia Product Recovery Options in Closed Loop Supply Chain Networks: A Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 607 Hiran Prathapage, Dmitry Ivanov, and Fabio Sgarbossa Challenges and Opportunities for Adopting Green Technologies in Maritime Transportation Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 620 Mohamed Ben Ahmed, Even Molland, and Tore Tomasgard A Framework for Enabling Manufacturing Flexibility and Optimizing Industrial Demand Response Services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 634 Paul Kengfai Wan, Matteo Ranaboldo, Alessandro Burgio, Chiara Caccamo, and Giuseppe Fragapane

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Contents – Part IV

Discrete Event Simulation for Improving the Performance of Manufacturing Systems: A Case Study for Renewable Energy Sources Production . . . . . . . . . 650 Panagiotis Mavrothalassitis, Nikolaos Nikolakis, and Kosmas Alexopoulos Analysing Barriers to Achieving SDG 7. Managing Green Product Development in the Wind Energy Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . 666 Rakel García-Alonso, Beñat Landeta-Manzano, German Arana-Landín, and Rubén Jiménez-Redal Resilience Management in Supply Chains Derivation of the Data Attributes for Identification of Incorrect Events in Supply Chain Event Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 685 Jokim Janßen, Tobias Schröer, Günther Schuh, and Wolfgang Boos Resilience Configurator for Procurement . . . . . . . . . . . . . . . . . . . . . . . . . . . 699 Maria Spiß, Tobias Schröer, and Günther Schuh A Proposal of Resilient Supply Chain Network Planning Method with Supplier Selection and Inventory Levels Determination Using Two-Stage Stochastic Programming. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 714 Hibiki Kobayashi, Toshiya Kaihara, Daisuke Kokuryo, Rina Tanaka, Masashi Hara, Yuto Miyachi, and Puchit Sariddichainunta Function-Based Approach for Disaster Relief Logistics in Germany . . . . . . . . 730 Theresa-Franziska Hinrichsen, Eduardo Colangelo, Martina Schaffer, Merlit Kirchhöfer, and Tobias Spanke Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 745

Circular Manufacturing and Industrial Eco-Efficiency

Developing Data Models for Smart Environmental Performance Management in Production Mélanie Despeisse1(B) , Qi Fang1 , Ebru Turanoglu Bekar1 , Nils Ólafur Egilsson2 , Karolina Kazmierczak2 , Lena Moestam3 , Helena Söderberg3 , Dennis Andersson3 , Jenny Hörnlund4 , and Björn Molin4 1 Department of Industrial and Materials Science, Chalmers University of Technology,

Gothenburg, Sweden {melanie.despeisse,qifa,ebrut}@chalmers.se 2 Circular Economy, Chalmers Industriteknik, Gothenburg, Sweden {nils.o.egilsson,karolina.kazmierczak}@chalmersindustriteknik.se 3 Department Quality and Engineering, Volvo Group Trucks Operations, Gothenburg, Sweden {lena.moestam,helena.hs.soderberg,dennis.andersson}@volvo.com 4 Design and Planning Automation, Sandvik Machining Solutions AB, Stockholm, Sweden {jenny.hornlund,bjorn.molin}@sandvik.com

Abstract. For manufacturing companies to prosper in the long term, they must demonstrate contribution to sustainable development by implementing greener practices using approaches such eco-efficiency and circular economy; i.e., creating social and economic value while minimising the environmental impact of production through efficient, closed-loop circulation of resources. In addition, industrial digitalization presents new opportunities to unlock new ways to measure complex systems’ performance and systematically improve towards circular economy and sustainability. This paper presents the results of a feasibility study aiming to develop a practical toolkit to implement environmental sustainability concepts at factory level. To achieve the project objective, we focused on data handling practices for environmental performance management, including process mapping, data inventory, data quality assessment, and gap analysis to identify existing strengths and define areas of improvement to boost the environmental performance of production systems. Keywords: Sustainable production · Green manufacturing · Eco-efficiency · Digitalization · Data management system

© IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 3–15, 2023. https://doi.org/10.1007/978-3-031-43688-8_1

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1 Introduction 1.1 Background and Aim The combined effects of natural resource consumption, associated pollutant emissions and waste generation are creating environmental pressures requiring urgent actions to curb global and local impacts on both ecological and human systems [1, 2]. The manufacturing industry has traditionally been a major contributor to such negative environmental and social impacts, but also a great contributor to social and economic development [3]. A new paradigm has emerged since the late 90s where the manufacturing industry is viewed as a driver and catalyst for the greening of our society while meeting the needs of a growing population [4, 5] with the help of advanced manufacturing technologies and sustainability concepts such as green innovation, pollution prevention, ecodesign, eco-efficiency and circular economy. While knowledge on sustainable manufacturing has developed since the 1990s and accelerated in the last decade, a knowing-doing gap calls for practical methods to increase the uptake of more sustainable solutions in industry. Many researchers and organisations have developed environmental tools and methods [6–9]. These tools can be combined to cover various dimensions, scopes and depths in a consecutive, complementary, encompassing or overlapping manner [6]. Frameworks such as The Natural Step [7] also defined how sustainability principles, business strategy, activities, concepts and tools, and environmental impact relate to each other to help integrate environmental impact assessment and management more systematically. But these tools remain largely unused due to implementation challenges ranging from, e.g., methodological complexity (requiring high levels of expertise, time and efforts to be used), lack of data and data quality issues, and lack of knowledge, time or clear responsibility to implement advanced analytical methods [9, 10]. In addition, environmental assessment results are typically limited to monitoring and reporting purposes and do not always provide a meaningful basis for decision makers to plan, develop, operate and improve operations. Therefore, it is essential to identify not only the right types of assessments and indicators for sustainable manufacturing, but also to define the practices for using the assessment results as information-basis for operations management decisions. Digitalization can help overcome some of the implementation challenges mentioned by automating many of the tasks required to collect data, simulate energy-mass balance to fill data gaps, perform environmental analyses, visualise and communicate the results, and optimise production performance accounting for multiple, complex parameters [1114] Research on data life cycle [15, 16], environmental data management [14, 17], and data quality challenges defined data model requirements for green manufacturing [18-20]. This paper presents the results of a feasibility study called FREED (Factory Resource and Energy Efficiency through Digitalization). The project aimed to help manufacturing companies identify data-supported methods readily implementable to manage the environmental performance of their operations. The specific focus was on data handling practices and data management systems to connect factory data to environmental information systems which can be used to monitor processes and identify improvements. The

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project investigated how increased digital maturity can help to achieve green manufacturing, thereby developing a tailored digital-green maturity assessment. In other words, it explored ways to automate as much as possible data handling for eco-efficiency so the efforts can shift from data collection, analysis and visualisation to exploitation of this data for development and improvement of greener operations. 1.2 Previous Work Previous work based on a systematic literature review of published empirical studies identified challenges to integrate green manufacturing tools and methods in daily operations management [21]. This systematic review proposed ways in which digitalization can help overcome these challenges. They form a research agenda with eight propositions: Digitalization should support… • • • • • • • •

resource efficiency and environmental impact reduction; the proactive integration of sustainability as a driver of performance; a holistic view of manufacturing systems to identify potential trade-offs; a life cycle perspective of manufacturing systems to avoid srebound effects; sustainability assessments for data-informed and fact-based business decisions; hybrid sustainability methods for manufacturing system optimisation; the integration of sustainability into established operations management systems; manufacturers in a more circular, service-based economy.

In addition, a meta-analysis of publications at the intersection of industrial digitalization and green manufacturing showed that eco-efficiency principles can be addressed directly or indirectly by digital technologies [22]. Improvement activities related to different environmental impact categories are typically measured using key performance indicators (KPIs), such material, energy and water consumption, water quality, pollution and toxicity, climate impact, and waste management [23]. To shift towards circular manufacturing [24, 25], circular economy principles need to be applied to resource flows and industrial assets in factories where there is no possibility to change the product design. The usability and useful of various circularity indicators specifically for a manufacturing company were reviewed in a previous study [9]. One of the indicators identified to best fit the requirements of manufacturing was the Material Circularity Indicator (MCI) by the Ellen MacArthur foundation [26]. Since the MCI is increasingly used by many companies, it can be used as a benchmark across similar industries to compare products from different producers. Efforts on standardising ways to measure circularity and developing information systems are still ongoing, such as product digital passports [27, 28]. Data management systems for environmental performance play a crucial role in helping organizations to measure, manage, and improve their environmental impact. Various characteristics of data models were identified as desired throughout the data life cycle [15], including data model architecture, data acquisition and storage, and data exploitation. By selecting the right characteristics of data models for their needs, organizations can streamline their data management processes, gain insights into their environmental performance, and make informed decisions about how to reduce their environmental footprint [20].

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2 Materials and Methods The project work followed a case study methodology and consisted of two company cases with high interest in data-driven solutions for greener manufacturing. The feasibility study focused on steps 1–3 and the process-level maturity assessment, with iterations as needed to deliver recommended actions for step 5 [29]; see workplan in Fig. 1. Since the study was limited to evaluating the feasibility of environmental methods and the readiness of the case companies, this paper focuses on the results of the qualitative study primarily (step 2) with some initial findings from the quantitative study (step 3).

Fig. 1. Work plan and activities adapted from [29].

The first step consisted of site visits and observations for the two case companies, including presentations from experts at the companies to better understand the manufacturing processes and the current data used to monitor environmental performance.

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Then series of focus group workshops were conducted to create process maps alongside a data inventory which helped identify feasible environmental methods as well as data gaps. In addition, a data quality assessment identified data model characteristics supporting the implementation of eco-efficiency principles and associated environmental KPIs. This quality assessment was based on a survey conducted in three eco-efficiency workshops prior to the feasibility study with 45 participants; see Fig. 2 for survey respondents’ background. In addition to representatives from manufacturing companies, the responses from researchers, consultants and other experts in the field of sustainable production were also collected to get broad insights into desirable characteristics of data models for eco-efficiency. The workshops were complemented by an in-depth focus group with two experts at one of the case companies during the feasibility study.

Fig. 2. Survey respondents’ background.

3 Results And Discussion 3.1 Manufacturing System Understanding The project team visited the case companies to get an introduction to the companies’ environmental management approach and an overview of manufacturing site. During the visits, the team met key stakeholders, including production specialists, line managers, and factory sustainability managers to learn about the manufacturing processes, current data handling, and environmental performance monitoring. The information gathered during the visits and focus group workshops was used for the process mapping, data inventory and data quality assessment related to data model characteristics. 3.2 Qualitative Study Process Mapping and Data Inventory. The purpose of the qualitative study was to explore various methods to model key features of the manufacturing systems determining the factory’s environmental performance. Since no single tool covers all aspects of interest, multiple methods were applied to capture the dimensions considered as potentially useful for application of environmental assessment methods. The IDEF0 method is a systematic tool to model manufacturing functions at different level of details. In the feasibility study, IDEF0 logic was applied to model the case

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companies’ manufacturing site, selected production areas, multi-machine systems, and single machines. Fig. 3 is an example of process maps in IDEF0 style, from factory level as the top parent diagram to the child level of one production area and the manufacturing processes within this production area. In the process mapping, inputs, outputs, mechanisms, and facilities attached to each functional unit were visualised with arrows. For the data inventory, an additional layer of information was added to the process maps by colour-coding the different arrows to show data availability based on information gathered in manufacturing system understanding step (green: available, blue: more information needed, red: not available). Hence, an initial picture of data availability was combined with the process mapping which helped bring the project team to the same page for further discussions.

Fig. 3. Process maps of a case company’s manufacturing site, production area and processes.

Data Quality Assessment. To gain more insights into the importance of various data model characteristics and the quality of data to measure environmental performance, workshops were conducted with experts from the project consortium and external stakeholders. While the focus was on manufacturing companies, the responses from researchers, consultants and other experts in the field of sustainable production was deemed valuable for the data quality assessment. The survey defined data model characteristics [15] as listed in Table 1. Then the survey respondents were requested to prioritise which characteristics were considered as the most or the least important to

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measure environmental impacts and improve resource efficiency in manufacturing processes. The importance of these various data model characteristics were first evaluated individually by the workshops’ participants, and then discussed in a plenary session. Table 1. Data model characteristics and their definition. Data model characteristics

Brief definition

Essentialness

Only collect data required to evaluate environmental performance towards company goals/targets. No data is left unused

Completeness

Scope comprehensive enough to account for environmental externalities upstream (suppliers) and downstream (customers)

Consistency

Standard data and metadata definitions are available to avoid misinterpretation

Correctness

Data values are assessed and repaired to avoid faults or recording defects. The data presented to users is unaltered and free from retrieval faults

Quality engineering

The data management system is managed by a central function using data quality engineering methods

Security

Accessibility protocols are in place for different end-users

Searchability

The data management system allows access to specific data based on the end-users’ needs

Understandability

The presented data is easy to understand. Data visualization matches user needs

Timeliness

The data is presented only when needed. Users can update the data values as often as needed

Flexibility

The data management system can be used for different applications

Compatibility

The data management system is compatible with other systems. The data is exported in standard formats

Data sharing

Data-sharing protocols to support internal and external data exchange are constructed based on standard metadata definitions

Evolvability

Regular maintenance is done by a central function to meet evolving requirements and is updated based on users’ feedback

The survey results are shown in Fig. 4 with the industry responses shown separately. Understandability, consistency and correctness were scored very high by most participants, regardless of background. Interestingly, the characteristics security was rated lower by companies than academic researchers with only two industry participants rating it as important. Completeness and essentialness were rated high by non-industry participants but less so by companies. Data sharing, flexibility, completeness, evolvability, security and essentialness yielded the most polarised responses. During the plenary

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discussions, it became clear that desirable data model characteristics are highly dependent on the type of product or process in question, the level of environmental performance already achieved and the level of digital maturity, the size and industrial sector of the company considered, amongst other factors. If a company is already performing well environmentally, then the need for data-driven solutions may be lower than for a company getting started with their sustainability transition, thus different characteristics would be prioritised. Conversely, if a company has achieved high digital maturity for non-environmental purposes, then translating their data capabilities for greener purposes could lead to different data model characteristics being prioritised.

Fig. 4. The importance of data model characteristics to support eco-efficient manufacturing.

To explore in more depth the importance of data model characteristics for different environmental KPIs, a focus group workshop was organised with the environmental manager and data scientist at one of the case companies. The data model characteristics were evaluated for each type of environmental KPI: energy efficiency, material efficiency, water quality and efficiency, waste reduction, carbon footprint, and toxicity. The results are shown in Fig. 5. The data model characteristic that holds the greatest importance across all environmental data categories is completeness, whereas essentialness is ranked the lowest. Moreover, the characteristics of consistency, correctness, and quality engineering are also deemed highly important for all categories of environmental data. The characteristic evolvability is also ranked medium to high, except for carbon footprint. The importance of the other characteristics varies across different environmental impact categories. For example, understandability and searchability were prioritised characteristics for waste data. In contrast, compatibility was deemed most important for material data to be used for process equipment requirements, carbon footprint and toxicity to comply with reporting requirements. Quality engineering, security, data sharing and evolvability are considered as particularly important for energy data. 3.3 Quantitative Study Environmental Performance Quantification. This feasibility study focused exploiting production data to quantify eco-efficiency and circularity indicators to support sustainable manufacturing. Such quantification is essential to understand the environmental

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Fig. 5. The importance of data model characteristics for different environmental KPIs.

performance of manufacturing processes which otherwise is not visible and therefore difficult to improve. Environmental performance assessment and visualisation are necessary to support evidence-based decision making. The emphasis of this step was on existing data at the case companies and relatively simple methods which are compatible with existing data management systems. Based on previous steps, two basic environmental methods were identified as promising for quantitative analysis, namely material flow balance analysis, such as Sankey diagrams, and resource efficiency flowchart, such as Pareto diagrams. However, they could not be completed due to gaps in environmental data, also mentioned in the data quality assessment. Table 2 shows examples of data gaps for one of the case companies. Table 2. Examples of data gaps limiting environmental methods implementation. Characteristics

Data gaps identified

Completeness

Specific water consumption unavailable for selected production area

Timeliness

Consumable consumption not updated in real-time in the warehouse

Consistency

Electricity consumption collected every 10s; steel coils consumption recorded daily; waste data reported monthly

Correctness

Timestamps not fully aligned between equipment

Understandability

Data collection sheets not standardized

Circularity Indicator Screening and Selection. Circularity indicators are useful to guide decisions in product design and business development. However, they often have limited relevance to improve manufacturing operations for a given product design or a given business model. Most indicators point to improvements beyond the control of manufacturing stakeholders but some circular strategies usually applied to the product are

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relevant for production materials and equipment. Based on the process maps, potential circular strategies that could be measured in the processes for both case companies were identified. The circular strategies identified as relevant at factory level include: recycled and renewable materials, production waste recycling, reduction in material types, with corresponding indicators presented in Table 3. Based on the circular strategies identified, a focus group workshop with two company participants evaluated the relevance of the proposed indicators for their facility. All aspects were found relevant at a factory level, however they were not all relevant for the material flows related to the specific processes investigated. In addition, industrial asset durability, repair and remanufacturing are also relevant at factory level. But these circular strategies relate to the maintenance function or to process development rather than daily process management, thus would require new stakeholders to be involved to be investigated and implemented. Table 3. Circular aspects identified in the process mapping. Category

Indicator

Recycled material inputs

• • • •

Renewables material inputs

• Renewable materials used in production • Renewable materials used in packaging

Recirculated material outputs, reused or sent to recycling

• • • • •

Reduction of material types

• Number of materials used in production • Number of materials used for packaging • Number of materials from suppliers’ packaging

Recycled materials used in production Recyclable materials used in production Recycled materials used in packaging Recyclable materials used in packaging

Number of products in take back system Reusable packaging Amount of materials sent to recycling Quality of materials sent to recycling Materials sent back to original producer

3.4 Action Plan The data gaps identified preventing the implementation of quantitative environmental methods were translated into improvement actions. For example, water meters will be installed and connected to collect data automatically. Furthermore, with improved data characteristics, factories can integrate environmental methods with environmental indicators. Material flow balance analysis can include CO2 emissions for environmental footprints quantification. Resource efficiency flow diagram using emissions as units can help identify top losses and non-value adding activities in environmental performance management. Finally, a maturity assessment was performed in parallel to the qualitative

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and quantitative study to better understand the current practices in place for environmental data management. The aim was to improve and possibly automate data handling practices so that more focus can be placed on using the information generated to improve environmental performance.

4 Conclusions and Next Steps This paper presented a feasibility study analysing the data management systems in place at the case companies’ manufacturing sites to identify existing strengths to build from and weaknesses to be remedied. The study focused on specific production areas within the manufacturing sites. The current state of production data management to support environmental practices was evaluated using process maps, data inventory and desirable data model characteristics evaluations. This qualitative study helped identify promising environmental methods to be applied in further work. New practices for data management were suggested to fill data gaps and to support the implementation of eco-efficiency and circularity in manufacturing. The overall procedure proposed in Fig. 1 will be part of a toolkit to support manufacturing companies in selecting suitable methods to integrate seamlessly environmental information their daily operations and continuous improvement activities. While advanced digital capabilities are not necessary to achieve high environmental performance, data-driven and data-supported solutions can be a powerful enabler. With increased digital maturity, the company can reduce the workload for collecting and translating data into useful information for green manufacturing improvements, ultimately leading to increased environmental maturity; see main concepts illustrated in Fig. 6. In addition, the toolkit developed aims to be generic and adaptable to ensure broad applicability in the manufacturing industry and to meet specific company’s needs building on their existing strengths and supporting improvements in critical areas.

Fig. 6. Using data-supported solutions to integrate environmental sustainability in operations.

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Acknowledgements. This work was supported by Swedish innovation agency Vinnova and the strategic innovation programme Produktion2030 under grant no. 2022–02460. The work was carried out within Chalmers’ Area of Advance Production. The support is gratefully acknowledged.

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Optimization of Distribution Center and Supply Chain Management with Mixable Products: A Case Study of Recycling Mixable Metal Waste in South Korea Sewon Oh1 , Junseok Park1 , Juyoung Kim2 , Alex Yoosuk Kim2 , and Ilkyeong Moon1,3(B) 1

2 3

Department of Industrial Engineering, Seoul National University, Seoul 08826, Korea [email protected] Maintain corporation, 23, Naksan 3-gil, Jongno-gu, Seoul 03088, Korea Institute for Industrial Systems Innovation, Seoul National University, Seoul 08826, Korea

Abstract. This paper addresses the need for systematic and efficient South Korean metal waste management. The growing waste management market demands further research on managing metal waste effectively. This paper presents a mathematical model to efficiently handle mixable waste from the standpoint of an intermediate disposal facility. The model considers the specific characteristics of the mixable metal waste market in South Korea and optimizes the operation of the intermediate disposal facility. The mixable metal waste is a mixture of sludge or powder that can be further mixed and stored in a cluster according to specific element criteria. The model determines how to mix and send the waste cluster to the desired disposal facilities after drying. This model presents a solution with a reasonable time to assist the operation of an intermediate disposal facility in the real world. The model can accommodate changes in operational situations, such as changes in the candidates of discharge and disposal facilities, step function unit costs of disposal facilities, and constraints on inventory levels. In addition, we offer insights into various operational aspects, such as whether current contracts are worthwhile and which facilities to contract with for future use. Keywords: waste management facility · mixable product

1

· metal waste · intermediate disposal

Introduction

As various industries develop, solid metal waste increases yearly as a by-product. According to Hoornweg and Bhada-Tata [4], the solid waste management market rose to 205.4 billion USD in 2012. By 2025, it will be a competitive market c IFIP International Federation for Information Processing 2023  Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 16–28, 2023. https://doi.org/10.1007/978-3-031-43688-8_2

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valued at 375.5 billion USD. Metal waste accounts for 7 percent of solid waste in South Korea, and the proportion of metal waste is predicted to increase worldwide. However, despite the growing waste management market, there are no related studies of systematic and efficient waste management for metal waste with various types of waste, usually in bulk forms. These bulk forms are metal chips, powder, and sludges. Among these, we define mixable metal waste, such as powder and sludges form of waste, as a mixture that can be mixed. In South Korea, the need for waste management is increasing as available land for disposal decreases and the manufacturing industry grows. In addition, as the manufacturing industry develops, metal waste containing a large amount of metal is also increasing. Recycling at the disposal facility has emerged as a reasonable means of metal waste treatment due to rising landfill costs and the lack of landfills. In addition, China’s ban on waste imports [7], stricter waste management regulations [5], asymmetric information among stakeholders in the waste management industry [8], and public interest in the environment have influenced waste facilities to be more lawful, efficient, and eco-friendly. The waste treatment process involves a series of steps that transport the waste from a discharge facility to a disposal facility, where it is processed for recycling. Figure 1 briefly shows the waste treatment process. A transporter moves waste from the discharge facility to an intermediate disposal facility. This waste is sampled for quantitative analysis of the concentration of elements, and a cluster is assigned. After going through a drying process in the intermediate disposal facility, the dried wastes are mixed and transported to the disposal facility for a later-stage recycling process. Because of the specificity of the waste produced, waste transport and treatment occur under a three-way contract between the discharge facility, transport company, and disposal facility. Typically, a discharge facility is a manufacturing facility that discharges metal-bearing wastes as a by-product of powder or sludge. A disposal facility is a company that recycles certain chemical elements. However, waste discharged from the discharge facility and waste desired by the disposal facility differs in elements and types. Also, most metal-bearing waste contains a great deal of moisture, so drying the waste is essential during recycling. In order to satisfy the characteristics of the waste management market, an intermediate disposal facility is necessary to serve as a distribution center during the intermediary phase of the overall waste recycling process. This paper deals with mixable metal waste, such as powder or sludge, among metal wastes with different shapes and characteristics. These wastes are mainly discharged as by-products of manufacturing facilities and can be easily recycled after drying without other metal processing. In addition, these wastes are in high demand at recycling sites due to their easy processing. In Korea, contracts are always possible only when the legal classification of waste is specified, so treating waste only in powder or sludge is realistic. The intermediate disposal facility receives wastes discharged from the discharge facility, dries them, and makes mixtures before sending them to the disposal facility. Unlike typical items in general distribution centers, mixable metal waste is a mixture that can be mixed and change

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Fig. 1. Flowchart of waste treatment.

elements during processing. Therefore, this waste is stored in a cluster rather than by product or part in the intermediate disposal facility. In addition, the intermediate disposal facility processes and discharges the waste, allowing it to register as a discharge facility and disposal facility simultaneously. Therefore, it is possible to operate more efficiently by separating a three-way contract rather than directly transporting from the discharge to the disposal facility. One separating three-way contract is between a discharge facility, an intermediate disposal facility, and a transport company. The other is between an intermediate disposal facility, a disposal facility, and a transport company. Research in solid waste management is actively proceeding, and in particular, a method of solving the waste disposal issue with vehicle routing problems was proposed. Anghinolfi et al. [1] and Das and Bhattacharyya [3] studied vehicle routing problems, from solid waste collection to disposal. Anwar et al. [2] studied the problem of minimizing the cost of all solid waste disposal in a specific region by dividing the scenarios into centralized and decentralized. Wagner and Bilitewski [9] conducted a study on the operation of an intermediate storage facility for preprocessing solid waste before treatment by the final disposal facility. For the safe operation of the intermediate storage facility, they recommended operational approaches focusing on environmental factors. However, few studies have considered the mixable characteristics of metal-bearing waste among solid waste, and few studies have suggested a reasonable solution using a mathematical model. In this paper, considering the specificity of the South Korean metal waste market, we present a mathematical model that optimally decides what to mix with the dried waste cluster and where to dispose of it from the standpoint of the intermediate disposal facility. The optimization model finds the optimal decision variable for maximizing or minimizing the objective function within a given constraint. Optimization is an excellent methodology in this paper’s problem for determining the amount of waste mixed and transported that maximizes profits under mixing conditions and preference for disposal components. This mathematical model can significantly help the circular economy by proposing recycling at a more reasonable level than landfill in waste recycling logistics, which does not yet have a systematic system. The remaining sections of this paper are as follows. Section 2 describes the data and the decision variables used in the optimization model. In Sect. 3, an optimization model is developed. The objective function of the optimization

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model is maximizing profits, and the model’s constraints are presented with the meaning of forming and reason. Section 4 shows the experimental settings and results of the optimization model. Finally, Sect. 5 summarizes the contribution of this paper and presents the future research directions of this paper.

2

Problem Description

This section presents a detailed explanation of the problem we solve in this paper. 2.1

Notations

We summarize the notation used in the mathematical model of this paper in Table 1. The four separate parts are a set used in the model, a parameter used in the discharge facility, a parameter used in the intermediate disposal facility, and a parameter used in the disposal facility, respectively. The data type means the type of the data element. The first part of the table describes the model’s set. Set I, J, and K refer to discharge, intermediate disposal, and disposal facilities. In the case of set L, this means the chemical elements used in the model, such as iron, aluminum, moisture, and various heavy metal elements. The disposal facility prefers wastes containing very little moisture, as high moisture concentration can complicate transportation and increase energy consumption. An intermediate disposal facility can fulfill this moisture reduction role, and waste always goes through a Table 1. Description of Sets and Parameters. Notation Data type Description I J K L O T D

Set Set Set Set Set Set Set

Set Set Set Set Set Set Set

of of of of of of of

discharge facilities intermediate disposal facilities disposal facilities chemical elements clusters in intermediate disposal facility total periods drying periods

ait eil a ¯ot e¯olt

Real Real Real Real

Discharged waste amount of discharge facility i at time t Discharged waste element l of discharge facility i Sum amount of the discharge facilities’ waste included in the cluster o by EC at time t Mixing element l of the discharge facilities’ waste included in the cluster o by EC at time t

tcaodt tceoldt EC

Real Real Real

Waste amount of temporary cluster o in dry time d at time t Waste element l of temporary cluster o in dry time d at time t Element criteria for cluster assignment

LBkl UBkl qk capak ck m

Real Real Real Real Real Real

Lower bound of element l that disposal facility k wants Upper bound of element l that disposal facility k wants Demand of disposal facility k during T Capacity of disposal facility k per day Unit total cost from intermediate disposal facility to disposal facility k Minimum waste amount of transportation

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drying process. So that moisture is removed from the chemical element set L. In addition, disposal facilities prefer low heavy metals content for higher product value and regulatory compliance. Because the market often wants waste with low heavy metals, it can be assumed that the waste we discuss in this paper is always low in heavy metals. The desired element ranges conditions of heavy metals are always satisfied, so the chemical element set L excludes heavy metals. Set O refers to the cluster set used by the intermediate disposal facility. This set continues to vary with market demand and supply. For example, iron is in high demand and abundantly available in various bulk wastes. So the cluster set includes a high iron cluster with very high iron content and a low iron cluster with slightly lower iron content. Valuable elements available in wastes, such as aluminum and zinc, are added to the cluster set. Set T means the time horizon. In the South Korean metal-bearing waste market, most contracts fix the time unit to one month, so T equals {1, 2, . . . , 30} in this model. The set of drying periods D fixes to seven days when most of the waste is dried. The waste dried for seven days is not assigned to a cluster transported to a disposal facility but to a temporary cluster. So D equals {1, 2, . . . , 7}. The second part of the table describes the data of the discharge facility. This paper uses quantitative information on the concentration of each element of waste discharged at each discharge facility. The third part of the table describes the data of the intermediate disposal facility. tcaodt and tceoldt refer to the amount and elements of the waste entering the intermediate disposal facility during the drying process. The cluster during the drying process is named a temporary cluster. The value of EC, specific element criteria, determines which cluster the waste is assigned to. We fix iron, aluminum, zinc, and copper as a cluster set in this model. EC is composed of dividing the high iron and low iron clusters, the aluminum cluster value, the zinc cluster value, and the copper cluster value. The fourth part of the table describes the disposal facility data. The disposal facility is a company that recycles specific chemical elements and desires a specific range of elements. LBlk and U Blk refer to the lower and upper bounds of elements the disposal facility desires. If there is no desired range of elements, the default values of LBlk and U Blk fix to 0 and 1, respectively, so they are always satisfied. q k means the minimum amount of waste a disposal facility receives over one month. capak is the amount of waste that can be disposed of per day, depending on the recycling capacity of the disposal facility. ck means the relevant total cost unit from the intermediate disposal facility to the disposal facility. The notation m is the minimum amount of waste once transported. Table 2 summarizes the decision variables used in the mathematical model of this paper. The problem is optimally deciding what to mix with the dried waste cluster and where to dispose of it. From the point of view of the intermediate disposal facility, this is determined in the most profitable direction. For this becomes the decision variable: the purpose, in the mathematical model, xok t amount sent to each cluster’s disposal facility. Waste can be sent from multiple clusters to the same disposal facility simultaneously. It can be assumed that the

Optimization of Mixable Product in the Korean Metal Waste

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Table 2. Description of Decision Variables. Notation Data type Description xok t

Real

zkt

Binary

1 if

caot

Real

Waste amount of cluster o at time t

ceolt

Real

Waste element l of cluster o at time t

Delivery amount from cluster o to disposal facility k at time t  ok o∈O xt > 0, 0 otherwise

waste from each cluster is mixed and sent, so it does not require a different condition for mixing. Other decision variables are related to xok t for modeling, and a detailed description of this is highlighted in Sect. 3. 2.2

Overview of the Framework

Figure 2 shows the framework of this model. The discharged waste from a discharge facility is transported to an intermediate disposal facility with amount and element information. According to the EC, the transported waste is classified into high iron, low iron, aluminum, zinc, copper, and other clusters. This waste belongs to a temporary cluster during drying time. After drying, the waste is mixed with previously remaining waste and assigned to a dry cluster. Because this waste has been dried, it is assumed that it can be immediately transported to a disposal facility. The intermediate disposal facility mixes dry clusters to satisfy the element ranges desired by the disposal facility and benefits by selling them to the disposal facility. In this paper, we maximize the profit to determine the decision variable: which waste from dry cluster to mix and where to send it. In Fig. 2, the red letters explain the corresponding decision variables. In addition, the blue letters briefly display the core data of each facility. We add some assumptions to create real problems so that this model can be helpful for the operation of an intermediate disposal facility. The complexity of

Fig. 2. The overall structure of the proposed model. It contains three facilities: discharge facility, intermediate disposal facility, disposal facility.

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the situation covered in this problem is very high. There are various stakeholders, such as discharge and intermediate disposal and disposal facilities. In other words, we should consider the entire market, not just the intermediate disposal facility. From the perspective of the entire market, we seek to determine the valuable waste. In addition, we maximize profits at once for the entire time without making greedy choices daily. Also, this model includes nonlinear constraints, a highly complex problem. Therefore, we assume a simple situation that can be extended to solve problems of various situations. Assumption 1. The contract with the discharge facility is fixed, and the intermediate disposal facility is single. This paper aims to optimize all waste flows in the intermediate disposal facility. However, we assume that the contract with a discharge facility is deterministic. The first reason is that optimizing simultaneous contracts with all discharge and disposal facilities increases the problem’s complexity. The second reason is that operating the intermediate disposal facility is possible even if this is solved sequentially. The value of waste at the discharge facility is determined through the optimal benefit in the problem of fixing the contract with the discharge facility. This value instructs which discharge facilities it is worth contracting with. That is, the problem of fixing the discharge facility is solved and used for operation sequentially. Therefore, the problem in this paper focuses on optimizing transportation to the disposal facility while assuming a fixed contract with the discharge facility. In addition, in the case of operating two or more intermediate disposal facilities, each intermediate disposal facility is independent, except for in cases in which the simultaneous disposing of waste exceeds the capacity of the disposal facility. So, except for such cases, the optimal of each intermediate disposal facility is globally optimal. This paper assumes a single intermediate disposal facility because the problem can be easily expanded by adding capacity constraints. Assumption 2. The cluster’s capacity is large enough to carry out the contract in this problem. This paper uses the metal waste of iron, aluminum, zinc, and copper, which account for a high proportion of metal waste in South Korea. In particular, the demand for iron is very high, so high and low iron clusters are used, depending on their iron content. It can be beneficial in profit if waste from all discharge facilities is stored separately, but this is impossible. Hence, cluster storage is essential by setting a certain number of clusters. This problem can be easily expanded by adding related data if we want to use other clusters. In addition, Assumption 1 assumes that the contract with the discharge facility is already deterministic, so the intermediate disposal facility contracts as far as they can store. Therefore, we assume the cluster’s capacity is large enough to be processed. Assumption 3. Total cost is proportional to the amount of waste.

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This paper considers transportation and disposal costs. Transportation cost is the cost of transporting waste from an intermediate disposal facility to a disposal facility, and disposal cost is the cost of recycling waste by the disposal facility. Transportation cost is proportional to the amount of waste because the distance between the intermediate disposal and disposal facilities is constant. Disposal cost is also proportional to the amount of waste if the waste is within the desired element ranges. Therefore, the total cost is proportional to the amount of waste because it only transports waste within the desired element ranges. In some cases, the disposal facility pays more for the waste in which a specific element content is high. These cases can be expressed by expanding the problem to subdivide the range and define the disposal costs for each range.

3

Mathematical Formulation

3.1

Main Model

This mathematical model defines constraints and the objective function at the intermediate disposal facility for a month and maximizes profits within the constraints. For convenience, T  is defined as {1, 2, . . . , |T | − 1}, and D is defined as {1, 2, . . . , |D| − 1}.

max





s.t.

 xok t

ck

o∈O t∈T

k∈K

caot+1





= caot + tcao|D|t −

xok t

∀ o ∈ O, t ∈ T 

(1)

∀ o ∈ O, l ∈ L, t ∈ T 

(2)

∀ o ∈ O, d ∈ D , t ∈ T 

(3)

k∈K

ceol,t+1

=

ceolt caot + tceol|D|t tcao|D|t caot + tcao|D|t

tcaod+1,t+1 = tcaodt tceol,d+1,t+1 ¯ot tcao1t = a o tcel1t = e¯olt tcaodt = 0 tceoldt = 0

=



∀ o ∈ O, l ∈ L, d ∈ D , t ∈ T

tceoldt

caot = 0 xok t

 t=1

a ¯ot ≥

(4)

∀ o ∈ O, t ∈ T ∀ o ∈ O, l ∈ L, t ∈ T ∀ d > t, o ∈ O, d ∈ D, t ∈ T

(5) (6) (7)

∀ d > t, o ∈ O, l ∈ L ∀ d ∈ D, t ∈ T

(8)

∀ o ∈ O, t ∈ D

(9)

∀ o ∈ O, k ∈ K, t ∈ D (10)

=0

|T |−|D|



 k∈K t∈T

xok t

∀ o ∈ O (11)

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S. Oh et al. |T |−|D|



|T |−|D|

≥

(12)

∀ k ∈ K, t ∈ T

(13)

∀ o ∈ O, k ∈ K, t ∈ T

(14)

xok t

∀ k ∈ K, l ∈ L

(15)

xok t

∀ k ∈ K, l ∈ L

(16)

k xok t ≥q

∀k∈K

(17)

k xok t ≤ capa

∀ k ∈ K, t ∈ T

(18)

∀ o ∈ O, t ∈ T

(19)

∀ o ∈ O, l ∈ L, t ∈ T

(20)

∀ o ∈ O, k ∈ K, t ∈ T

(21)

∀ k ∈ K, t ∈ T

(22)

t=1



t=1

M · ztk

k ceolt xok t ≥ LBl ·

o∈O



caot+|D|

k xok t ≥ m · zt

o∈O xok t ≤





∀o∈O

tcao1t

 o∈O

k ceolt xok t ≤ U Bl ·

o∈O





o∈O

o∈O t∈T



o∈O caot ≥

0≤

0

ceolt

≤1

xok t ≥0 k zt ∈ {0, 1}

The objective function is the total cost of a product of the unit total cost ck and the transported waste amount xok t . This model maximizes this objective function. Constraints (1) and (2) are the balance equations for the amount and element of the waste of the cluster. After all the waste flow occurring at time t, it becomes a waste state of time t + 1. Constraints (3) and (4) are the amount and element of waste in a temporary cluster during drying time. In the case of a temporary cluster, the state is the same as the previous state. Constraints (5) and (6) define the first time the waste enters a temporary cluster. At time t, the wastes discharged from the discharge facility enter the assigned cluster. Constraints (7)–(10) assume the initial setting of the problem. We set up the problem when the intermediate disposal facility starts the operations empty. Therefore, since waste can enter the temporary cluster from the first day, constraints (7) and (8) are that there is no waste in advance. In addition, constraints (9) and (10) mean no dried waste cluster during the first drying process. If it is an ongoing intermediate disposal facility operation, a temporary cluster and cluster that some waste already occupies can be written as initial setting data in constraints (7)–(10). Similarly, temporary cluster and cluster data can be put in repeated monthly operations. Constraints (11) and (12) are an equation to exclude impossible situations. Constraint (11) prohibits sending the waste to the disposal facility more than receiving it from the discharge facility. Constraint (12) prohibits the waste from entering the cluster more than entering the temporary cluster. If the model does not have these constraints, the model works inappropriately by infinitely increasing xok t , the decision variable, without violating the remaining constraints. Constraint (13) means the minimum waste amount of transporta-

Optimization of Mixable Product in the Korean Metal Waste

25

tion. In this problem, 100 kg is set to m, which can change according to the operation policy of the facility. (14) combines  If constraint intermediate disposal k ok is 0 if z is 0, and x is a positive number with constraint (13), o∈O xok t t o∈O t k ok k if zt is 1. So, they make the relationship between xt and zt . Constraints (15) and (16) mean the lower and upper bounds among the desired element ranges  in the disposal facility. In the case of constraint (15), since

ok ceo lt xt ok o∈O xt

o∈O 

LBlk .



is an

If o∈O xok element of the mixed waste, it is greater than or equal to t is  ok 0, nothing happens, and if o∈O xt is greater than 0, it can be multiplied, so it is possible to reform linearly for xok t , as shown in constraint (15). In addition to constraint (16), only the direction of the inequality sign is different, and the rest is the same as above. Constraint (17) is the demand for the disposal facility. South Korean metal-bearing waste disposal contracts often stipulate that more than a certain amount of waste that satisfies the range of elements must be sent for a month. If the model is required to satisfy other demands, such as more than a few times within a month, we add the constraint that Σt∈T ztk is more than the number of times contracted with disposal facility k. Constraint (18) means the capacity of the disposal facility per day. Constraints (19)–(22) mean the range of decision variables. caot and xok t are the amounts of waste, so they are nonnegative. ceolt is the waste element, so between 0 and 1. ztk indicates whether the cluster transports the waste to the disposal facility, so they are expressed as a binary variable. 3.2

Extended Model

In the model of Sect. 3.1, the EC is the parameter to find the solution for optimizing the operation of the intermediate disposal facility. However, the metal waste market frequently changes, such as changing the value of particular wastes due to the external environment and waste supplied by sources, so there is a need to set a reasonable EC. We develop an extended model by setting EC to the decision variable. If the model adds the EC constraint to the main model, the model should add the nonlinear expressions for the conditional statement. Therefore, we choose the extended model that solves the main model in a fixed EC after choosing EC among the many EC candidates.

4

Computational Experiment

The models are developed in FICO Xpress 8.12 and solved with Xpress Optimizer 38.01.04. Experiments are performed with an AMD Ryzen 7 2700X eight-core CPU processor at 3.70 GHz and 16GB of RAM running on a Windows 10 64-bit operating system. This paper uses reliable information such as public data from the Ministry of Government in South Korea [6] and quantitative analysis data obtained at a work site through X-ray fluorescence. In collaboration with a company that operates an intermediate disposal facility, we collect real data, make a database, and process the data for use in this model.

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We solve the 30-day-size problem, and the basic unit of the waste amount is a ton. When parties sign a contract in the South Korean waste market, the contract must specify how much waste will be handed over within a month. The intermediate disposal facility also decides the monthly amount of each contract for the discharge and disposal facilities. They will carry out the same waste treatment plan every month unless it is newly added or removed. Therefore, making an optimal plan for a month and using the optimal waste flow until the contract changes are reasonable. Also, because the waste transported is almost more than one ton, the basic unit of the waste amount is reasonable to assume to be a ton.

Fig. 3. Experimental results of the extended model

Figure 3 shows the solution of the extended model. This solution is sufficient because the extended model includes the main model. Among suitable EC candidates for the disposal data in this paper, EC with Fe of 0.5, Al of 0.3, and Zn of 0.1 is the most optimal, and the optimal objective function value is 12,366,600. The figure shows the flow of waste occurring at a specific time, the waste from the discharge facility is allocated to the cluster by the EC, and the element desired by the disposal facility is made and transported. Because of the nonlinear constraints, the model of this paper presents a local optimum using a heuristic. We present local optimality through two steps: successive linear programming (SLP) to approximately solve nonlinear optimization problems in Xpress and finding solutions belonging to the optimal gap using nonlinear heuristics. If the SLP exceeds 100,000 iterations, it takes too long to find

Optimization of Mixable Product in the Korean Metal Waste

27

a solution and cannot be used in operation. Therefore, it is set as unfinished and excluded from the results.

5

Conclusions

For the South Korean metal-bearing waste industry, this paper shows how to optimize the operation of an intermediate disposal facility for mixable metal wastes. We develop a mathematical model to find solutions within a reasonable time by considering the characteristics of the South Korean metal waste market. This model aims to offer the most profitable solution for the intermediate disposal facility. We discuss how to mix waste and which disposal facility be transported the dried waste cluster. Since the complexity of the problem situation is high, the model has been simplified so that the intermediate disposal facility can be operated with practicality by making multiple assumptions. In this model, we can effectively manage the operation of the intermediate disposal facility, and the value of the waste discharged from the discharge facility can be practically estimated. This information can present valuable operational insights, such as whether the current contracts are of worth and which facility should be chosen for the next contract. With value information, this model can help the circular economy by inducing high-value waste to be recycled rather than landfilled at a reasonable cost. The model presented in this paper has many extensions. It has the data’s scalability quantities, such as changing discharge and disposal facilities and the cluster set. Also, it has scalability for operational changes, such as with the initial conditions of the problem, the step function unit costs of disposal facilities, and the constraints on inventory levels. In addition, the model in this paper can be applied to any mixable product, so the logistics flow of other mixable products can be optimized. By developing the idea of this paper, future papers will be able to expand this problem in consideration of various positions such as discharge, disposal facilities, and transportation companies, as well as from the standpoint of intermediate treatment. For example, from each stakeholder’s perspective, the problem can consider several companies’ interests, such as a game-theoretic approach that maximizes each benefit or a supply chain contract that derives a win-win strategy through contracts. In addition, this paper’s problem solved the repeated waste logistics flow every month because there is a deterministic demand for each month under the contract. By extending it, considering random distribution, the problem can be approached from the perspective of robust optimization. Also, unlike this problem, if the waste logistics of the intermediate disposal facility are stored using other facilities, such as conveyors, other conditions can be added and modeled. Because of the high complexity of the model in this paper, we approximate the optimal solution using heuristics to present the solution in a reasonable time. Future studies can use the methods such as presenting another problem situation to reduce this complexity or linearizing the nonlinear constraint in this model.

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Acknowledgements. This research was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning [Grant Number NRF-2019R1A2C2084616] and the Technology development Program of MSS [Grant Number S3060734].

References 1. Anghinolfi, D., Paolucci, M., Robba, M., Taramasso, A.C.: A dynamic optimization model for solid waste recycling. Waste Manage. 33(2), 287–296 (2013) 2. Anwar, S., Elagroudy, S., Abdel Razik, M., Gaber, A., Bong, C.P.C., Ho, W.S.: Optimization of solid waste management in rural villages of developing countries. Clean Technol. Environ. Policy 20(3), 489–502 (2018) 3. Das, S., Bhattacharyya, B.K.: Optimization of municipal solid waste collection and transportation routes. Waste Manage. 43, 9–18 (2015) 4. Hoornweg, D., Bhada-Tata, P.: What a waste: a global review of solid waste management (2012) 5. Korea Legislation Research Institute (2021). Wastes control act 6. Ministry of Environment, Republic of Korea (2018). The first basic plan for resource circulation (2018–2027) 7. Qu, S., et al.: Implications of China’s foreign waste ban on the global circular economy. Resour. Conserv. Recycl. 144, 252–255 (2019) 8. Shinkuma, T., Managi, S.: License scheme: an optimal waste management policy under asymmetric information. J. Regul. Econ. 39, 143–168 (2011) 9. Wagner, J., Bilitewski, B.: The temporary storage of municipal solid wasteRecommendations for a safe operation of interim storage facilities. Waste Manage. 29(5), 1693–1701 (2009)

A Stochastic Frontier Analysis (SFA)-Based Method for Detecting Changes in Manufacturing Energy Efficiency by Sector and Time Ga Hyun Lee

and Hyun Woo Jeon(B)

Department of Industrial and Management Systems Engineering, Kyung Hee University, Yongin-Si, Gyeonggi-Do, Republic of Korea {leegh,hwjeon}@khu.ac.kr

Abstract. The manufacturing industry consumes a significant amount of energy, and therefore it is crucial to address energy efficiency issues in manufacturing. One way to study manufacturing energy efficiency is to investigate the changes in energy efficiency by sector and time. For that, the Industrial Assessment Center (IAC) database can be used since this dataset includes information needed to assess energy efficiency of the U.S. manufacturers by sector and time. Currently available efficiency analysis methods such as Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) do not fit well with the IAC database: the IAC provides only unbalanced panel data, but SFA and DEA are not mainly for analyzing unbalanced panel data. Therefore, we aim at developing a new approach based on SFA to use unbalanced panel data for energy efficiency analysis. Specifically, the whole manufacturing industry in the IAC database is classified into 20 sectors based on Standard Industrial Classification (SIC) codes. Then, we build each SFA model for each sector and evaluate energy inefficiency values for all manufacturers. From the SFA result, the average energy inefficiency values are calculated for comparative analysis of energy efficiency by sector and time. The final analysis results suggest that the annual energy efficiency was improved around 2010 in most manufacturing sectors, and manufacturing sectors can be classified into three groups (that is, increasing, maintaining, and decreasing) according to the changes of energy efficiency after 2016. We also calculate Spearman’s rank correlation coefficient for SFA models to check the consistency of the models. Keywords: Energy Efficiency · Manufacturing · Stochastic Frontier Analysis

1 Introduction Generally, energy consumption is measured and analyzed in four different areas: industrial, commercial, transportation, and residential sectors [1]. Among those four sectors, the industrial sector is responsible for the largest proportion of energy consumption at approximately 33%. Within the industrial sector, the manufacturing area accounts for about 81% of energy consumption, suggesting that energy consumption in manufacturing is very significant [1]. Therefore, investigating and improving the energy efficiency © IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 29–42, 2023. https://doi.org/10.1007/978-3-031-43688-8_3

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of the manufacturing industry is crucial for solving energy-related issues and plays a key role in addressing serious environmental problems worldwide. It is challenging to find the theoretical minimum of energy required for any production process. Therefore, energy efficiency in manufacturing needs to be evaluated through comparison between energy-consuming entities; in this study, energy efficiency/inefficiency is defined as decrease/increase in energy consumption for producing the same quantity of final products. Especially, the comparisons by manufacturing sector and time are crucial, since identifying the efficient or inefficient sectors and detecting changes in the efficiency over time can motivate follow-up studies to find reasons for the inefficiency and optimize energy consumption in manufacturing. To this end, the Industrial Assessment Centers (IAC) database by the U.S. Department of Energy (DOE) can be valuably used [2]. The IAC database provides information on energy consumption and production from energy consulting for U.S. manufacturing companies: the database includes types and amounts of energy, principal products, annual productions (quantity), factory area, and the number of employees by manufacturing sector and time. To compare the energy efficiency of each manufacturing sector, we can use Standard Industrial Classification (SIC) code to classify data from the IAC database. The SIC code indicates the company’s type of business, and the SIC range from 2000 to 3999 is used for manufacturing [3]. For all energy assessments conducted in the IAC, the SIC codes are available. Energy assessment year information is also available in the IAC database. One issue with the IAC database is that it generally provides only unbalanced panel data since energy consulting is conducted for different manufacturers over time. Unbalanced panel data offers observations from different entities over time while balanced panel data provides observations from the same entities over time [4]. For example, the energy costs of A company (2022 and 2023) and B company (2022 and 2023) constitute balanced panel data, and the energy costs of A company (2022) and B company (2023) are unbalanced panel data. Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) are common methods for analyzing efficiency. However, both approaches are not appropriate for comparing efficiency values from unbalanced panel data [5–8]. Generally, a DEA method for analyzing the unbalanced panel data is unavailable. In the case of SFA, it is possible to evaluate and compare the efficiency of each entity, but SFA cannot be directly used for unbalanced panel data such as the IAC database. In response to the lack of a suitable method, we suggest a new approach based on SFA to compare and analyze manufacturing energy efficiency by sector and time. More specifically, we classify the manufacturing sectors in the IAC database (unbalanced panel data) first and apply SFA to the IAC data classified by manufacturing sector. Then, we evaluate energy inefficiency values for each manufacturer, and average energy inefficiency values for each sector (SIC range) and time (year) are computed. As a result, we can obtain energy inefficiency values in the form of sector-time two dimensions data. Then, we can compare and evaluate the energy inefficiency (or efficiency) by the manufacturing sector and time. Our proposed approach is visually summarized in Fig. 1.

A Stochastic Frontier Analysis (SFA)-Based

31

Fig. 1. Summary of the proposed method

2 Literature Review Previous studies on energy efficiency evaluation have limitations in terms of scope and method. Most studies evaluate the energy efficiency of the individual company and do not pay attention to comparing the efficiency by the manufacturing sector and period. In particular, even though the IAC database provides important information on energy use in various manufacturing sectors (that is, SIC 2000–3999), previous studies could not take advantage of the IAC database to provide comparative studies according to sectors or periods. Some previous studies used the IAC database but mostly focused on investigating whether individual companies have implemented recommendations or on analyzing correlation among the manufacturers. Tonn and Martin divided an energy-efficiency decision-making process for individual companies into four stages and analyzed the development of each stage over time [9]. Abadie et al. analyzed the impact of financial factors and IAC evaluation recommendations on the energy efficiency investment decisions of companies [10]. Blass et al. analyzed the influence of the top management of each company on energy efficiency implementation using Ordinary Least Squares (OLS) and logistic regression models [11]. Both DEA and SFA are popular for efficiency analysis in economics [12, 13]. As the field of application has expanded, DEA and SFA have also been applied to analyze energy efficiency. Kumbahkar and Lovell explained SFA by dividing it into three different efficiency types: production efficiency, technical efficiency, and cost efficiency [13]. In particular, Boyd introduced SFA in energy efficiency analysis and used SFA to explain energy inefficiency as the difference between energy use and production function [14]. DEA is a non-parametric and non-statistical method. Due to these two characteristics, DEA is not appropriate for generalizing and comparing efficiency analysis results. Generally, when input/output relationships are analyzed, a specific production function parameter is estimated. However, non-parametric methods, such as DEA, analyze only sample data without assuming the shape of the production function (that is, the distribution of the population). In addition, non-statistical methods estimate production

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relationships only with the given sample data without statistical assumptions about the distribution of residuals. For these reasons, DEA is not an efficient method to compare efficiency between different samples. Contrary to DEA, SFA is a statistical and parametric model, enabling the comparison of efficiency results from different samples [15–17]. In addition, DEA generally uses cross-sectional data for inputs. Cross-sectional data are from different targets at different time points [18]. Although the Malmquist productivity growth index has been developed as a DEA method to use panel data, it can only be used with balanced panel data [17]. As the use of balanced panel data is also recommended for SFA, unbalanced panel data has not been comprehensively utilized with SFA [19]. Hence this study focuses on how to apply SFA to unbalanced panel data from the IAC database. Previous studies on energy efficiency with DEA or SFA have evaluated the energy efficiency of individual manufacturers using balanced panel data and compared the changes year by year. Oh and Hildreth evaluated the energy efficiency of the automobile manufacturing industry using DEA and SFA and analyzed the growth rate of efficiency over time: they used Spearman’s rank correlation coefficient to compare the two models to confirm the consistency of the results of the DEA and SFA analyses [5]. Na et al. evaluated the energy efficiency of China’s steel industry using SFA with balanced panel data and analyzed changes in energy efficiency patterns by year [6]. Kang et al. evaluated the impact of regional integration in China on regional energy efficiency using SFA with balanced panel data and additionally analyzed the external effects of government intervention [7]. Sarpong et al. evaluated the energy efficiency of African countries using panel data and a two-stage DEA and analyzed the cause of efficiency and inefficiency [8]. Some studies applied DEA and SFA to the IAC database to compare by manufacturing area but did not analyze the efficiency changes over time. Jeon et al. analyzed the energy efficiency of the manufacturing industry using SFA applied to IAC data [20]. Perroni et al. applied DEA, SFA, and Corrected Ordinary Least Squares (COLS) to the IAC database to evaluate the efficiency of each production area and compared the results [21]. In summary, previous studies using DEA have limitations in that the studies based on DEA are inappropriate to generalize or compare efficiency analysis. Also, studies using SFA or IAC database have not sufficiently considered extending the method for energy efficiency comparison based on unbalanced panel data. To address the aforementioned shortcomings of previous studies, we apply SFA to the IAC database and calculate average energy inefficiency values to compare the energy efficiency among the manufacturing sector and time. By analyzing the energy efficiency of the classified SIC sectors with the proposed approach, the inefficiency values of each manufacturing sector are evaluated. Then, we compare the annual average inefficiencies by sector and time. The rest of the paper is organized as follows: In Sect. 3, the IAC database and SFA models are introduced. In Sect. 4, the results of the energy efficiency analysis for each manufacturing sector and comparison results over time are presented. Section 5 then provides the conclusion and discusses the findings from the study.

A Stochastic Frontier Analysis (SFA)-Based

33

3 Method This section introduces the database used and the methodology we prroposed. 3.1 IAC Database As the research dataset, the IAC database is used. IAC is a free industrial assessment program provided by the DOE for small and medium-sized manufacturing companies. Currently, energy evaluation teams from 37 universities in the U.S. conduct energy evaluations of manufacturing companies and provide consulting services for energy savings and productivity improvement [2]. For our SFA models, a linear regression model needs to be built first. For constructing a linear regression model, i) energy usage, ii) employees, iii) plant area, iv) production hours, and v) energy cost are selected from the database. Energy usage is the dependent variable (Y ) calculated by integrating all different energy consumed into kWh. Then, the independent variables are employees, plant area, production hours, and energy cost. Observations with missing values are deleted, and, as a result, a total of 9,649 observations from 1992 to 2021 are used for the five variables. The manufacturing industry (SIC 2000 to 3999) is classified as shown in Table 1. The IAC database does not accumulate information on the same company for several years (mostly one-off), and therefore only ‘unbalanced panel data’ is available. As described in Sect. 2, most existing methods for energy efficiency analysis are only for balanced panel data. Hence the existing method used for balanced panel data needs to be improved to be applied to unbalanced panel data. 3.2 SFA-based Energy Efficiency Method for Unbalanced Panel Data In this study, an existing SFA model is applied first, and energy inefficiency values are calculated to deal with unbalanced panel data for energy efficiency analysis. Equation (1) shows the general regression model. For SFA, the error term in the regression Eq. (1) is divided into u and v as Eq. (2). Here, v is statistical random noise and follows a normal distribution as in Eq. (3). u represents technical inefficiency. u and v are independent of each other [20]. Generally, there are four different SFA models, depending on the distribution of u. The most commonly used models are the half-normal, exponential, truncated-normal, and gamma models as Eq. (4). n βi Xi +  (1) Y = i=1

 =v+u

(2)

v ∼ N (0, σ 2 )

(3)

    u ∼ N + 0, σ 2 or exp(λ)or N + μ, σ 2 or G(θ, P)

(4)

where a cumulative distribution function of G(θ, P) is

x 0

θ P exp(−θs)sP−1 ds. (P)

34

G. H. Lee and H. W. Jeon Table 1. Industrial sectors by SIC and the number of observations from the IAC database

Notation SIC-20 SIC-21 SIC-22 SIC-23 SIC-24 SIC-25 SIC-26 SIC-27 SIC-28 SIC-29 SIC-30 SIC-31 SIC-32 SIC-33 SIC-34 SIC-35 SIC-36 SIC-37 SIC-38 SIC-39

SIC Range 2000~2099 2100~2199 2200~2299 2300~2399 2400~2499 2500~2599 2600~2699 2700~2799 2800~2899 2900~2999 3000~3099 3100~3199 3200~3299 3300~3399 3400~3499 3500~3599 3600~3699 3700~3799 3800~3899 3900~3999

Industrial Sector (Products) Food and Kindred Products Tobacco Textile Mill Apparel and Other Textile Lumber and Wood Products Furniture and Fixtures Paper and Allied Products Printing and Publishing Chemicals and Allied Products Petroleum and Coal Products Rubber and Miscellaneous Plastics Products Leather and Leather Products Stone, Clay, and Glass Products Primary Metal Fabricated Metal Industrial Machinery and Equipment Electronic and Other Electric Equipment Transportation Equipment Instruments and Related Products Miscellaneous Manufacturing Industries

Total

Observations 1,228 13 297 144 439 262 493 327 566 91 1,020 49 350 602 1,233 1,012 553 550 260 160 9,649

The half-normal distribution model is a single-parametric model that uses only positive values, estimating the mean of the normal distribution as 0 and the variance as σ 2 . The half-normal model is the most popular SFA model for estimating inefficiency in SFA. The exponential distribution model is also a single-parametric model. Similar to the half-normal model, the exponential distribution model is likely to produce lesser inefficiency [19]. The truncated-normal distribution model is a multi-parametric model that uses only positive values, estimating the mean of the normal distribution as μ and the variance as σ 2 . The gamma distribution model is a multi-parametric model that estimates θ and P. The truncated-normal distribution model includes and generalizes the half-normal model, while the gamma model includes and generalizes the exponential model [13, 19, 22–25]. In this study, all four models are applied, and the consistency of the results is examined.

A Stochastic Frontier Analysis (SFA)-Based

35

After careful examination, the SFA model in Eq. (5) is selected as a final model to estimate energy inefficiency. In Eq. (5), Y is energy usage, X1 is employees, X2 is plant area, X3 is production hours, and X4 is energy cost. All variables are natural-log transformed. βn represents the coefficient of Xn . As defined in the introduction, the energy inefficiency u in this study is considered as energy usage resulting from the manufacturing process and facility utilization beyond the effects of the independent variables. If u values are larger, energy consumption is increased with less energy efficiency. We consider four SFA models in which v follows the normal distribution, and u follows one of the half-normal, the exponential, the truncated-normal, and the gamma distributions. ln(Y ) = β1 ln(X1 ) + β2 ln(X2 ) + β3 ln(X3 ) + β4 ln(X4 ) + u + v

(5)

Then, we present the SFA-based methodology to evaluate manufacturing energy efficiency using unbalanced panel data as in Fig. 1. First, we apply the SFA model defined by Eq. (5) to each of the 20 manufacturing sectors as in Table 1. Through the SFA model by each SIC sector, we obtain energy inefficiencies values (u) of every entity (manufacturer) within the SIC sectors. Then, these results with u values can be utilized for building sector-time two-dimensional u data. Then, the average u for each SIC sector is calculated to compare efficiency across sectors. Within each SIC sector, the values of u are calculated as an annual average, and the time series data of u for each sector are derived for further analysis.

4 Results We apply the same SFA model in Eq. (5) to 20 manufacturing sectors. SIC 2000 to 3999 are denoted by the first two digits of SIC codes for each manufacturing sector: for example, SIC-20 is for SIC 2000 to 2099. Accordingly, a total of 20 sector results are obtained. Among the estimated coefficients by the four models, the analysis results of the half-normal model are representatively presented in Table 2. Among all cases, model estimation for sectors SIC-20, SIC-21, SIC-31, and SIC-39 was not possible due to the residual skewness problem. Generally, the value of u and efficiency are inversely proportional in the SFA models, and the residuals should have positive skewness (a thicker right tail in a probability distribution function). However, the residuals of the four sectors had a negative skewness, and therefore estimating inefficiency was impossible. Table 3 shows the total averaged values of inefficiency u by manufacturing sectors. From the inefficiency comparison of each sector, SIC-24 is the most energy-inefficient manufacturing sector (highest average of u), and SIC-27 is the most energy-efficient manufacturing sector (lowest average of u). The time series figures of the average annual u estimations for each sector are presented in Fig. 2. Among the analysis results of the four models, only the results of the half-normal distribution model are representatively provided. In order to visualize the trend more intuitively with fewer fluctuations, the moving averages with a lag of 5 are used. The common characteristic of the time series of u throughout all manufacturing sectors is that the values of u are low around 2010, suggesting that manufacturing energy efficiency was high then.

36

G. H. Lee and H. W. Jeon Table 2. The estimated coefficients of the half-normal model Notation

Sector

SIC-22

Textile Mill

SIC-23

Apparel and Other Textile

SIC-24 SIC-25 SIC-26 SIC-27 SIC-28 SIC-29 SIC-30 SIC-32 SIC-33 SIC-34 SIC-35 SIC-36 SIC-37 SIC-38

Lumber and Wood Products Furniture and Fixtures Paper and Allied Products Printing and Publishing Chemicals and Allied Products Petroleum and Coal Products

Employees Plant Area

0.1268*** 0.0953*** 0.2067*** 0.9643*** -0.1155** 0.0717

*

0.0768

*

0.0057 0.1084 -0.0524

Primary Metal Fabricated Metal

***

*

-0.0595

Rubber and Miscellaneous Plastics Products 0.0686 Stone, Clay, and Glass Products

0.0022 0.1443 -0.0218

Electronic and Other Electric Equipment 0.1002

***

Instruments and Related Products

***

0.1044 0.1395

0.0017 0.1422

***

0.1529

***

0.1430

***

0.0395

*

0.2454

***

0.0216 ***

0.0938

Transportation Equipment

0.1687**

0.0506

-0.0231 ***

***

Industrial Machinery and Equipment

Production Energy Hours Cost

***

0.0247 0.1080

***

0.2272

***

0.2379

***

0.1673

***

0.3999

***

1.1181***

0.1382

***

1.0900***

0.1097

**

0.9819***

0.1177

***

1.0165***

0.3161

***

0.8256***

0.2693

***

1.0298***

0.4256

***

1.0055***

0.2291

***

0.8154***

0.1504

***

1.1164***

0.2555

***

0.9886***

0.1989

***

1.0055***

0.2030

*

0.8367***

0.2328

***

0.7978***

0.2446

***

0.8516***

0.3008

**

0.5587***

*** ** *

, , => Significance at 1%, 5%, 10% level

From Fig. 2, we can cluster all manufacturing sectors into several groups based on similar time series trends in energy inefficiency u. Specifically, in all manufacturing sectors, there exists a ‘big pit’ phase between 2008 and 2016 where inefficiency u fell and then rose. Depending on the behaviors of each time series, we classify SIC lines (u) into three groups: increasing, maintaining, and decreasing groups. The increasing group is for SIC lines with ‘u’ increasing after ‘big pit’, and the decreasing group is for SIC lines with ‘u’ decreasing after ‘big pit’. The maintaining group is for SIC lines with ‘u’ staying similar after ‘big pit’. In Table 4, SIC-22, 26, 27, 29, 32, 33, and 34 belong to the increasing group whereas SIC-23, 30, 35, 36, 37, and 38 can be assigned to the maintaining group. SIC-24, 25, and 28 are in the decreasing group (Figs. 3, 4 and 5).

A Stochastic Frontier Analysis (SFA)-Based Table 3. Averaged u (energy inefficiency) by the manufacturing sector Notation

Sector

Half -normal

Exponential

Truncated -normal

Gamma

Average

SIC-22

Textile Mill

0.4028

0.2448

−

0.2573

0.3017

SIC-23

Apparel and Other 0.5442 Textile

0.3425

0.3325

1.0678

0.5718

SIC-24

Lumber and Wood 0.7607 Products

0.6229

0.6857

0.7990

0.7171

SIC-25

Furniture and Fixtures

0.4889

0.3285

0.3342

0.6382

0.4474

SIC-26

Paper and Allied Products

0.3294

0.2325

0.2332

0.2502

0.2613

SIC-27

Printing and Publishing

0.3599

0.2268

0.2374

0.2179

0.2605

SIC-28

Chemicals and Allied Products

0.4257

0.2803

0.2800

0.2548

0.3102

SIC-29

Petroleum and Coal Products

0.5655

0.3091

−

0.1982

0.3576

SIC-30

Rubber and Miscellaneous Plastics Products

0.3931

0.2619

0.2883

0.2514

0.2987

SIC-32

Stone, Clay, and Glass Products

0.5094

0.3076

0.9924

0.3777

0.5468

SIC-33

Primary Metal

0.4186

0.2559

0.2544

0.1874

0.2791

SIC-34

Fabricated Metal

0.4091

0.2585

0.2591

0.3020

0.3072

SIC-35

Industrial Machinery and Equipment

0.4135

0.2731

0.2741

0.2511

0.3030

SIC-36

Electronic and Other Electric Equipment

0.4206

0.2703

0.6997

0.2457

0.4091

SIC-37

Transportation Equipment

0.4377

0.2719

−

0.4233

0.3776

SIC-38

Instruments and Related Products

0.5856

0.4249

0.3952

0.3130

0.4297

37

38

G. H. Lee and H. W. Jeon

Fig. 2. u of each sector estimated by the half-normal model

Table 4. Clustering SIC sectors Criteria: The time series trend of inefficiency u after ‘big pit’ around 2008–2016 Increasing Group

SIC-22, 26, 27, 29, 32, 33, 34

Maintaining Group

SIC-23, 30, 35, 36, 37, 38

Decreasing Group

SIC-24, 25, 28

Fig. 3. u of the increasing group estimated by the half-normal model

A Stochastic Frontier Analysis (SFA)-Based

39

Fig. 4. u of the maintaining group estimated by the half-normal mode

Fig. 5. u of the decreasing group estimated by the half-normal model

Among the four SFA models, most models show consistent estimation results. To quantitatively verify this, we compute and evaluate Spearman’s rank correlation coefficients of the annual averages of u from the four SFA models. Spearman’s rank correlation coefficient was introduced by Spearman in 1904 [26]. The coefficient calculates the correlation by converting the value of each variable into a rank and has a value between –1 and 1: the closer the coefficient is to −1 (or 1), the stronger the negative (or positive) correlation, respectively. In Table 5, H is the half-normal model, E is the exponential model, T is the truncatednormal model, and G is the gamma model. By excluding SIC-37, all sectors have a high correlation among the results from the four SFA models. The range of correlation coefficients is between 0.846 to 1.000, indicating a very strong positive correlation. This observation suggests that although there may be differences in inefficiency estimated by each SFA model, consistent results have been obtained from this study.

40

G. H. Lee and H. W. Jeon Table 5. Spearman’s rank correlation coefficient between distributions

SIC-22 H E T G SIC-24 H E T G

H 1.000 0.977* 0.958* H 1.000 0.914* 0.968* 0.949*

E 1.000 0.982* E 1.000 0.981* 0.989*

SIC-26 H E T G

H 1.000 0.981* 0.981* 0.968*

SIC-28 H E T G

T

T 1.000 0.993*

G 1.000 G 1.000

SIC-23 H E T G SIC-25 H E T G

H 1.000 0.986* 0.988* 0.959* H 1.000 0.988* 0.989* 0.989*

E 1.000 1.000* 0.957* E 1.000 1.000* 0.980*

T 1.000 0.959* T 1.000 0.981*

G 1.000 G 1.000

E 1.000 1.000* 0.984*

T 1.000 0.984*

G 1.000

SIC-27 H E T G

H 1.000 0.998* 0.999* 0.988*

E 1.000 1.000* 0.988*

T 1.000 0.987*

G 1.000

H 1.000 0.982* 0.982* 0.966*

E 1.000 1.000* 0.990*

T 1.000 0.990*

G 1.000

SIC-29 H E T G

H 1.000 0.990* 0.949*

E 1.000 0.978*

T -

G 1.000

SIC-30 H E T G

H 1.000 0.923* 0.984* 0.968*

E 1.000 0.996* 0.993*

T 1.000 0.993*

G 1.000

SIC-32 H E T G

H 1.000 0.969* 0.970* 0.975*

E 1.000 0.894* 0.988*

T 1.000 0.913*

G 1.000

SIC-33 H E T G SIC-35 H E T G

H 1.000 0.976* 0.989* 0.966* H 1.000 0.997* 0.997* 0.990*

E 1.000 0.995* 0.990* E 1.000 1.000* 0.992*

T 1.000 0.983* T 1.000 0.992*

G 1.000 G 1.000

SIC-34 H E T G SIC-36 H E T G

H 1.000 0.995* 0.996* 0.992* H 1.000 0.980* 0.930* 0.963*

E 1.000 0.999* 0.994* E 1.000 0.863* 0.981*

T 1.000 0.996* T 1.000 0.846*

G 1.000 G 1.000

SIC-37 H E T G

H 1.000 0.980* -0.268 0.985*

E 1.000 -0.247 0.982*

T 1.000 -0.268

G 1.000

SIC-38 H E T G

H 1.000 0.826* 0.934* 0.745*

E 1.000 0.924* 0.962*

T 1.000 0.885*

G 1.000

-

* => Significance at 1% level

A Stochastic Frontier Analysis (SFA)-Based

41

5 Conclusion Manufacturing energy efficiency needs to be compared and studied to help mitigate the impact of energy consumption since the manufacturing industry accounts for a large proportion of total energy consumption. For that, a new approach for comparing energy efficiency by sector and time is proposed. Especially, SFA is applied for analyzing unbalanced panel data to utilize the IAC database, and sector-time two-dimensional energy inefficiency results are obtained for further analysis. By classifying the manufacturing industry of the IAC database into 20 sectors and by applying four SFA models, we can analyze the most efficient/inefficient manufacturing sectors and detect the temporal changes in the energy efficiency of each sector. The findings of this study indicate some improvements in energy efficiency within the manufacturing sectors around 2010. Furthermore, based on the trend of change in the time series of inefficiency u, all manufacturing sector has been categorized into three distinct groups. Through this classification, we identified sectors that have the potential to improve energy efficiency as those sectors showed energy efficiency worsened recently. Furthermore, the efficiency estimation results of the four SFA models are validated to be consistent. This study can motivate follow-up studies on detecting and improving energy efficiency in the manufacturing industry. Also, the results of energy efficiency evaluation by the manufacturing sector can help manufacturers and researchers establish energy efficiency benchmarking strategies. In addition, the results of the energy efficiency analysis over time can be used to predict and prepare for future energy efficiency changes. In this study, the detailed differences between the four SFA models were not analyzed. This limitation can be addressed in a future study. Acknowledgments. This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.RS2022–00155911, Artificial Intelligence Convergence Innovation Human Resources Development (Kyung Hee University)).

References 1. 2. 3. 4. 5.

U.S. Energy Information Administration. https://www.eia.gov/. Accessed 02 Apr 2023 Industrial Assessment Center. https://iac.university/. Accessed 02 Apr 2023 U.S. Securities and Exchange Commission. https://www.sec.gov/. Accessed 08 Apr 2023 Sayrs, L. W.: Pooled Time Series Analysis. Sage (1989) Oh, S.C., Hildreth, A.J.: Estimating the technical improvement of energy efficiency in the automotive industry-stochastic and deterministic frontier benchmarking approaches. Energies 7(9), 6196–6222 (2014) 6. Na, H.M., et al.: Review of evaluation methodologies and influencing factors for energy efficiency of the iron and steel industry. Int. J. Energy Res. 43(11), 5659–5677 (2019) 7. Kang, J., Yu, C., Xue, R., Yang, D., Shan, Y.: Can regional integration narrow city-level energy efficiency gap in China? Energy Policy 163, 112820 (2022) 8. Sarpong, F.A., Wang, J., Cobbinah, B.B., Makwetta, J.J., Chen, J.: The drivers of energy efficiency improvement among nine selected West African countries: A two-stage DEA methodology. Energ. Strat. Rev. 43, 100910 (2022)

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9. Tonn, B., Martin, M.: Industrial energy efficiency decision making. Energy Policy 28(12), 831–843 (2000) 10. Abadie, L.M., Ortiz, R.A., Galarraga, I.: Determinants of energy efficiency investments in the US. Energy Policy 45, 551–566 (2012) 11. Blass, V., Corbett, C.J., Delmas, M.A., Muthulingam, S.: Top management and the adoption of energy efficiency practices: evidence from small and medium-sized manufacturing firms in the US. Energy 65, 560–571 (2014) 12. Cooper, W.W., Seiford, L.M., Zhu, J.: Handbook on Data Envelopment Analysis. Spring-er Science & Business Media (2011) 13. Kumbhakar, S.C., Lovell, C.A.: Stochastic Frontier Analysis. Cambridge University Press, Cambridge (2014) 14. Boyd, G.A.: Estimating plant level energy efficiency with a stochastic frontier. Energy J. 29(2), 23–43 (2008) 15. Aigner, D., Lovell, C.A.K., Schmidt, P.: Formulation and estimation of stochastic frontier production function models. J. Econometrics 6(1), 21–37 (1977) 16. Lee, J. D., and Oh, D. H.: Theory of Efficiency Analysis-Data Envelopment Analysis. IB Book, Seoul (2012) 17. Hu, J.-L., Honma, S.: A comparative study of energy efficiency of OECD countries: an application of the stochastic frontier analysis. Energy Procedia 61, 2280–2283 (2014) 18. Beerenwinkel, N., et al.: Learning multiple evolutionary pathways from cross-sectional data. In: Proceedings of the Eighth Annual International Conference on Research in Computational Molecular Biology, pp. 36−44 (2004) 19. Kumbhakar, S., Parameter, C. F., Zelenyuk, V.: Stochastic Frontier Analysis: Foundations and Advances, CEPA Working Papers Series from University of Queensland, School of Economics (2018) 20. Jeon, H.W., Taisch, M., Prabhu, V.V.: Modelling and analysis of energy footprint of manufacturing systems. Int. J. Product Res. 53(23), 7049−7059 (2015) 21. Perroni, M. G., Gouvea da Costa, S. E., Pinheiro de Lima, E., Vieira da Silva, W.: The relationship between enterprise efficiency in resource use and energy efficiency practices adoption. Int. J Product. Econ. 190, 108–119 (2017) 22. Boyd, G., and Doolin, M.: The Energy Efficiency Gap and Energy Price Responsiveness in Food Processing. US Census Bureau, Center for Economic Studies (2020) 23. Greene, W.H.: A Gamma-distributed stochastic frontier model. J. Econometrics 46(1), 141– 163 (1990) 24. Greene, W.: LIMDEP: Econometric Modeling Guide, version 11 (2016) 25. Wang, H.J.: Stochastic Frontier Models. University Library of Munich, Germany, MPRA Paper (2006) 26. Spearman, C.: The proof and measurement of association between two things. Am. J. Psychol. 15, 72–101 (1904)

Analyzing Emerging Circular Economy Business Models in the E-waste Sector Through the Business Model Canvas W. Tirufat Dejene(B)

, Moreno Muffatto, and Francesco Ferrati

School of Entrepreneurship, Department of Industrial Engineering, University of Padova, Padova, Italy [email protected], {moreno.muffatto, francesco.ferrati}@unipd.it

Abstract. Annual e-waste (waste electrical and electronic equipment) generation globally is increasing, resulting in a significant waste stream because of its large quantity, potential negative impact on health and the environment, and the valuable materials it contains. In managing e-waste, minimizing pollution, and maximizing product value, the circular economy (CE) becomes an ideal solution. However, despite the potential environmental, social, and economic benefits the circular economy approach can bring to the WEEE industry, knowledge and implementation remain limited. There is a lack of understanding of business opportunities related to alternative end-of-life options, which hinders interested parties from implementing circular strategies. To address these gaps, our study identifies and analyzes the business model (BM) of young companies operating in the WEEE industry. An exploratory research design with an inductive approach was employed, and we collected data from 412 companies selected from the Crunchbase database. Using the business model canvas, we examine these business models in four key dimensions. These are value propositions (products and services offered), value delivery (delivery processes and customer segments), value creation (creation processes and circular operation forms), and value capture (revenue streams). Through the collection and analysis of data pertaining to companies in this industry, significant insights have emerged. Our findings show that about 50% of these companies engage in IT asset disposition (ITAD). Among the target customers of these ITAD companies, an overwhelming majority of 78% focus on serving the B2B market and government agencies. These companies specialize in office equipment and networking devices. However, 27% of the analyzed businesses specialize in trade-in, buyback, and reselling pre-owned electrical devices. These companies serve both B2B and B2C markets. The findings highlight a concerning trend: despite the alarming increase in global e-waste generation caused by the rising demand for high-tech products and their decreasing service life, the practice of reusing these products, especially from individual customers, is not adequately observed among young companies operating in the WEEE sector.

© IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 43–57, 2023. https://doi.org/10.1007/978-3-031-43688-8_4

44

W. T. Dejene et al. Keywords: waste electrical and electronic equipment · business models · circular economy · value propositions · value creation · value delivery · value capture

1 Introduction The sector of Electrical and Electronic Equipment (EEE) generates electronic waste (e-waste) with hazardous substances (such as lead and cadmium) as well as valuable materials such as precious metals and critical raw materials [1]. According to Cucchiella et al. (2015), e-waste is increasing at a rapid pace of 3–5% per year, making it one of the fastest-growing types of waste [2]. E-waste is regarded as a top-priority waste stream for a variety of reasons, including its large volume, potential negative impact on health and the environment, resource depletion, ethical concerns, and inadequate end-of-life management [3–5]. While e-waste production is on the rise, recycling rates are still low, with just 17.4% of electronic waste being sorted and recycled appropriately. In 2019, e-waste production reached 53.6 million metric tons, projected to rise to 74.7 million metric tons by 2030 [6]. These figures emphasize the significance of developing effective management strategies for e-waste disposal. The circular economy (CE) model presents a sustainable and viable alternative to the unsustainable linear economy model, which prioritizes resource recovery in a closedloop system. The circular economy aims to reduce resource depletion and promote economic benefits by prioritizing reuse and re-manufacturing over recycling for improved resource recovery. Applying circular economy strategies to the WEEE industry can promote sustainable development in both the environment and the economy by closing the product life cycle, minimizing pollutant emissions, and maximizing product utility and value. However, implementing a circular economy in the WEEE industry remains challenging for companies, organizations, and governments. Literature shows that end-of-life practices in WEEE focus on recycling, and the percentage of valuable resources recovered is low. Although circular economy business models (CEBMs), such as remanufacturing and product-as-a-service systems, can provide significant economic and environmental benefits, their adoption in industries is not widespread. Therefore, with the projected increase in global e-waste generation to 74.7 Mt by 2030 [6], there is a need for studies focusing on a sector-specific approach to CE practices implementation within the WEEE industry [11, 12]. Business model (BM) innovation is critical to changing the way businesses conduct business as they move towards a circular economy [13], so companies need to reconsider their value creation, proposition, delivery, and capture [14, 15]. Considering that young companies’ business models are more open to change and improvement, this study examines how circular economy practices are incorporated into the business models of these WEEE companies. The study uses business model dimensions of value creation, value proposition, value delivery, and value capture [16, 17] to analyze the companies. The study focuses on the “6R” (reuse, resale, repair, refurbish, remanufacture, and recycle [18]) circular economy strategies to examine the business models of young companies operating in the WEEE industry (Fig. 1). This work aims to contribute to the CEBM

Analyzing Emerging Circular Economy Business Models

45

literature by addressing the following research question. What are the key business model characteristics of young companies operating in the waste electrical and electronic equipment sector? Based on this research question, our study examines and analyzes the emerging circular economy business models within the WEEE sector by identifying the dimensions and characteristics of their business models. This includes investigating the types of end-of-life products primarily processed by these companies and the specific services they offer. Exploring the target customers to whom these companies offer their value propositions and the customer channels they use. Identifying the key value-creation processes and circular operating forms employed by these companies to realize their value propositions for their targeted customer groups. This study also analyzes these companies’ revenue streams. This study uses inductive approaches to identify and analyze the BM characteristics of young companies in the WEEE sector and presents the results through a business model canvas. We organize the remaining part of this paper into different sections. Section 2 presents the background of the research and Sect. 3 presents the research method for identifying and analyzing business models. Section 4 provides the study’s results and Sect. 5 provides discussion and conclusions.

Fig. 1. Circular economy approach and scope of the study

2 Background: Circular Economy in the Waste Electrical and Electronic Equipment Sector The linear business production model is resource intensive and has led to the emergence of the circular economy model, which prioritizes economic benefits, environmental quality, and social equity [19]. The circular economy focuses on reuse, recycling, and recovery to reduce resource depletion, increase economic benefits, and extend the product lifecycle. Individual organizations need to adopt circular economy business models for the transition from a linear economy to a circular economy [20]. The goal of circular

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economy business models (CEBMs), as described by Lahi et al. (2018), is to minimize resource usage, increase durability, and maximize value retention. Circular economy business models strive to keep products, components, and materials at their maximum value throughout their life cycle [21]. These business models have been widely proposed for electronic product lifecycle management [7] showing that the WEEE sector could benefit from CEBM adoption. In 2003, research on the WEEE industry began within the context of the circular economy to reduce e-waste disposal and prevent further e-waste production [8]. The circular economy adopts a range of circular economy strategies for managing EEE at the end of its lifespan that aim to conserve resources. These strategies include high-level strategies such as resale, reuse, repair, refurbishment, and re-manufacturing, and low-level strategies such as repurposing and recycling [21, 23]. These models aim to reduce resource usage, prolong product lifespan, and maximize value capture [21] by maintaining products, components, and materials at their peak value throughout their lifecycle [21] and recovering raw materials from waste [23]. High-level strategies are more resource efficient and offer more environmental savings than low-level strategies, although they are often overlooked. The WEEE Directive [9] and the Circular Economy package [10] promise to support a holistic approach to WEEE management in the future. However, there is still a lack of substantial insight and evidence into the business potential for alternative end-of-life options based on reuse, repair, and re-manufacturing. This lack of insight keeps stakeholders from implementing circular strategies [22]. The study aims to provide a comprehensive view of the development of circular economy practices within the WEEE industry. It examines emerging companies’ business models and “6R” circular economy strategies. To analyze and understand CEBMs, the Business Model Canvas [17] and the core functions of a business model - value proposition, value creation, value delivery, and value capture - are often used in the literature. This helps to examine various components of the business model building blocks and how they align with the principles of the circular economy [17, 24]. In the traditional business model, authors (e.g., Richardson 2008, Osterwalder and Pigneur 2010, and Frankenberger et al. 2013) describe the business model components in different ways. This study adapted the four core components of a business model stated by Richardson (2008) and Osterwalder and Pigneur (2010) to present the value analysis of each business model in the circular economy.

Value Creation Value creation Process Circular Operating Form

Value Proposition

Value Delivery

Products

Customer Segments

Services

Value Capture

Channel

Fig. 2. Business model framework (adapted from [17, 20, 24])

Revenue Stream

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Based on our literature review, we found a significant gap in studies that analyze the business models of WEEE companies. This scarcity suggests an opportunity for further research and exploration in this area. Despite the limited number of studies, we identified 15 relevant articles that shed light on different aspects of business models within the WEEE sector. These articles offer valuable insights and observations into the various business model dimensions employed by companies operating in this field. To present a comprehensive overview of these dimensions and facilitate a better understanding of the existing literature, we meticulously organized and summarized the key findings in Table 1. Table 1. Comparison table of litterateurs in business models of the WEEE sector Source

Product

Service

Target Customer

Value Delivery Process

Value Creation Process

Lee, S. J., et al. (2020)

x

x

x

x

x

Zheng, X., & Xu, F. (2016)

x

x

x

x

x

Zuo, L., et al. (2020)

x

x

x

x

Shevchenko, T., et al. (2021)

x

x

x x

Magrini, C., et al. (2021) Kissling, R., et al. (2012)

x

x

x

Kim, C. H., et al. (2022)

x

x

x

Deng, W. J., et al. (2017)

x

x

Ongondo, F. & Williams, D. (2011)

x

x

Phoochinda, W., & Kriyapak, S. (2021)

Circular Operation Form

Revenue Mechanism

x

x

x

x

x

x

x

x

x

x

x

x

x

H. Chesbrough and R.S Rosenbloom (2002)

x

Xin T., et al. (2018)

x

x x

x

x

Coughlan, D., & Fitzpatrick, C. (2020)

x

x x

x

x

x

x x

x x

x

x

x

Król, A., et al. (2016)

x

x

x

Sun, Q., et al. (2018)

x

x

x

x

x x x

x

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3 Research Methodology We employed a literature review and exploratory research design with an inductive approach to achieve the study goal. We used various combinations of keywords related to the concepts of circular economy, business models, and WEEE to identify relevant articles in the area. The resulting search string: (“circular economy” OR “closed$loop” OR “reverse logistic*” OR “recycl*” OR “end of life” OR “reus*” OR “refurbish*” OR “Repair” OR “maintain*” OR “recover*”) and (“business*” OR “business model*” OR “value proposition*” OR “value creation*” OR “value delivery” OR “value capture”) and (“Ewaste*” OR “electronic-waste*” OR “electr* waste*” OR “electr* scrap*” OR “electr* garbage” OR “electr* rubbish” OR “WEEE” OR “waste* electr*” OR “obsolete electr*”) was used to identify papers that focus on the topic and resulted with 15 articles. The study used the descriptive framework for business models defined by Osterwalder et al. (2005) [14], as well as frameworks applied to circular business models by Lüdeke-Freund et al. [24] and Lewandowski [20]. This study adapted these frameworks and used them to explore the business models of selected case companies (Fig. 2). The four categories of the framework employed to categorize business models include “value propositions,” which refer to the products and services offered to customers; “value creation,” which corresponds to the configuration of the value creation processes and operating forms to create and deliver the value propositions; “value delivery” corresponds to the customer segments that a company wants to provide value to and the way a company uses to get in touch with its customers; and “value capture,” which refers to the way a company generates revenue through a variety of streams [17, 20, 24]. Circular economy business models aim to put circular economy principles into practice and help organizations create, deliver, and capture value [25, 26]. According to this study, “value propositions” refer to the products and services offered by WEEE companies to their customers (for example, in the trade-in or sale of EEE). The “value delivery” dimension concentrated on the segments of customers served and analyzed how the company reaches out to its customers. Meanwhile, the “value creation” dimension investigated the circular operating forms the company uses to create value. And, the revenue streams of these business models focus on how this business makes money. Based on these dimensions, we develop an analytical framework to analyze and categorize young companies operating in the WEEE sector.

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Fig. 3. A framework for analyzing case studies using analytical methods.

Figure 3 provides a detailed overview of these categories, and Sect. 4 explains the five circular business models. This study conducts an extensive literature review to examine circular business models in the WEEE sector. To present a concise overview, we generate a comparison table of relevant literature on business models in the WEEE sector (Table 1). We adopt an inductive approach to examine and analyze WEEE circular business models. We used secondary data from company websites and Crunchbase profiles. Crunchbase, known for its extensive coverage of high-tech companies worldwide, served as the ideal source, providing information on technology-based startups and firms across various high-tech industries. We used “sustainability” keywords and then filtered the category list by “recycling.” A dataset consisting of 412 active WEEE young companies was selected. Extracting data from each company’s website and examining similarities and differences in the analytical framework dimensions, the study identified circular business models employed by these young companies within the WEEE sector.

4 Results Our analysis of the 412 young companies’ business models operating in the waste electrical and electronic equipment sector revealed five end-of-life EEE business models. These business options include: - (1) the IT asset disposition model, (2) the trade-in/buyback & re-commerce model, (3) the e-waste recycling model, (4) the technical support model, and (5) the reverse logistics model. We discuss each model in the next section and also describe each model based on the analytical framework presented in Tables 2, 3, 4, 5 and 6.

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4.1 IT Asset Disposition Model (ITAD) The majority of companies (50.7% of the analyzed firms) are involved in IT asset disposition (ITAD). This model refers to a set of practices and processes for disposing of electronic equipment and data in a secure, compliant, and environmentally friendly manner. This is significant because electronic equipment often contains sensitive data that needs to be carefully handled for privacy and security concerns. Electronic equipment can also have harmful components that damage the environment if not taken care of responsibly. The ITAD model involves several activities that include surplus asset remarketing, asset refurbishment and remarketing, data center decommissioning, asset liquidation, reverse logistics, asset tracking and reporting, secure data destruction, redeployment, environmentally responsible disposal, and recycling. These activities help ensure electronic equipment is properly disposed of while maximizing its value through resale or recycling. The ITAD model is relevant to a wide range of customers, including large corporations, small and medium-sized businesses, government agencies, data centers, non-profit organizations, distributors, and resellers. Most companies generate revenue from asset disposition and data destruction services. In addition, they sell refurbished products, and in some cases, recovered components and parts for recycling (Table 2). 4.2 Trade-In/Buyback and Re-Commerce Model The trade-in/buyback and re-commerce companies (27%) in this study offer individuals and businesses a convenient way to sell their used electronic devices for cash or credit for purchasing another device. In addition, these models offer a cost-effective way for customers to buy used electronic products at a lower price through their e-commerce platforms or third-party online marketplaces. Our analysis shows that this model involves several key activities such as the valuation process to assess the condition, age, and other factors of the device to decide its value, cleaning, and data sanitation processes to remove any personal data or sensitive information from the device, refurbishing or upgrading processes and once the device has been refurbished or upgraded, it can be sold as a refurbished device with a warranty to offer customers an extra sense of security. Under this model, most companies generate revenue by selling second-hand products at a lower price than brand-new ones (Table 3).

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Table 2. IT Asset Disposition Model Dimensions

Sub-dimensions

Descriptions

Value Creation

Value creation process

- Data wiping, degaussing, and physical destruction, Surplus asset remarketing, refurbishment and remarketing, data center decommissioning, asset liquidation, collection and transportation, material recovery

Circular Operating form

- 87% Extend the product life cycle - Resource Recovery

Products

- 88% concentrates on IT & Telecommunication and Consumer equipment (Desktop computers, Laptops/Notebooks, Tablets, Servers, Networking equipment, Television, and Camera)

Services

- Fast, easy, efficient ITAD and remarketing services - 63.15% Fast, easy, efficient ITAD service with partners for EOL management, 36.84% ITAD with EOL - Remarketing services: maximum return on retired assets - Secure data and physical destruction onsite/offsite - Clean, safe, & secure removal of redundant IT assets - Convenient and secure logistics (pickup options) - Sustainable disposal of assets in compliance with environmental and data security regulations - Chain of custody reporting and certification for all services

Target Customers

- 78% small & medium-sized businesses and corporations, Government agencies, Non-profit organizations, Distributors, and resellers

Channels

- 72.2% of the companies deliver service onsite - Customer support through phone, email, or chat or website and social media platforms; - Sales channel: direct wholesale sales; Partnerships; Online Marketplace

Revenue Stream

- 88.5% Sale of services, Sale of products components, or materials, Leasing/Subscription Fee

Value Propositions

Value Delivery

Value Capture

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W. T. Dejene et al. Table 3. Trade-in/ Buyback and Re-commerce Model (27%)

Dimensions Sub-dimensions Value Creation

Descriptions

Value creation process - Trade-in/Buyback, Cleaning, and data sanitation, repairing and refurbishing, Selling pre-owned electronic devices Circular Operating form

- Focus on extending the product life cycle

Value Products Propositions

- 62.1% Only IT and telecommunication (equipment Refurbished Smartphones, Tablets, Laptops/Notebooks, smartwatches) - 30% IT and telecommunication with consumer electronics

Services

- 39.6% Hassle-free way for customers to sell their used devices or Trade-in/Buyback with a competitive price - 72.9% Sell affordable devices with a warranty and Wholesale distribution service - Data wipe to ensure that all personal data is securely erased - Shipping and after-selling services

Customer Segment

- Individuals and business

Channels

- 66.2% of the companies deliver service through e-commerce platforms and third-party online marketplace, Retail store - Customer support through Mobile APP, Website, and social media platforms - Customer support through phone, email, or chat; - Sales channel: direct wholesale sales; Online Marketplace, and collection through Mail-in or courier

Revenue Stream

- 72.9% of revenue from sales of refurbished devices - Commission from online marketplace

Value Delivery

Value Capture

4.3 Electronics Recycling Model Companies under the electronic recycling model (22.3%) involve the responsible and environmentally friendly disposal of electronic devices. These services typically involve several activities, such as pickup and drop-off services, de-manufacturing, part harvesting, shredding, crushing, and sorting of materials and disposing of them safely and responsibly. These companies also offer data destruction services to ensure that any sensitive information stored on electronic devices is completely wiped and destroyed before disposal. To ensure responsible disposal, electronic recycling companies provide certificates of destruction and recycling. The main customers of electronic recycling models include large corporations, small businesses, organizations, governments and municipalities, and households. These services generate revenue primarily from electronic

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recycling and data destruction services, and the sale of recovered valuable materials. Some companies also refurbish and sell devices that can be reused (Table 4). Table 4. Electronic Recycling Model (22.3%) Dimensions

Sub-dimensions

Descriptions

Value Creation Value creation process - E-waste collection, transportation, recycling, and disposal - Data destruction through shredding and crushing, reselling of harvested parts and secondary raw materials

Value Propositions

Circular Operating form

- Resource Recovery

Products

- IT and telecommunication, consumer equipment and PV, small household appliance, and large household appliances

Services

- Cost-effective material recovery services - Sustainable disposal of assets in compliance with environmental and data security regulations - Secure data destruction and physical destruction onsite/offsite - Convenient and secure logistics (pickup options) - Chain of custody reporting and certification for all services - Customized recycling solutions

Value Delivery Customer Segment

Value Capture

- 91% target small & medium-sized businesses and corporations, Government agencies and municipalities, - Non-profit organizations, Distributors and retailers, OEM

Channels

- 77.4% of the companies pick up e-waste from the customer premises and drop-off in a designated collection point - Customer support through phone, email, or chat; - Mobile APP, Website, and social media platforms

Revenue Stream

- 89.2% revenue generated from the sale of recycling and data destruction service, sale of parts, components, and secondary raw materials

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4.4 Technical Support (IT Service) Model Businesses under the technical support model (4.7% of the analyzed firms) specialize in offering a service of repairing and replacing electronic devices to fix various issues. These services include diagnosis and repair of common hardware or software issues, such as cracked screens, broken buttons, malfunctioning charging ports, and battery replacements. Technical support businesses provide software-related services and offer remote, on-site, or in-store repair and replacement services tailored to meet the needs of their customers. Most technical support businesses target individuals, small and mediumsized businesses, and large corporations. These models generate revenue from repair and replacement services (Table 5). Table 5. Technical Support Model (4.7%) Dimensions

Sub-dimensions Descriptions

Value Creation

Value creation process

- Repair shops and part replacement

Circular Operating form

- Extending product life

Value Products Propositions

Value Delivery

Value Capture

- 90% of products are IT & telecommunication Smartphones, laptops, or tablets, printers, computers, networking devices, servers

Services

- Cost-effective repairing and replacement services - Convenience through Remote, on-site, or in-store

Customer Segment

- Individuals and small & medium-sized businesses

Channels

- Remote, on-site, or in-store/ repair shop - Customer support through phone, email, or chat; - Website, and social media platforms

Revenue Stream - Sale of repairing and replacement service, sale of replacement accessories

4.5 WEEE Reverse Logistics Model Reverse Logistics models (2.4% of the analyzed firms) focus on managing the return of electronic products and devices. The main activities of these models include secure transport and logistics, which involve the collection, sorting, inventory management, asset auditing, and asset tracking of WEEE items from multiple locations. These models ensure compliance with regulations and standards for the responsible disposal of electronic waste, minimizing the environmental impact of these products. These models also offer a chain of custody documentation from collection to disposal. The customer segments for companies operating under this model include large corporations, small and medium-sized businesses, government agencies, retailers, and OEMs. These companies

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generate revenue from the fees charged for the transport, logistics, and disposal services they provide (Table 6). Table 6. WEEE Reverse Logistics Model (2.4%) Dimensions

Sub-dimensions

Descriptions

Value Creation

Value creation process - Collection, transporting, sorting, asset auditing, and asset tracking and inventory management Circular Operating form

- Facilitate maximum product utilization and resource recovery

Value Propositions

Products

- All electronic products

Services

- Secure logistics

Value Delivery

Customer Segment

- Large corporations, small and medium-sized businesses, government agencies, retailers, and OEMs

Channels

- On-site service - Customer support through phone, email, or chat, website, and social media platforms

Value Capture Revenue Stream

- Sale of transportation and logistics service

5 Discussion and Conclusion This study provides a comprehensive understanding of the WEEE sector structure by developing a business model classification. This can help describe the various circular business models used for managing end-of-life electronics. The characteristics of the business models identified in this study serve as valuable tools for distinguishing between the different options available for managing end-of-life electronics. These insights provide a clear framework for understanding and differentiating diverse approaches to electronic waste. In particular, our study identified five main circular business model options from the analyzed 412 young companies operating in the WEEE sector: the IT Asset Disposition (ITAD) model, Trade-in/Buyback and Re-commerce model, Electronic Recycling model, Technical Support model, and Reverse Logistics model. These models rely on circular economy principles to minimize electronic waste by prioritizing refurbishment, recycling, and responsible disposal of electrical devices. Through the collection and analysis of data pertaining to companies in this sector, significant insights have emerged. This study reveals that approximately 50% of these companies engage in IT asset disposition. These companies target the overwhelming majority of 78% of SMEs and corporations. Furthermore, these companies specialize in office equipment and networking devices. However, approximately 27% of the analyzed businesses specialize in electrical and electronic equipment trade-in, buyback, and recommerce. These businesses serve a wide range of customers, including individuals and businesses. The findings highlight a concerning trend: despite the alarming increase

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in global e-waste generation caused by the rising demand for high-tech products and their decreasing service life, the practice of reusing technology products from individual customers is not observed sufficiently among young companies operating in the WEEE sector. In conclusion, this study provides a framework for researchers and professionals to gain a better understanding of the WEEE sector business models. This framework can be used by interested parties to evaluate business opportunities in managing end-of-life electrical products and identify areas for improvement, and develop strategies for more sustainable electronics management. Funding. This research was funded by CARIPARO Foundation, Padova, Italy. Conflicts of Interest. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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Gap Analysis for CO2 Accounting Tool by Integrating Enterprise Resource Planning System Information Martin Perau(B) , Dogukan Seker, Tobias Schröer, and Günther Schuh FIR, Insitute for Industrial Management at RWTH Aachen University, Campus-Boulevard 55, 52074 Aachen, Germany [email protected]

Abstract. Detailed carbon accounting is the foundation for reducing CO2 emissions in manufacturing companies. However, existing accounting approaches are primarily based on manual data preparation, although manufacturing companies already have a variety of IT systems and resulting data available. The gap analysis carried out based on the GHG Protocol and an reference ERP system shows how much of the required information for CO2 accounting can be integrated from an ERP system. The ERP system can cover 20% of the required information. The information availability can be increased to 49% through additionally identified modifications of the ERP system. Integrating the CO2 accounting tool with other systems of the IT landscape, e.g. Energy Information System, enables an additional increase. Keywords: sustainability · enterprise resource planning system · CO2 accounting · IT landscape · gap analysis · information system

1 Introduction Manufacturing companies are a major contributor to greenhouse gas emissions due to their economic activities and high CO2 emissions [1]. In 2021, the industry accounted for 21,44% of global CO2 emissions [2]. As a measure, the countries have set targets to reduce CO2 emissions, which also affect the manufacturing industry. For example, the European Union aims to become greenhouse gas neutral by 2050 [3]. The basis for reducing and managing CO2 emissions is accounting for these emissions [4]. It enables corporations to pinpoint areas where CO2 emissions are particularly high and to adjust their activities accordingly [4]. Various information is needed for accounting CO2 emissions of a company [5]. In addition, for reducing CO2 emissions, it is beneficial to have the most accurate and comprehensive carbon accounting information possible. This requires high-quality and diverse information. [6] Environmental accounting, including corporate carbon accounting, is typically still performed using specialized and stand-alone software. Manually collecting the required information is time-consuming and costly. [7]. © IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 58–71, 2023. https://doi.org/10.1007/978-3-031-43688-8_5

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A promising approach is to integrate sustainability management, particularly carbon accounting, into a company’s existing IT landscape. IT landscape consists of different information systems, often called IT systems, which encompass the activities of collecting, storing, processing, and transmitting information [8]. Within the paper, the term information system and IT system are used as synonyms. Better use of IT systems in the context of sustainability management can improve the speed of data collection and processing, increase the efficiency of processes, improve the monitoring of environmental aspects, and helps to identify measures to reduce the ecological impact [9]. This paper aims to investigate the extent to which CO2 accounting information is currently available through integrating other information systems. The focus is on enterprise resource planning systems (ERP systems), which are common in manufacturing companies [10]. In addition, we will identify the potential to increase the information availability from the ERP system to conduct CO2 accounting more efficiently. Finally, it will be considered to which extent the information availability can be increased by integrating other information systems with the CO2 accounting tool.

2 Relevant Theoretical Background This research is based on the topics of CO2 accounting and information systems of manufacturing companies. We explain the basic of the topics in the following section. 2.1 CO2 Accounting Carbon accounting is a crucial process in measuring and tracking a company’s greenhouse gas emissions. In the implementation of this process, there are different methods available. Mainly companies can differentiate between Product Carbon Footprint (PCF) and Corporate Carbon Footprint (CCF). PCF measures all greenhouse gas emissions produced throughout a product’s entire lifecycle, from creation to disposal referend to a product as a reference unit [11]. On the other hand, CCF is a measure of the total amount of GHG emissions produced by a company due to its activities, including but not limited to product manufacturing, energy consumption and transportation. It uses an organizational boundary, such as a plant or the whole company [12]. This paper focuses on the CCF, which is discussed in more detail below. The process of CCF accounting can be divided into five steps [13]: 1. 2. 3. 4. 5.

Identify emissions sources Select calculation approach Collect data and choose emission factors Apply calculation tools Roll-up data to corporate level

Various guidelines and standards are available to assist identifiying of emission sources. For example, the GHG Protocol or the ISO 14064–1, ISO 14069 and ISO 14072 standards can be used for support [14]. The GHG Protocol is particularly common [15] and is based on an original publication by the World Resource Institute and an consortium of industrial companys from 1998 [16]. The first edition of the Corporate Standard was

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Fig. 1. Overview of scopes and emissions across a value chain (GHG) [13]

published in 2001 and is regularly updated since [13]. The current GHG Protocol divides emission sources into three main areas, known as scopes (see Fig. 1) [13]. Scope 1 or direct emissions derive from sources owned or controlled by the accounting company. This includes, for example, the combustion of fossil raw materials in the company’s vehicles or boilers. Scope 2 emissions focus on energy-related emissions based on the purchasing of various energy types. The emissions to be optionally accounted for are the Scope 3 emissions, which include emission sources in the upstream and downstream value chain [13]. Emissions can be measured directly, determined by mass balance, or by calculation. The most common methods are calculation approaches, which have different characteristics. The basic principle is that an emission factor for a specific activity (e.g. CO2 emissions per kg of steel purchased) is related to a corresponding activity (quantity of steel purchased). The GHG Protocol distinguishes between different approaches with varying degrees of accuracy. The aim is to use approaches that are as accurate as possible, whereby complementary approaches with lower accuracy can be used if no information for other approaches is available [13]. Exemplary calculation methods for accounting purchased goods and services include the supplier-specific method, the hybrid method, the average-data method, and the spendbased method. The supplier-specific method is the most accurate, as it is applied to a specific product and uses a specific emission factor for that product. The average-data method requires companies to account for quantities or other reference units of goods, which are then multiplied with a corresponding emission factor. The spend-based method uses the economic value of a good and multiplies this with value-based emission factors. The accuracy of the approaches decreases in the described order. Hybrid methods are special forms of these methods. The required information differs in type and number depending on the chosen method. [17]. In the subsequent step of data collection, existing data sources in the company are analyzed to see whether they can provide data for accounting or not. Finally, CO2

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accounting tools can be used to calculate the respective emissions. The last accounting step focuses on expanding and implementing accounting for different organizational units of the company [13]. This paper focuses on improving step 3 of the carbon accounting process. The basis for this is a comprehensive description of the possible emission sources, the different calculation methods, and the resulting necessary information. As described in Sect. 1, this information should ideally be realized through information integration with existing information systems. Therefore, the following section provides a brief description of IT landscapes, information systems and ERP systems in manufacturing companies. 2.2 Information System An information system, often abbreviated to an IT system, serves to achieve defined goals and achieves them through the communication and processing of information from the environment [18]. In order to exploit the efficiency of data processing, the individual information systems must be considered together when designing the IT landscape [19]. An IT landscape is an arrangement of individual information systems configured in a particular manner to act as a foundation for an enterprise’s business operations [20]. For this purpose, the systems must be integrated with each other. This integration refers, among other things, to data or information integration. This includes the conceptual, logical and/or physical merging and exchange of data so that it can be used by different entities in the IT landscape [21]. This paper will specifically discuss integrating data or information from individual IT systems into a CO2 accounting tool. Among these individual IT systems, enterprise resource planning systems (ERP systems) are one of the widely used information systems in manufacturing companies [10] and represent the focus of paper. An ERP system is responsible for the management, planning, documentation, and control of business processes and resources of an enterprise [22]. In particular, the ERP system is characterized by the fact that it has a wide range of functionalities [23]. One of the core functions of an ERP system is sales management, for managing customer activities such as creating quotes and orders. Typically, an ERP system enables further the planning of quantities and timing of materials to be produced and procured. Herefore, the ERP system involves the modules of disposition, procurement and production planning. These functionalities are supported by the material master data module, which includes functionality for creating and maintaining materials, bills of materials. In addition, ERP systems can also have functions for shipping management or service management [23]. Due to the large and heterogeneous functional scope of the ERP system, it has a wide range of different data. As such, it tends to be particularly well suited to providing diverse data for integration into a CO2 accounting tool.

3 Research Approach The aim is to analyze which information can be used for CO2 accounting by integrating information from the ERP system to a comprehensive CO2 accounting tool and which information is missing. The resulting gap enables the identification of measures

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to increase the availability of information. This objective corresponds to the method of a gap analysis. The aim of a gap analysis is to identify a distance between a current and a target state and to derive measures to reach the target state [24]. Gap analyses are used in a variety of research domains, such as service engineering [25] or technology planning [26]. The starting point of the gap analysis is the definition of the target status, followed by the determination of the current status [24]. Based on the defined gap, measures can be derived [27]. We structured our gap analysis in the same way, whereby the derivation of measures was divided into two steps. The research approach is visualised in Fig. 2.

1. Define target state

Necessary information for CO2Accounting based in GHG Protocol

2. Detemine current state

Current information availability gap

Information availability by integration of the ERP system Necessary information

Information available

3. Detemine modifications measures

4. Detemine further potentials

Information availability gap after modifications

Information availability after modifications of the ERP system

Information availability by integrating further information systems

Information not available

Fig. 2. Approach of the gap analysis

The first step is to determine the target state. The target state represents the information required for a company’s carbon accounting, which should be provided by integrating existing information systems. The required information is defined by the Greenhouse Gas Protocol (GHG) and the calculation guidelines of the GHG Protocol [13, 17, 28]. We chose the GHG Protocol because it is an internationally recognized and widely used standard for corporate carbon footprint accounting [15]. Scope 1 and Scope 2 emissions were considered in full in the analysis. For Scope 3 emissions, only upstream activities were considered (excluding fuel and energy related activities). Scope 3 downstream activities were not considered as the focus of the paper reflects a cradle-to-gate accounting of corporate CO2 emissions. Furthermore, the majority of Scope 3 emissions of manufacturing companies, except for CO2 emissions occurring in the use phase of products, are located in upstream activities [29]. Within the GHG Protocol, we considered each accounting method separately. Hybrid approaches were not considered, as these can be formed from the individual methods. The second step is to determine the current state. Here, the ERP system was considered as the main information system that can provide relevant information for CO2 accounting, as it is particularly widespread in manufacturing companies [10]. For this purpose, we examined the reference ERP system proALPHA ERP and validated the results with experts. We analyzed which data from the reference ERP system can be used as necessary information for CO2 accounting. We use the proALPHA Application

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guide as a source of information for this analysis. It contains extensive descriptions of the functions of the ERP system and, in particular, the existing master data in the system [30]. This was used to analyze whether the existing master and transactional data or information of the ERP system can provide information for CO2 -accounting. Consequently, the information availability could be calculated: Information availability = Nec/ Av with

(1)

Nec = Number of necessary information according to GHG-Protocol Av = Number of the available information in the reference ERP System The information gap equals 100% minus the information availability. The following table shows an extract of the resulting information after applying the first and second steps of the research approach (see Table 1). Table 1. Exemplary comparison of target and current state Target state Scope

Current state of information availability

Sub-Category Specification Calcuation method

Scope 3 Purchased upstream products and services

Products

Necessary information (Nec)

supplier-specific Product number or identification number

Available information (Av)

Information availability

Type of part 75% and type of use or serial number

Mass or number Number of of pieces of the pieces or purchased goods delivery quantity Supplier of the Supplier purchased goods master data Supplier-specific Not cradle-to-gate available emission factor

Four information items are required for accounting purchased products using the supplier-specific method of the GHG Protocol. In this case, the ERP system cannot provide information for supplier-specific cradle-to-gate emission factors. The resulting information availability equals 75% and the information gap equals 25%. The method was applied in the same way for the other categories of the GHG protocol. The third step focuses on identifying possible modifications to the ERP system to provide more information for carbon accounting. The identified measures allow for a defined reduction of the information availability gap. Finally, the fourth step is analyzing the remaining information gap and examining how it can be closed by integrating further information from other IT systems.

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4 Results of the Gap Analysis According to the steps of the gap analysis, the results are presented. First, an assessment of the gap between the current and the target state is presented (step 1 and 2 of the gap analysis). This is followed by suggestions for modifications in the ERP system and the potential for increasing the information availability. Subsequently, more comprehensive measures are identified to close the information gap. 4.1 Status Quo of Information Availability Gap Based on the research approach described above, the information gap was determined. The analysis of the GHG protocol in the defined scope shows that 288 information items are required for the accounting if the different calculation methods are considered. The necessary information results from the accounting of the different CO2 sources. Due to the high number of identified information items, it is not possible to list the identified information items within this paper. The corresponding amount of information is determined based on the extensively described research approach and analogous to the example shown in Table 1. Using the detailed functional description of the reference ERP system, it was determined which of this information can be provided by information from the ERP. The analysis shows that the ERP system can provide 59 of the 288 required information items. Table 2, 3 and 4 show for which area of consideration information is available. Table 2. Information availability gap for scope 1 emissions Target state

Number of available information

Availability Ratio

Number of available information

Availability Ratio

Specification

Company facilities

Generation of fuel-based electricity, heat or steam through the combustion of fuels

6

0

0%

4

67%

Physical or chemical processing

activity-based

4

0

0%

1

25%

Fugitive emissions

activity-based

7

0

0%

1

14%

Transport of goods

fuel-based

45%

Transport of people

Number of necessary information

Information availability after modifications

Sub-Category

Company vehicles

Calcuation method

Current state of information availability

11

0

0%

5

distance-based

7

0

0%

3

43%

average-data

7

3

43%

4

57%

fuel-based

3

0

0%

2

67%

distance-based

5

0

0%

1

20%

average-data

6

2

33%

4

67%

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Table 3. Information availability gap for scope 2 emissions Target state

Sub-Category

SpecificaTion

Purchased energy

Heat Electricity Steam Cooling

Calcuation method

Number of necessary infor-mation

Current state of information availability

Information availability after modifications

Number of available information

Number of available information

Availability Ratio

Availability Ratio

location-based

8

2

25%

5

63%

market-based

9

1

11%

6

67%

location-based

8

2

25%

6

75%

market-based

9

3

33%

6

67%

location-based

8

2

25%

5

63%

market-based

9

1

11%

6

67%

location-based

8

2

25%

5

63%

market-based

9

1

11%

6

67%

Table 4. Information availability gap for scope 3 upstream emissions Target state

Current state of information availability

Sub-Category Specification Calcuation method

Purchased products and services

Services

Products

Capital goods

Upstream Transport Transport and Distribution Distribution

Operational waste

Information availability after modifications

Number of Number of Availability Number of Availability necessary available Ratio available Ratio information information information

provider-specific

5

3

60%

4

80%

average-data

5

3

60%

4

80%

spend-based

7

5

71%

5

71%

supplier-specific

4

3

75%

3

75%

average-data

11

6

55%

8

73%

spend-based

10

4

40%

7

70%

supplier-specific

3

2

67%

2

67%

average-data

8

3

38%

5

63%

spend-based

9

3

33%

5

56%

fuel-based

14

2

14%

4

29%

distance-based

7

1

14%

5

71%

spend-based

8

4

50%

5

63%

location-based

11

0

0%

1

9%

average-data

4

0

0%

0

0%

supplier-specific

4

0

0%

2

50%

waste-type-specific 4 method

0

0%

2

50%

average-data

0

0%

1

4

25%

(continued)

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M. Perau et al. Table 4. (continued)

Target state

Current state of information availability

Sub-Category Specification Calcuation method

Business travel

Commuting

Information availability after modifications

Number of Number of Availability Number of Availability necessary available Ratio available Ratio information information information

fuel-based

9

0

0%

3

33%

distance-based

10

0

0%

2

20%

spend-based

10

1

10%

4

40%

fuel-based

5

0

0%

0

0%

distance-based

6

0

0%

0

0%

spend-based

6

0

0%

0

0%

In total, the analysis shows that an information availability of approximately 20% and an information gap of 80% exist. However, the availability of information varies depending on the scope of the CO2 accounting (see Fig. 3). Current gap 20%

Scope 1, scope 2 and scope 3 upstream Only scope 1 Only scope 2 Only scope 3 upstream

9%

288

80%

56

91% 21%

68

79%

24%

164

76% Information available

Information not available

Necessary inforamtion

Fig. 3. Information availability gap for CO2 accounting based on ERP-System

The difference between scope 1 and scope 2 or 3 results from the fact that an ERP system typically does not contain process or technical information. This information is needed for Scope 1 accounting. For example, the amount of fuel consumed or the amount of gas produced during chemical processing are required. On the other hand, information is more widely available for scope 2 and scope 3 accounting. Due to the typical modules of purchasing and finance, the ERP system contains many information items about external entities [23]. In addition, within a scope, there are also significant differences in the availability of information. In scope 3, the subcategory purchased products and services has the highest availability of information. In addition to the type of emissions, the type of calculation method affects the information availability (see Fig. 4).

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4% fuel-based

48

96%

0%

35

100%

activity-based distance-based 3%

45

97%

5

83%

17%

market-based

36

81%

19%

location-based

40%

60%

provider-specific 34%

spend-based

43

62%

38%

average-data

45%

55%

supplier-specific

4 11

100%

waste-type-specific method

50 11

66%

0% Information available

Information not available

Calculation method

Fig. 4. Information availability gap for CO2 accounting based on ERP system depending on the calculation method

The average-data, supplier-specific or provider-specific, and spend-based methods have comparatively high information availability. High availability of information for the average-data method results from the fact that, for example, quantity data is contained in the bill of materials of an ERP system. This information is a core component of the average-data method. For example, information for the spend-based method can be obtained from incoming invoices. This includes, for example, net amounts. This first step of the analysis enables the subsequent identification of modification possibilities within the ERP system to reduce the information availability gap. 4.2 Modification Measures of ERP System to Reduce the Information Availability Gap Based on the identified gaps, easy-to-implement measures to reduce the information availability gap are identified and validated through expert interviews. The identified measures can reduce the gap from 80% to 51% (see Fig. 5). Due to the number of modifications, only extracts can be explained.

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Scope 1, scope 2 and scope 3 upstream

Only scope 1 Gap with modifications

Without modificaons

Without modificaons 9%

20% 29%

With modificaons

Gap with modifications

36%

With modificaons

45%

49% 51% Only scope 2

55% Only Scope 3 upstream Gap with modifications

Gap with modifications Without modificaons With modificaons

21%

Without modificaons 45%

24% 20%

With modificaons

66%

44% 34%

56% Information available

Information not available

Fig. 5. Information availability gap for CO2 accounting with modification measures of ERP system

The information availability gap for Scope 1 accounting can be reduced by providing more specific receipts and invoices. This means, among other things, that the amount of fuel consumed by the company’s goods or passenger transport should be recorded in the system. Another way to improve the availability of information is to determine and implement the distance between a customer’s location and the production site. To improve the accounting of fossil fuel combustion, the amount of purchased fuel should be specified in a data field within the ERP system. The information gap for Scope 3 emissions can be improved through various modifications. In the area of emissions resulting from waste from operations, this can be realized by specifying waste as an inventory item, analogous to production parts. This means that information on the type and quantity of waste is available in the ERP system. Accounting for transport emissions can be improved by expanding supplier-related master and transaction data. For instance, the distance traveled, the mass of the goods transported, and the type of vehicle or mode of transport should be recorded in the ERP system. 4.3 Potentials for Reducing the Information Availability Gap by Integrating Further Information Systems The gap analysis shows which necessary information is unavailable in the ERP system, even after modifications. Integrating other information systems with the CO2 accounting tool can further reduce the resulting information gap. Suitable information systems for this integration are shown below (see Fig. 6).

Gap Analysis for CO2 Accounting Tool Providing 48% of the necessary information Providing up to 22% of the remaining necessary information Providing up to 10% of the remaining necessary information

CO2 accounting tool

Providing up to 10% of the remaining necessary information Providing up to 9% of the remaining necessary information

69

ERP system Emission factor databases Transport management systems Human resourcemanagement system Energy information system Other systems

Fig. 6. Potentials for reducing the information availability gap by integrating further information systems

Integrating an emission factor database has the most significant potential to reduce the information gap after the ERP system. Up to 22% of the identified information for CO2 accounting corresponds to emission factors. These factors can be used to calculate specific emissions based on activity data from other systems [31]. In the area of CO2 accounting, it is vital to ensure that appropriate emission factors are selected from external emission factor databases based on the integrated information from other information systems. For example, the appropriate emission factor for a material (information from the emission factor database) is selected based on the type of material (information from the ERP system). In addition, integrating transport management systems (TMS) can increase the availability of information. TMS enables transport booking, planning, and monitoring [32]. As a result, they have the potential to provide information on vehicle types, fuel efficiency, the total volume of transported goods, possible type of shipment activities, and other transport-specific information. In this case, the integration of external systems or the company’s own TMS systems is considered. Integrating human resource management systems or modules could provide about 10% of the missing information. These modules are also sometimes part of ERP systems. SAP R/3 Human Resource Management includes, for example, HR master data management, organizational structure and management, personnel development and recruitment, time management, payroll, and travel management [33]. In particular, information about employees’ business trips and commuting activities can increase the information availability. In addition, the information availability can be increased through the integration of energy management systems. The main functions of an energy information system are energy monitoring, variance analysis, energy controlling, forecasting and energy reporting [34]. These systems could therefore provide information such as the amount of heat or energy per reference unit, thermal efficiencies, energy suppliers and other energyspecific information. The systems mentioned could also provide more information, thus reducing the amount of information integrated from the ERP system. Additionally, other information systems could also provide information.

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5 Conclusion, Research Limitations and Outlook The gap analysis to determine the information gap for an integrated and data-driven carbon accounting shows that 20% of the carbon accounting information is available in the ERP system. Simple modifications to the ERP system can increase the availability of information so that the information gap after modification is 51%. Integrating different information systems, particularly an emission factor database, can achieve a highly integrated and data-based carbon accounting. In summary, efficient carbon accounting requires integrating data from different information systems. The results should be viewed critically, as the gap analysis was carried out for an exemplary ERP system. ERP systems may differ in functionality, so the information available may also differ. In addition, the ERP system was primarily considered as the information provider, although other systems could also provide a wide range of information. Therefore, in their future work, the authors aim to carry out the described methodology independently of a specific ERP system and also concerning other information systems. In addition, the aim is to further specify and classify the necessary information for CO2 accounting and describe the ERP system’s usable information in a semantic data model to advance the required technical integration. Acknowledgments. Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany´ s Excellence Strategy – EXC-2023 Internet of Production – 390621612.

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How Can Digitalisation Support the Circular Economy? An Empirical Analysis from the Manufacturing Industry Beatrice Colombo(B) , Albachiara Boffelli , Jacopo Colombo , Alice Madonna , and Simone Villa Department of Management, Information and Production Engineering, University of Bergamo, Via Pasubio 7/B, 24044 Dalmine, BG, Italy {beatrice.colombo,albachiara.boffelli,jacopo.colombo, alice.madonna}@unibg.it, [email protected]

Abstract. Digitalisation is widely recognised as an enabler of circular economy. Nevertheless, this relationship has mainly been assessed taking a theoretical lens so far. Therefore, this paper aims at investigating how digital technologies can support companies in implementing the circular economy from an empirical point of view. In detail, the current work sheds light on the digital technologies that most help the circular economy and which of its practices are mainly affected by them. A multiple case study from the Italian manufacturing context is the methodology adopted to perform the study. The obtained results confirm that digitalisation can foster the implementation of circular economy practices. Specifically, Internet of Things, Big data analytics and Simulation mainly support Refuse, Reduce and Rethink, while other Rs are less impacted. Keywords: Circular economy · digitalisation · multiple case study

1 Introduction To meet the Sustainable Development Goals, the transition towards the circular economy (CE) is essential. Indeed, by extending the life of products, the CE represents a sustainable approach to resource management and efficient consumption [1]. It is precisely in this direction that the global economy and industries are moving. An increasing number of companies are transforming their businesses towards the conceptual model proposed by the CE to overcome raw material scarcity and safeguard the global ecosystem [2]. However, the transition that organisations are facing is far from trivial. A common problem preventing the implementation of the practices proposed by the CE is the lack of adequate information and means, both within companies and along the supply chain [3, 4]. In such a context, digitalisation can play a pivotal role [5, 6]. Digital technologies (DTs) bring several benefits to companies [7]. Among others, their adoption enables products, machines and other objects to interact with people and communicate with © IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 72–84, 2023. https://doi.org/10.1007/978-3-031-43688-8_6

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each other, making all types of information available and permitting the construction of efficient systems [8, 9]. Accordingly, it is widely recognised that digitalisation positively impacts CE in manufacturing contexts [10]. A plethora of studies have already conceptualised from a theoretical perspective that digitalised environments ease the implementation of circular strategies and practices, as well as business models [11, 12]. Nevertheless, empirical papers evaluating the application of DTs to enable CE in actual case studies are scant [13]. Therefore, this paper aims to answer the following research question: “How can digital technologies support companies in implementing the circular economy?” The current research contributes to the existing body of literature by providing insights from three manufacturing companies located in Lombardy, a region in northern Italy. Overall, the empirical results obtained confirm the strong relationship between digitalisation and CE. More in detail, the analysed DTs seem to highly support circular practices belonging to the ‘Smart product use and manufacture’ area, such as Refuse, Rethink and Reduce, while remaining circular practices are currently less tackled. The remainder of this paper is organised as follows: Sect. 2 reviews the extant literature on the integration between digitalisation and CE. Section 3 outlines the methodology adopted to select and subsequently perform the case studies. Section 4 proposes the focal findings. Lastly, Sect. 5 critically discusses the results in terms of theoretical and managerial implications and provides concluding remarks, future research avenues and limitations.

2 Related Literature on Integration Between Digitalisation and CE The fourth industrial revolution, known as Industry 4.0, shows how new enabling technologies can benefit organisations’ performance [14, 15]. Indeed, the new wave of digitalisation is composed of a multifaceted set of technologies, well-established in the literature, whose main applications include the industrial Internet of Things (IoT), cloud computing, big data analytics, simulation, augmented reality, additive manufacturing, horizontal and vertical system integration, autonomous robots and cybersecurity [16]. Recently, the literature has also studied the potential impacts of digitalisation on the CE [17], which is defined as “an economic system that replaces the end-of-life concept with reducing, alternatively reusing, recycling and recovering materials in production/distribution and consumption processes” [18]. Academics and practitioners have produced additional definitions of the term taking into account their perspectives. However, in general, the CE is a production and consumption model that aims to reduce waste as much as possible [19]. In addition to multiple definitions, numerous frameworks have been developed to synthesise CE practices. One of the latest and most innovative frameworks is the 10Rs framework, which groups the different types of CE practices (refuse, rethink, reduce, reuse, repair, refurbish, remanufacture, repurpose, recycle, recover) into three major clusters: (i) smarter product use and manufacturing, (ii) extended lifespan of products and their parts and (iii) useful application of materials, proposing also a classification of the practices in terms of circularity’s capability [20].

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The major stream of literature emphasises the ability of digitalisation to support organisations in improving circular performance [3, 9]. DTs positively support CE by first ensuring greater knowledge and monitoring of processes and products due to increased measurement and traceability capabilities [17]. Besides this perspective, a second viewpoint exists that sees DTs as enablers of circular models. This attributes strategic value to them in involving key stakeholders in the value chain to achieve CE [9]. While these considerations apply at a general level, there are implications related to each specific technology. IoT and big data analytics are the most pervasive technologies as they affect different areas of circularity. Using the data collected through IoT, products can be redesigned to be easily maintained, upgraded, disassembled and recycled, thus extending product lifespan and closing the loop through repair, remanufacturing and recycling [21]. In addition, companies can monitor product condition and usage, discouraging careless user behaviour, limiting wear and tear and extending product life. Furthermore, the analysis of big data collected through IoT enables preventive maintenance, extending product lifespan, as well as offering personalised advice to customers to optimise product utilisation. This feature helps increase resource efficiency [21]. Simulation is associated with better management of complex supply chains or remanufacturing of complex products but it can also improve resource efficiency or enable the development of new services related to maintenance [9]. Additive manufacturing improves product reuse and reselling through repair, refurbishment and remanufacturing with minimal environmental impact, thus extending the life of products [22]. However, the literature is not studying the digitalisation-CE relationship in a oneway direction. In fact, alongside these studies there is research focusing on the mutual relationship between the two dimensions. From this perspective, the intersection of CE practices and DTs supports the achievement of Sustainable Development Goals [23] and a sustainable supply chain [24]. Rajput and Singh (2019) [25] concretise the mutual relationship through the identification of enabling and challenging factors to connect CE and Industry 4.0. Belhadi et al. (2022) [26] developed a framework of CE-I4.0 integration using the principles of the dynamic capabilities perspective and an integration index to assess the level of integration between the two dimensions. Beyond research approaches, it clearly emerges that digitalisation is composed of many enabling technologies, while CE is addressed by applying several practices. Lei et al. (2022) [27] identified that IoT, additive manufacturing, big data analytics and artificial intelligence are the top technologies, while reduce, recycle, maintenance and waste management are the top circular practices. Nevertheless, the presence of core technologies should not suggest that the relationship can exist solely because of a single digital application. There must be broad adoption, especially of DTs, to ensure that this relationship has positive effects for CE. This means that the adoption of one technology alone is not enough to implement all circular practices. In view of the above, it is therefore time to focus on how DTs can foster the implementation of CE practices from a more empirical perspective.

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3 Methodology The study has been carried out following the research methodology based on case studies. Voss et al. (2002) [28] reported that the case research methodology is one of the most effective empirical methodologies for conducting research in operations. Besides allowing the development and testing of new theories, it can also bring benefits in the practical field [28]. The decision to use the case method for this project depends on several factors. First, case study analysis allows for answering research questions that investigate the “how” of certain phenomena [28]. Second, this kind of empirical research promotes learning and understanding of recent trends, such as the connection between digitalisation and the CE [9]. Last but not least, the case study technique has been used in this paper since it can help build the theory that explains how DTs promote the CE. 3.1 Case Selection Case selection is critical to the success of empirical analysis. Generally, when a limited number of cases are analysed, there is a greater possibility of going into detail. In contrast, when a higher number of cases is considered, it is easier to generalise results [28]. This research was constructed following the first direction between the two options to try to analyse the selected cases in more detail. Another important aspect to consider is the choice of the sample of companies to analyse. The latter must be selected to facilitate the achievement of the research objectives and predict similar results [28]. A sample of 16 companies was considered as a starting point. These companies had already participated in a project launched by the local industrial association in collaboration with the university, focusing on the CE and digitalisation. The project was carried out in the first half of 2022, and it requested the companies to express their views on the CE, indicating the current circular practices, those they intend to implement in the future and the support provided by DTs in this area. The companies also participated in a supplementary questionnaire to assess their level of digitalisation. Three researchers and one member of the local industrial association participated in all the interviews conducted to determine the circularity and digitalisation levels of the companies. An analysis of the results of this first investigation was carried out to select the companies that had the desired characteristics to investigate the research question. In particular, three companies were chosen due to their strong attitude towards the CE and their advanced digital maturity level. Table 1 reports the main characteristics of the analysed case studies. 3.2 Data Collection The data were collected during nine semi-structured interviews, three for each company. The first two interviews dealt with the preliminary assessment of digitalisation and circular levels and were carried out during the first phase of data collection described in the previous section. The third batch of interviews was conducted during the fourth quarter of 2022. All the interviews were prepared and conducted following precise indications and advice from Voss et al. (2002) [28] to increase the study’s validity.

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B. Colombo et al. Table 1. Characteristics of the analysed case studies.

Case Study

Role of the interviewees

Business Sector

# of employees (2021)

Revenue (mln e, 2021)

Alpha

Quality, environment and safety management system assistant manager; Plant manager

Chemical Industry

51

35

Beta

Sustainability & Innovation Manager; Linen & Hemp Technologist Manager

Textile Industry

31

71

Gamma

ICT Manager; Marketing Director

Plastic and Rubber Industry

399

91

An ad-hoc interview protocol was created to ease the accomplishment of trustworthy research [28]. The protocol’s questions (available upon request) were designed and organised to support the respondent in achieving the study’s primary objective rather than influencing responses. Moreover, the same protocol was used with all companies to not create bias between the different interviews [12]. The organisations were contacted by means of a cover letter indicating the objectives of the project. Before the interview, companies were also given the interview protocol to prepare for the themes to be covered. The data collection was carried out directly in the companies’ plants to allow the collection of observational data. All the interviews lasted more than an hour and were conducted by four researchers with different roles and company figures. These two aspects have increased the validity of the research [28]. First, the choice to conduct interviews with several interviewers helped produce better results thanks to the convergence of different observations. Also, this choice enabled real-time discussions right after the data collection. Secondly, since the macro topics analysed in this research are two, namely CE and DTs, the possibility of interviewing more figures within each company has allowed for collecting information from people specialised in either of the two. Before the interviews, the information gathered for each company during the first research phase has been reviewed by the researchers to be aligned for the circular activities implemented and prepare potential cues to be used during the interview. All the filed notes were shared and compared, as well as observations were noted. The interviews were recorded and transcribed verbatim. Further, the material from the first two interviews conducted during the preliminary assessment was collected for analysis. Finally, secondary data from the companies’ websites, balance sheets and reports, news and

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articles from the media was gathered, increasing the archival material for triangulation purposes. 3.3 Data Analysis The data were analysed within and across cases to find significant patterns. First of all, the collected material for each case was coded. The emerging topics have been coded following the order of the sections present in the protocol. The fundamental aspects of each have been summarised and reduced into tables to facilitate visualisation thereof. As suggested by Voss et al. (2002) [28], the entire procedure was crucial since it enabled us to categorise the data and determine their correlations and strengthen the triangulation and reliability of the study. Each case’s analysis has focused on identifying the potential connections between the specific DT and the CE practices. On the other hand, the cross-case analysis was carried out to compare the results from the different companies to identify whether patterns recurred among the cases. Additionally, this phase has assisted in recognising both frequent and more creative adoptions of DTs in support of CE practices. The former served to confirm and generalise the results already present in the literature, while the latter to determine the starting point for the research of future studies. Besides identifying the most supported practices, technologies have been examined to identify how they assist companies and the capabilities thereby provided. Lastly, difficulties businesses faced while implementing the two paradigms were briefly examined to recognise recurring problems.

4 Results This section presents the main findings of the study on the relationship between DTs and CE practices. It is important to note that these results are preliminary as the study is still in its early stages. Therefore, further investigations of individual variables and their relationships, as well as an expansion of the number of cases to be analysed, are needed to enhance the research. 4.1 Cross-Case Analysis To facilitate the analysis of the information obtained from the three case studies, a specific matrix called the CE-DTs Matrix (Table 2) was designed. Table 2 shows the CE practices implemented by each case and indicates whether they were supported by DTs. It is worth mentioning that, based on the digital maturity assessment, the digital maturity level is medium-high for all the cases under investigation and all the companies analysed systematically use the DTs considered in this research. Overall, all the examined cases show that at least half of the circular practices implemented by organisations are supported by DTs. Although there are a small number of practices from the 10Rs framework that companies pursue without the use of DTs, the research findings fully validate the potential of DTs in supporting circular business models. Moreover, the outcomes align with the initial hypothesis regarding the DTs with the greatest potential for the CE. In fact, IoT and big data analytics emerge as the most

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B. Colombo et al. Table 2. CE-DTs matrix (A: Alpha, B: Beta, G: Gamma).

Circular practices

Implementation of CE practices

Digital Technologies (DTs) Internet of Things

Big data analytics

Simulation

A

A, B, G

Smarter product use and manufacture

Refuse

A, B, G

A

Rethink

A, B, G

A

A

Reduce

A, B, G

A, B, G

A, B, G

A, B, G

Extend lifespan of product and its parts

Reuse

A, B

-

-

-

Repair

A, G

G

-

-

Refurbish

A

A

-

-

Remanufacture

A, B, G

A, B, G

-

-

Repurpose

A, B

B

-

-

Recycle

A, B, G

A

-

-

Recover

-

-

-

-

Useful applications of materials

exploited technologies in the analysed case studies concerning the implementation of circular activities. This can be attributed to their ability to address the issue of information scarcity within companies and along the supply chain. In particular, they make it possible to extract, monitor, analyse and share data and information in an effective and efficient way. According to the results, the circular practices most supported by DTs belong to the ‘smarter product use and manufacture’ category. Each of the three practices in this area is assisted at least once by DTs. More in detail, Reduce practice has received the most attention by DTs. Less common, but still present, seems to be the DTs’ support for CE practices in the second group, aimed at extending the lifespan of products and their parts. On the other hand, although the organisations interviewed carried out some material recycling activities, the results did not highlight significant support for the ‘useful application of materials’ practices provided by IoT, big data analytics and simulation, apart from Recycle in the case of Alpha. 4.1.1 Identified Relationships Between DTs and CE Companies’ efforts to reduce the consumption of their production resources appear to be particularly recurrent, whether to achieve greater production efficiency or a targeted decrease in materials used. While it cannot be denied that this was already a common practice before the development of DTs, digitalisation greatly facilitates this. With the implementation of DTs, companies can activate automatic procedures they were previously unable to do. They are now able to make production processes and warehouses efficient, know exactly the condition of machinery, constantly monitor how customers are using products and properly test items to be produced.

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All these innovative features effectively introduced by DTs have enabled organisations to reduce many of their resources. The results showed that electricity and raw materials are the most commonly reduced resources. The former, for example, is greatly reduced because companies with photovoltaic systems have sensors and platforms to monitor consumption and detect abnormal peaks. Conversely, the latter decreases as organisations have greater control over their production processes and product development, and management can easily intervene by minimising raw material waste. As shown in Table 2, the DTs most helpful to companies in implementing this circular practice are the IoT and big data analytics. Common to all three case studies is how both technologies are leveraged to raise production efficiency. Specifically, the three companies explained how they adopted the two DTs in complementary ways. In a few words, sophisticated sensor systems are installed on machines to generate and gather vast amounts of data on production parameters in real time, while data governance and analytics tools are employed to manage and analyse the data to provide the insights needed to understand how to reduce resource consumption. Another recurring benefit, in cases Beta and Gamma, within the Reduce practice, is the opportunity that IoT and big data analytics enable to monitor the status of machinery. Both companies witnessed that they were able to perform predictive maintenance by avoiding breakdown repairs and ensuring greater production efficiency. This has been rendered possible through purpose-built IT platforms and sensors connected directly to machinery and tools that collect data on machinery utilisation and wear and tear. From a CE perspective, the advantage of this process is that recurring disruptions generated by production breakdowns can be avoided. Accordingly, fewer materials could be wasted, for instance, during the production of defective products and, therefore, less energy would be consumed. Efficient inventory administration and elimination of overproduction by keeping track of customer usage are less commonly adopted but still viable ways to save resources. Finally, the use of simulation tools as an aid to reduce resource consumption was demonstrated in all the cases. Whereas previous technologies intervened in already industrialised products, simulation is mainly used in the product development phase to design and test items to minimise the raw materials needed. Similarly, Refuse and Rethink practices also benefit from digitalisation. Here assistance is provided for the development of innovative products designed with non-harmful materials that last longer. In general, DTs help companies collect and analyse a variety of data on customer behaviour to promote eco-design. It turns out that in Alpha, IoT sensors are used to monitor product consumption directly from the customer’s plant. Big data analytics are used to extract insights and support decision-making on product redesign to increase circularity. In addition, simulation tools help developers with more technical tasks, enabling the prototyping of products with recycled and unusual materials, just as in resource-saving practice. Concerning ‘Extend lifespan of product and its parts’ practices, only the support provided by IoT emerged from the interviews. Apart from isolated cases of Repair and Refurbish, the most common practice in this group is Remanufacture. In particular, data on production waste is constantly monitored and collected in all three cases to

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easily manage and reuse saved raw materials in new production cycles and bring new products to life. In this way, companies can prevent the disposal of production waste and keep it in use, as required by CE. Also, in the area of remanufacturing, an activity by Alpha highlighted the company’s ability to promote an efficient reverse supply chain by leveraging IoT. Similar to previous practices, this technology allows companies to access useful information about remote items located at customer sites, such as quantity and condition. By keeping track of these data, companies can determine the exact time to repurchase used goods, to promote their circularity by remanufacturing them. The same procedure has also been found in the Recycle practice.

5 Discussion and Conclusion The present work aimed to identify how DTs, specifically IoT, big data analytics and simulation, can support the effective implementation of CE practices. The results confirm those anticipated by Rejeb et al. (2022) [29] in their literature review. Indeed, to provide an early answer to our research question, we can claim that digitalisation is able to support CE practices directly. Our research extends the evidence by bringing more specificity to the relationship by considering selected DTs and CE practices. In particular, our findings show that the DTs that can contribute the most are IoT and big data analytics. Through IoT, companies can remotely and in real-time monitor product usage and status, as well as consumer behaviour, thus improving their products to contrast obsolescence and material waste, thereby enabling the transition to CE with support for refuse, rethink and reduce practices. By enhancing manufacturing efficiency and effectiveness, IoT helps extend the product life by supporting the practices of repair, refurbish, remanufacture and repurpose. Finally, IoT can also assist material tracking by contributing to endof-life product collection and waste management, fostering Recycle and closing the loop. These results are aligned with Bressanelli et al. (2018) [21], who showed how this technology could affect all aspects of circularity. In the same study, a link between IoT and big data analytics emerged, also found in our research. If IoT can offer a large amount and variety of data, provided quickly and even in real-time, analytics can transform this data into information and support decision making. Simulation appears to be the least pervasive technology in relation to the support offered to circular practices. While in the literature this technology is associated with better management of complex supply chains, remanufacturing of complex products, resource efficiency improvement or the development of new services related to maintenance [9], our results show support primarily with respect to refuse and reduce practices, while also offering the possibility of promoting the production of circular goods. Our study thus offers several practical examples of DTs application to support CE, confirming the relationship hypothesised in the literature between digitalisation and circularity and how the former can help the latter. This consistency, also taking into account the limitations of the study, does not revolutionise existing knowledge on the subject at hand. However, our work provides additional insights into the DTs-CE relationship as well as avenues for future research. First of all, looking at the implemented technologies, it is possible to say that most digital applications were not adopted from the beginning to support circularity. IoT, big data analytics and simulation were already present and

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used before circularity to improve the performances. The literature on digitalisation is exhaustive in stating how DTs are able to support performance, both productive and related to the entire organisation. In other words, DTs were not born primarily to support CE but were adapted to do so. While this may seem to have a negative connotation, it is actually not at all. This outcome shows how organisations already at an advanced level of digitalisation are easily able to enable circular models simply by leveraging and adapting the resources they already have. Second, this investigation shows where relationships exist between the specific DT and the CE practice, thus serving as a starting point for future vertical research focused on studying the relationship between a single DT and a single CE practice. 5.1 Theoretical Contributions With this work, we contribute to the extant literature by providing empirical contributions to understanding the relationship between DTs and CE, first confirming the role of digitalisation as an enabler of circular models. In addition, we showed how DTs do not necessarily have to be developed from scratch but that existing resources can be repurposed to support circular practices. This contribution opens a new research perspective on how organisations can reconfigure their resources to support CE. This concept of reuse already invokes circularity, but it is also valuable in bringing organisations that cannot afford significant new investments into the CE. Moreover, we provide an empirical contribution to the gap highlighted by Rejeb et al. (2022) [29], according to whom there is a need to further understand how IoT can enable CE by supporting the integration of other technologies. We contribute to this gap by demonstrating how DTs do not act as a standalone. On the contrary, they need to be intertwined to effectively combine the different capabilities that each technology unlocks and render data available to support the decisionmaking process. Indeed, we observe that IoT is complementary and necessary to big data analytics and illustrate the configurations under which their integration supports the CE. Further, by showing where there is a relationship between the specific DT and the CE practice, we provide a starting point for future vertical research focused on the relationship between an individual DT and an individual CE practice. Finally, our study contributes to understanding the CE-DTs relationship by showing how circularity can drive new digitalisation investments. This does not conflict with the previously expressed concept of reuse but is complementary. Precisely as with digitalisation, a scenario emerges for circularity in which organisations may reach different maturity levels, and advanced maturity in circularity may require specific new investments in digitalisation. 5.2 Practical Contributions The research has identified some key aspects of how digitalisation supports the CE. Through practical examples from different industries, this analysis has shed light on the most feasible circular practices, the necessary DTs and some avenues for value creation by integrating the two paradigms at hand.

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By having several ways of utilising DTs to support the circular practices of the 10Rs framework, companies are facilitated to comprehend which practices best fit their business model. This understanding helps them determine which practices to pursue based on their level of DTs adoption. On the other hand, the management of companies that already pursue circular practices could identify some ideas not yet explicitly known but favourable to strengthen their position along the two industrial paradigms. Lastly, by utilising the CE-DTs matrix, managers may get a general idea of how DTs should be exploited and understand when these should be employed in conjunction with one another or on their own to design the most effective value creating processes and business models. 5.3 Limitations and Future Research The present work has been conducted rigorously; nevertheless, it presents some limitations that could be addressed with future research. First, the identified results did not reach theoretical saturation since the analysis was carried out only on three case studies. Therefore, while aligned with the extant literature, the identified outcomes could depend on the business model of the companies interviewed, the industry to which they belong and their dimensions. In particular, our results refer to manufacturing companies; future research could explore the CE-DTs relationship considering service companies. For the same reasons, these aspects have not allowed us to identify some relationships between digitalisation and CE that, instead, are relevant in other sectors. Moreover, the relationship between DTs and circular practices for ‘Useful application of material’, such as the Recycle and Recover, as well as the Refurbish and Repurpose must be explored more. Further, although minor insights into these two practices have been identified in this research, additional material would be needed to deepen them. Therefore, future studies should conduct an empirical analysis on a greater number of cases belonging to different industries that could favour the identification of additional relations between the variables and finally achieve theoretical saturation of the results. Lastly, it could be worth investigating how the willingness (or need) to increase circularity can stimulate the digitalisation of organisations, allowing them to become more sustainable while improving their performance.

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Assessment Framework for Circular Supply Chains Management Towards Net Zero Targets in Limburg, the Netherlands Verena Zielke and Adriana Saraceni(B) Maastricht University, 08544 Maastricht, NJ, Netherlands [email protected]

Abstract. The aim of this research is to propose an assessment framework for Circular supply chains management towards net zero targets in the Limburg region, the Netherlands. The methodology approach provides an assessment of the province Limburg and its sustainability development. Secondary data on sustainability policies, initiatives, indicators, and assessment frameworks for sustainable cities is collected in order to derive general insights regarding circular supply chain management in more detail. Results, various practices, targets and initiatives are being assessed to be able to evaluate the circular supply chain management of Limburg. Multiple initiatives working on the concepts of circular economy, and industrial symbiosis, towards a common goal of waste reduction towards net zero are being implemented. This leads to a vast amount of direct and indirect varying indicators. Thus, this research contributes to the field of supply chain management with a collection of relevant indicators to categorize the development of multiple practices and improvements for the integration of circular economy towards a sustainable supply chain future for sustainable cities. Keywords: Circular Supply Chain · Sustainable Cities · Circular Economy · Net Zero · Sustainable Supply Chain

1 Introduction Measuring sustainability indicators in supply chain management has been a continuous hurdle for research, as many diverging actors and viewpoints need to be considered in order to create a reliable framework for utilization [1]. In the Netherlands, the Dutch government works with already existing measures towards achieving a more sustainable future for the nation, as well as its provinces, such as Limburg. These initiatives and policies originate in the sustainability development goal 11 from the United Nations [2]. The followed policies include the “European Green Deal” to make Europe climate neutral by 2050 [3], as well as the Dutch government circular economy program “Nederland Circular in 2050” [4], and regional policies such as the “Limburg Circular Economy Limburg 2.0” [5]. Within the Limburg region in the Netherlands, although many initiatives and polices are already in place, research has been scarce on how to work towards a sustainable future [1, 6]. © IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 85–99, 2023. https://doi.org/10.1007/978-3-031-43688-8_7

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The province of Limburg has drawn up the policy framework “Circular Economy Limburg 2.0” in order to work towards using raw materials sparingly and responsibly and working towards a circular economic future. Central point of this policy is to use 50% less primary raw materials by 2030 by working on rethinking, collaboration, good example, sharing and the circular agenda. They additionally mention around 5300 active circular initiatives in Limburg of the time of policy creation [5]. The “Supply Chain Valley”, “Brightlands Institute”, “Limburg Future Proof” and the “Chemelot Circular Hub” are other mentions of circular supply chain management initiatives within the Limburg region. The current initiatives still offer further development to realize the policies, as well as multiple further opportunities [7]. Most of the current initiatives, however, focus on general circular economy strategies or residential life. Policies cover all aspects of living, but do not give a clear guide to help Sustainable supply chain management (SSCM) grow towards the next level. Neither do they provide a clear framework to measure sustainability progress using indicators within supply chain management, rather only giving broad net zero thresholds as targets. This underlines the research gap of finding such sustainability indicators within the supply chain management in order to help prepare for future sustainable cities. The aim of this research is to investigate the current state of SSCM in the region of Limburg and analysing the potential benefits and challenges of circular supply chain management. This research will contribute to the global NZS CITIES Project of WUN “Towards Net Zero and Sustainable Cities with Resource Optimisation, Circular Economy and Research Network”, as well as add to the understanding of how cities and businesses can work together to create a more sustainable future. Three objectives will be addressed in the process of answering the main research question How to propose an assessment framework for Circular supply chains management towards net zero targets in the Limburg region? At first, secondary data about sustainability measures and sustainability targets of Limburg will be identified. Included in this objective is the collection of the status-quo of the chosen region in terms of sustainability, as well as the sustainability targets that have been set. Furthermore, assessment methods and tools for sustainability indicators on a local level will be reviewed within the second objective of this research. Specific focus will be set on assessment of sustainable supply chain processes. Using the results of the first and second objective, methods to analyse and help enhance circular supply chain management of the selected region will be proposed. This third objective includes propositions of existing theories of circularity in integration to the environment of the Limburg province. This research has the potential to contribute towards the development of multiple practices and improvements for the integration of circularity towards a more sustainable supply chain future for potential sustainable cities and regions around the world.

2 Literature Review Linear supply chains need to become more circular in order to achieve sustainability and resource efficiency, therefore adapting to global sustainability goals and the UN sustainability agenda [8]. The same level of sustainability targets must be applied to more local settings where the execution of circularity takes place. By keeping resources

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in use, instead of being consumed, the life cycle of materials is extended and further value streams are opened up [9]. Establishing sustainability indicators is needed to find points of contact within a supply chain to enhance their sustainability. Due to the integration of this research into the NZS CITIES project, it will be possible to find globally applicable indicators by initially conducting research on a local level and then applying it to the global scale using comparative analysis. In order to conduct necessary research on a local scale, the essential variables will be described in the following. Net Zero. Cambridge Dictionary defines Net Zero as “removing as many emissions as it produces” [8]. For sustainable cities this means a target of net zero greenhouse gas (GHG) emissions by reducing the creation of GHG, as well as generating compensating measures for the emissions created. The model of circular economy and its resource reuse supports the net zero concept, which will be made clearer in its definition to come. Net Zero therefore presents a fundamental goal behind sustainable development. Within this research and the corresponding WUN project, Net Zero represents the ultimate target sustainable cities thrive for. However, not all initiatives, policies and indicators can be measured by an achievement of the net zero target, but rather on their path towards achieving this common goal. Sustainability Indicators. Research on sustainability indicators towards net zero development has been conducted globally for various causes and research. Different research uses different indicators in order to measure sustainability depending on their goals. The European commission has gathered multiple tools from across the globe in order to access cities sustainability [9]. Some of those tools will be analyzed further within this research. Indicators and initiatives for sustainable development can be applied at different geographical scales in order to capture specific data and implementations depending on the specific goal of research [1]. The indicators selected can be weighted by choice but must be standardized in order to be comparable and to be able to run analysis. They should be robust and clear in their objective [10]. Additionally, any set of indicators is highly dependent on the set of data available, therefore what is desired may not always be feasible [9]. Supply Chain. Generally supply chain means “the system of people and organizations that are involved in getting a product from the place where it is made to customers” [8]. For the purpose of this research the focus will be set on Circular supply chains (CSC), as they represent the integration of sustainability values within classic supply chain management. CSC are defined by Dull [11] as “CSC are interconnected systems that use secondary and renewable inputs to generate value by reducing, then maximizing, resource use” [11]. This definition of CSC will be used in order to analyse indicators of sustainability within a regional supply chain. Sustainable CSC can additionally help gain competitive advantages and reduce entropy of materials [12]. The concept of circular economy fits into Dull’s definition of CSC, even though supply chains cannot be technically circular, it’s “product” can be [12], and will be quickly explained in the following. Circular Economy and Industrial Symbiosis. The general principles of circular economy are to eliminate waste and pollution, circulate products and materials, and regenerate natural systems [13]. This can be concluded as making waste into money by seeing and

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creating a value stream in waste [11]. By turning goods that are at the end of their service life into resources for another service life, a loop of an industrial ecosystem will be created [14]. This in addition will reduce the environmental footprint of the actors [15]. Coming from the traditional linear economy of creating products for sale and using resources only once, the concept of circular economy sees consumers becoming users, as products in a circular economy are used and not consumed [11]. Circular economy brings not only new value streams, but also additional benefits such as creating jobs or saving energy and reducing carbon emissions through time saving, flexibility, leaner processes, connectivity and predictivity [11]. Additionally, the benefits of the reduction of consumption and waste by using renewable and secondary materials are given by circular economy [14]. Recycling represents thereby only the last resort of circular economy, as products are rather reused, refurbished or repaired [14]. However, the current market still needs to adapt in order for circular economy models to be able to thrive in their high potential, as supply streams of circular and sustainable developments need to be created [15]. Here, the concept of industrial symbiosis comes up as part of enabling and accomplishing circular economy. Industrial symbiosis can be achieved by combining resource streams by partnerships between different actors [16]. Industrial symbiosis is commonly defined under industrial ecology [17], where each results of a process becomes a component of a new interconnected ecosystem in form of new raw materials for others [16]. First defined by Chertow [17] as a “place-based exchanges among different entities […] for a collective benefit greater than the sum of individual benefits that could be achieved by acting alone”, industrial symbiosis is a mutualism based on collaboration. The relationship between different companies is of high importance as collaboration and geographic proximity build the key aspects of any industrial symbiosis [16]. The public actor can (and should) be included in industrial symbiosis systems in order to manage on an organizational level [18]. This public actor can be the national government, the regional legislation, as well as municipalities of cities thriving for a sustainable future. Another version of sustainable development would the performance economy, where the product created is merely a service being provided [14]. An analysis of how the concepts of circular economy help the circular supply chain management towards more sustainable processes, will be conducted within this study. Sustainable Cities. Cities who thrive towards the common net zero goal by upgrading themselves with technologies and expertise in order to gain a more sustainable environment are considered sustainable cities [10]. By managing resources of an urban area more efficiently and ecologically regenerating, the wellbeing of the city itself, as well as their residents, will be provided long-term [19]. In order to achieve more sustainable cities, circular economy and its sustainable development should be adapted and integrated within cities supply chain. Cities practicing circular economy are often referred to as circular cities, but sustainable cities are used as a target variable in this paper. Minimum research has been done on the topic of sustainable cities as net zero cities, opening the gap of research for this and future projects [10]. Conceptual Model of Research. From the literature review the lack of sustainability in current linear supply chains becomes visible. Whereas, circular supply chains, on the other hand, perform more sustainable. The research gap is revealed as the lack of

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quantitative comparability in this transformation from linearity to circularity in order to validate the net zero targets are reached. The conceptual model of this research is presented in Fig. 1 and will help in answering the research question by covering the current situation and necessary variables towards net zero cities in the Limburg region by propositioning circularity within the supply chain management.

Fig. 1. Conceptual Model

The conceptual model guides the path from linear towards circularity in supply chains in order to reach net zero targets, thereby transforming urban cities into sustainable cities. The specific sustainability indicators GHG emissions, waste and energy, heavily influence the change to SSCM as circularity variables. The selection of the three sustainability indicators used in this research will be further elaborated in Sect. 5. They depend on the importance within initiatives, policies and frameworks and availability of data towards net zero targets. SSCM further follows The integration of circularity into supply chains, by following concepts of circular economy and resource efficiency, will be elaborated further by answering the research question within the analysis of Limburg.

3 Methodology This review method focuses on research outcomes and Selection of Region, practices or applications [24, 25]. As a first step, a selection of analysed local region allows an analysis of specific sustainability indicators, has relevant available data and initiatives towards sustainable development. This data may include reports and targets on sustainable development, carbon footprint, net zero, emissions, waste, circular economy, industrial symbiosis or innovation in SSCM. The province of Limburg has been chosen as the local region for this research. The biggest municipalities in Limburg are Maastricht and Venlo, which play a crucial role in the sustainable development of the province. The Limburg region of the Netherlands hosts 1.115.872 inhabitants in 2021 and scores at 116% GDP per inhabitant in purchasing power standard of EU27 average (Eurostat). Maastricht is the provincial capital of Limburg with 122.473 inhabitants in 2017 (Eurostat).

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Data Collection. The data collection process follows multiple sources in order to gather a comprehensive overview on theory and measurable data. The data collection process can be split up into two separate steps. Step 1: With the intention to build a foundation, a literature search using the research database of Google Scholar has been conducted. After thorough filtering of the results on keywords, abstract and content, a forward and backward search was performed to include cited content. Step 2: Sources of secondary data collection may include different databases, scientific literature, reports, government documents, books, public records [20]. In order to assess the found secondary data, the results of the search are evaluated and filtered by the research question and relevance [21]. As the results of the secondary data collection are not always in the English language, Dutch references are translated into English using Google Translator. The collected data will then be analysed and current active measures, initiatives, policies and frameworks will be discussed. • Assessment Tools of Sustainable Development: To further analyze the sustainable development of Limburg, existing assessment tools for sustainability indicators of (sustainable) cities are collected. Assessment tools include national and international standards, previous research, as well as private and corporate creations. The sustainability and circular indicators within these assessment tools will be collected and analysed. • Analysis of Assessment Tools: Multiple assessment tools are now assessed on relevance in a small regional setting and on their identified indicators. A selection of common and important indicators will be conducted. This analysis focusses on circular and SSCM in a regional context. The results of the analyses are being thoroughly discussed in order to draw conclusions on sustainable development assessment tools being used for the regional setting. Proposing an Assessment Framework for CSC Management Towards Net Zero Targets in Limburg: To elaborate on the assessment framework, the selected region is analysed based on the sustainability initiatives, frameworks and indicators available and collected in the assessment tools. The analysis of region is based on the findings of the data collection. The disclosed data is used to discuss the current status in the regions on general sustainability performance and achievements within the supply chain in relation to their own targets and targets of national and provincial level. Initiatives in the selected region on their sustainable development within the supply chain management will be presented in order to classify sustainable development relevant for CSC of a local level later on.

4 Data Collection The statistic websites of the Dutch government (CBS, Statline, Klimaatmonitor) collect and supply data on multiple indicators regarding sustainability on provincial level. The following data has been collected in order to create a comprehensive overview of the

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sustainable development of Limburg. Regional initiatives of circular supply chain management are stated in Table 1 in the Appendix. A rough overview of initiatives involved in sustainable and circular supply chain management within the province of Limburg, as well as the municipalities Maastricht and Venlo, is presented. Sustainability indicators in connection with their targets are presented in Table 2 in the Appendix. This data includes targets from regional, national and European level, which Limburg has adapted to their region. Indicators presented show available data in the Limburg region in multiple topics, which are not necessarily covered by policies. A full overview of all collected indicator data can be found in the Appendix. Table 3 In the Appendix displays collected assessment tools for regional sustainable development. The tools cover circular economy or regional sustainable city development from multiple sources. Only assessment tools regarding the assessment of circularity in supply chains on a regional level are collected. The most prominent and relevant indicators for the analysis in this research out of these assessment tools are presented. The findings, as well as further development over multiple years (2010–2021) on sustainability measures, initiatives and targets of Limburg will be put into context with assessment frameworks of local sustainable development. The province does not report their sustainability development in annual reports and even the biggest cities within the province do not report publicly available city sustainability measures and processes on their official websites (gemeentemaastricht.nl and venlo.nl). (Global) Data collection organizations (e.g. CBS, StatLine, CDP, Eurostat) do not provide a vast amount of data on smaller regions other than national level. A vast amount of available sustainability (climate) data is not reliable over time with multiple gaps [22], especially on the municipal level. Eurostat, the official statistical office of the European Union, owns datasets of European countries and regions but does not provide enough environmental or relevant economic data on provincial level to derive sustainable city indicators [23]. The datasets of the climate monitor from Rijksoverheid and of StatLine by CBS build the most comprehensive source of relevant data. The available data is thoroughly examined, and relevant data is selected. In order to analyse the supply chain management, circular and sustainable economy and net zero initiatives of the region, data on waste, CO2 emission, electricity usage, energy usage, natural Gas usage, GHG emissions and renewable powers is collected. 4.1 Assessment Tools of Sustainable Development The European Commission provides a list of tools for sustainability city indicators concerning European cities [9]. In regard of circular supply chain management we are interested in measures on resource efficiency, circulating materials, waste management, emission reduction and elimination [11, 14]. The sustainability development goal 11 of the United Nations is the main originator for sustainable development frameworks and reporting, providing 231 unique indicators [2, 24]. The “European Framework” by RFSC presents 30 sustainable objectives. The main goal is to foster integrated urban development for small, medium and big cities across Europe. The framework attacks five dimensions and gives regions a guidance and indicators on what to include in their sustainable development management, without stating direct units of targets themselves, similar to the SDG 11. Relevant indicators to target sustainable supply chains cover the areas emissions, air, waste, energy, and waste [25]. The “Urban Sustainability in Europe”

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by the European Environmental Agency collects indicators from other frameworks and similarly orders them in multiple dimensions and views [26]. The “European Green Capital Award” and “European Green City Tool” provide a set of indicators for cities and their sustainable development. These indicators include air quality, greenhouse gas emissions, renewable sources, and energy efficiency. Other indicators cover noise, water cleanliness and land use, all leading towards urban ecosystems and biodiversity [3, 27]. The “European Green Leaf Award” belongs to the “European Green Capital Award” but focusses on smaller cities by adapting the indicators accordingly. The “European Cities Index” is a framework applied to multiple European cities; however, it has not been applied to Limburg. They also include similar indicators covering emissions, air, waste, energy, and water [28]. The province of Limburg declares some strategies that can be used to measure circular economy and offers their own framework in the form of action lines instead of numeric indicators: Think, Cooperation, Sample, Share, Circular agenda [5]. The International Organisation for Standardisation (ISO) covers multiple standards for organisations like companies, but mostly for governing agencies on sustainable cities. Additionally, there are Standards from the Global Reporting Initiative (GRI) on sustainability reporting for organizations [29]. Within the mentioned GRI standards the main indicators of circularity, as previously mentioned, are covered and specific guidelines in units and calculations are given. More assessment tools can be derived from previous research and case studies across the globe. Dufourmont [30] covers multiple circular economy assessment tools from different sources in their thesis on circular economy in European cities. The Ellen MacArthur Foundation “Material Circularity Indicator” helps measure aggregate circularity of a product or business with the focus on education, business and government, insight and analysis and communications [31]. CBSs “Monitor Sustainable Netherlands” covers quality of life, resources and comparison to foreign countries, as well as the “Green Growth Indicators” on environmental efficiency, resource efficiency, natural asset base, environmental quality of life, green policy instruments, economic opportunities [30]. Van Leeuwen et al. [32] proposes a framework which includes the setting of (or selecting) indicators, scoring them in a quantitative matter on a numeric scale, then using publicly available data in order to calculate results that are easily understandable for everyone [32]. Abu-Rayash & Dincer [10] propose their own framework which includes weighting and aggregation of variables, robustness and sensitivity, linkage to other variables. Their framework provides a tool on how to analyse found indicators in an individual setting [10]. A framework, also based on the weighing of indicators, is suggested by Gan et al. [33]. As indicator selection and integration is very subjective, weighing and aggregating is important for different dimensions and their substitutability, and the impact of each indicator needs to be considered. Indicators should therefore be weighted depending on goals, targets, scale of the research conducted and type of sustainability [33]. Reed et al. [34] summarises best practise from literature on assessing sustainability indicators at a local scale. They consider quantitative and qualitative indicators and use of top down or bottom up approach to define and assess sustainable indicator frameworks [34]. Analysis of Assessment Tools.

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Many assessment tools are investigating sustainability indicators exists nowadays. However, not all of them can be applied in the context of this research. The subject of an assessment tool depends on the use case it is being applied to. The SDG11 fits in line with the goal of applying sustainability measure more on the local level by using concepts of circularity and symbiosis. The “Green City Tool” is a direct contributor towards a possible industrial symbiosis. The “European Green City Tool” can be applied to Limburg as there is some data available on emissions, renewable data, energy efficiency and waste. A regional project in Limburg uses the INSURE method to analyse causes of unsustainability. They argue, that one framework does not fit any region in order to evaluate and compare local influences with each other [1]. The steps of creating a new framework can be utilized in order to create a provincial sustainable development framework for Limburg. This includes establishing context, setting goals and strategies, identifying indicators and monitoring iterative sustainability assessment cycle [1, 34].

5 Results and Discussion 5.1 Assessment Framework for CSC Management Towards Net Zero Targets To propose an Assessment framework for CSC management towards net zero targets in Limburg, the findings of Limburg will be discussed and analysed in its relevance to CSC. This includes information on sustainability targets and policies in the region, available environmental data and relevant initiatives regarding circularity and sustainability. Figure 2 Presents the Assessment framework for CSC management towards net zero targets in Limburg.

Fig. 2. Assessment framework for CSC management towards net zero targets in Limburg

Policies and Targets: The initiatives and policies followed by the province of Limburg include the “European Green Deal” to make Europe climate neutral by 2050 [3], as well as the Dutch government circular economy program “Nederland Circulair in 2050” [4]. Waste policies on European and national level have been set as well [4]. Limburg’s own targets, policies and initiatives directly build on these national and international regulations. The “Circular Economy Limburg 2.0” [5] is in accordance with the “European Green Deal” and “Regional Innovation Strategy of South Netherlands” and sets the same

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sustainability targets and circular economy goals. Limburg is additionally following the “National Climate Agreement” and “Climate Agenda” [35]. Especially action line 7 is interesting in this context, as it covers the use of materials and a sustainable industry and expects a more efficient and circular material use, reduction of waste, emissions and carbon footprint by increasing sustainable products. The “Regional Energy Strategy” region South Limburg [35] is set in place and argues municipalities should be working with residents, companies and social organisations towards sustainability. The “Limburg Waste Cooperation” (ASL) focusses on management of circular household waste, including recycling and sorting of waste [36]. The Netherlands are leading performers in their waste policies on household waste in Europe and even set a more ambitious target than the EU does in recycling rate [5]. General targets of all sources cover the reduction of use of raw materials and replacing it with an increase in renewable raw materials, reduction of CO2 emissions and waste reduction whilst reducing emissions. However, not many targets are stated in numerical units, but in a more general sense by thriving for reduction, mitigation, circularity, collaboration or effort towards a common goal of being more sustainable. Initiatives. Multiple companies within Limburg work on circular concepts and initiatives in order to not only reach the sustainability and economic targets but to increase their own efficiency of their supply chains. The most notable initiative towards circular or sustainable supply chains in Limburg is the Supply Chain Valley, of which the Gemeente Venlo, port of Venlo, province Limburg, Maastricht University and BISCI are some of the stakeholders [37]. The Supply Chain Valley and its stakeholders present significant work in all parts of the sustainable supply chain development of cities using the general concept of collaboration, just like industrial symbiosis does [37]. BISCI is the Brightlands Institute of Supply Chain Innovation, and part of “Brightlands” and Maastricht University, focussing on smart and sustainable supply chains of the future [38]. “BISCI” is following the principles of CSC by developing innovations towards sustainable supply chains. Limburg is heavily involved in “Brightlands” and “BISCI”, boosting the regional economy with innovation in sustainable supply chains and providing the biggest active governing initiative for the province. The “Chemelot Industrial Site” and “Circular Hub” concentrate on circular use of raw materials within the industry sector [39]. They are an example of active industrial symbiosis within the Limburg region. On another note, LIOF, the regional development company of the province of Limburg, uses the program “Limburg future proof” in order to help local companies on their journey towards circularity and integrating them into the net zero policies of the province [40]. The environmental zone in Maastricht is a start towards the zero-emission city logistics goal. Similarly the gemeente Maastricht thrives to have net zero emission vans and lorries in the city centre from 2025 onwards [41]. “Maastricht for climate” is a private initiative and climate movement focussing on climate justice. This movement focusses on an individual and political level. On economic level, the movement wants to support local business in their sustainability journey, which could be facilitated using circular concepts or industrial symbiosis, and resource sharing. Additionally, they are calling out the Municipality for procrastination on their climate goals and encourage everyone to take more action in the regional energy strategy [42]. This aligns with the city vision

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of Maastricht to focus more heavily on community work and integration [43]. Public surveys, working sessions and public opinions are used to created ideas, goals and ways to make Maastricht advance services for their population, but also evolve economically and work against climate change. However, there is not much economic value as it lacks the participation and implementation of companies at the moment. One major example of a sustainability initiative in the city of Venlo is the newly build city hall of Venlo, which is a frontrunner in circular Cradle to Cradle principles. Here, the stakeholders did not possess enough knowledge about the sustainable and circular principles beforehand, but were able to learn first-hand. This project shows the importance of knowledge distribution on circular developments [6, 44]. The municipality of Maastricht is working on multiple strategies, projects and programmes, in order to reach the ‘Zero waste Maastricht 2030’ and ‘Climate neutral city in 2050’ ambition. This is in line with the targets set by the government, of adapting circular economy in the form of material use and sustainability targets, within the coalition agreement [45]. These are just a few of many small initiatives being done by small and big companies, as well as private individuals. Not all initiatives are in the public eye or reach a big enough scale to impact the province. Frameworks: Investing in renewable sources leads to reducing CO2 emissions, which the data on Limburg proves. CO2 emissions are part of the waste output, as they are an end product and cannot be reused. CO2 emissions are the most common direct indicator stated in frameworks and policies. However, the value of reduction is rarely given. 50% reduction of primary raw materials covers fossil fuels, and therefore the CO2 emissions generated from them. Therefore the 6% reduction of CO2 emission in the Netherlands are a small progress towards that target. We do see a similar small trend in the reduction of CO2 emission in Limburg. However, the exact reduction of CO2 emissions from 1990 (in line with the target) within Limburg cannot be calculated with the presented data of Limburg. Reduction rates of other GHG emissions are also not available for Limburg. However, all greenhouse gas emissions of Limburg show a downward trend in the past. Indicators: According to Dull [11] the use of renewables and other secondary materials are the key input in CSC. Likewise, is the reduction and reuse of resources a main principle of CSC and circular economy. This includes per definition waste and its subcategories of emissions as resources to reduce. The indicators that will be looked at further therefore cover energy and electricity usage, renewable sources, waste and emissions. All of those indicators are clearly mentioned in the policies of governments, international agencies, as well as initiatives within the province of Limburg. Even though the targets are not always stated in measurable values, the indicators are part of circular economy and industrial symbiosis. Energy and Electricity are a common input in supply chain processes, which produces emissions as the energy is mostly carried by fossil fuels. Renewable sources provide a more sustainable alternative to create and use energy within supply chains and reduce waste. Renewable energy is an already existing sustainable alternative energy source, which plays a major part in future sustainable and circular development as a cleaner production of energy which companies can create an their own. Even though the usage of renewable energy has increased (18,7% growth in 2021), the percentage of renewable energy is at 6,1% and not close to the European Green City Tool target of 27% and

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Dutch target of 16% by 2023. By just looking at electricity (32% growth in 2021), the percentage of renewable electricity is already at 17,2%, however, here the national target lies at 100% by 2050. The indicators show that Limburg is applying renewable alternatives to energy and electricity production, nevertheless they are not reaching the set targets. Circularity concepts and applications become relevant in order to reach the targets set. As sustainable CSC use renewable sources, reduce waste, and would therefore help increase percentages of renewable sources for the Limburg province. As already discusses, emissions are categorized into the waste dimension. For a sustainable future, the amount of waste needs to be reduced, in line with resource efficiency and indicators from policies and frameworks. Not only the total amount of household and non-household waste plays a crucial role in waste management, but also the recycling and sorting rates are two common indicators. In a circular economy the recycling of materials should be kept to a minimum and materials need to be reused or repaired first [5]. Sorting rates provide information on materials that can be recycled or reused, instead of being considered waste. The sorting rate of 23% in Limburg has more than doubled in the last 10 years, compared to the recycling rate, which has only increased by 2% in the last 10 years, to 37% (2021). The values for household waste do not significantly vary from the total waste values. As companies included in possible circular development produce waste, usually at the end of their linear supply chains, a circular supply chain management aims to get rid of this waste output by using the right resources efficiently. Additionally, the common net zero target directly covers emissions and waste and presents the general aim to produce zero end product emissions, which all sustainable developments follow. These indicators and guidelines all progress towards the mutual main goal to reduce the environmental footprint and create sustainable cities.

5.2 Limitation and Further Research The biggest limitation of this research is the lack of local data. Even though the Netherlands are frontrunners in sustainable development and circular economy, the relevant data is still insufficient on regional context. On city level, the availability of data lacks consistency in order to create accurate analyses. It is unclear whether missing data is not available due to lack of records or documentation. Both options need to be explored. In terms of initiatives and frameworks, this research is limited by publicly available free sources, even though there are possibly many more frameworks by companies on their own internal sustainable development. By including stakeholders’ points of views in the research, internal initiatives and implementation of sustainability can be revealed in order to see the full picture of circular SCM of Limburg. The sets of indicators of sustainable development in general, but especially on circular and SSCMsustainable SCM, have evolved and will keep on evolving over time. This correlates with the amount and variety of gathered data, which will change the view and handling of SSCMsustainable SCM in the future [11]. This paper focusses only on the regional level and implementation of SSCM, which can only be applied to the global context with similar conditions [9]. Due to this continuous change and evolvement of sustainability knowledge its analysis will always be a limited insight into the research matter.

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6 Conclusion This research proposed an assessment framework for Circular supply chains management towards net zero targets in Limburg. By assembling the initiatives, frameworks and indicators relevant to the region, this research investigated the integration of circularity and sustainability indicators as a means to advance supply chains towards achieving net zero emissions in the Limburg. Sustainability indicators provide insights for a comprehensive framework for measuring and monitoring environmental and economic performance of supply chains. By adopting circular practises, businesses can effectively reduce their environmental footprint and contribute to the region’s sustainability targets. The integration of circularity requires collaboration and engagement among various stakeholders, including businesses, government entities, academia, and civil society, and it will be a step to move further within the NZS CITIES project development. Moreover, this research unfolds the importance of fostering partnerships and knowledge sharing to leverage collective expertise, resources and innovative solution through collaboration. Collaborative initiatives can facilitate the exchange of best practises, promote technological advancements, and create a supportive ecosystem for sustainable supply chains. This research contributes to the existing knowledge gap that is occurring within region setting, by proving valuable insights and recommendation for policymakers, business and other stakeholders seeking and needing to advance sustainability and circularity in supply chains on their journey towards net zero. This paper focusses on the regional level and implementation of sustainable SCM, which can only be applied to the global context with similar conditions and might not be applicable to other small regions. However, Limburg is a promising province at the start of sustainable development and circular SCM with a promising future in sustainable and circular development ahead. Acknowledgements. The authors would like to thank the support of Worldwide University Networks (WUN) collaboration – 2022 Award, Research Development Fund (RDF), under the project “Towards Net Zero and Sustainable Cities with Resource Optimization, Circular Economy and Research Network (NZS CITIES)”.

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Stakeholder Management in Circular Economy Product Development in the Mining Industry – A Case Study Juhoantti Köpman1(B) , Vesa-Matti Leiviskä2 , Harri Haapasalo1 Petteri Annunen1 , and Jukka Majava1

,

1 Industrial Engineering and Management Research Unit, University of Oulu, Oulu, Finland

[email protected] 2 Tapojärvi Oy, Tornio, Finland

Abstract. Developing circular economy products involves a wide array of stakeholders that affect different phases of the product development process. This article studies stakeholder management in circular economy product development by conducting a case study on a circular materials producer in the mining industry. The key stakeholders were identified, categorized, and the stakeholder relationship between them and the case company was analyzed. The results show that different types of stakeholders exert their influence in different phases of the product development process. Furthermore, the need for more systematic ways of managing different types of stakeholders in circular economy product development was revealed. Keywords: circular economy · stakeholder management · product development · mining

1 Introduction The looming threats of insufficiently sustainable production and limited material availability have sparked interest to find new, more environmentally friendly production methods in the mining industry. Circular economy (CE) is an umbrella term for various methods that aim to keep materials in useful circulation for a longer time compared to traditional linear production methods, where materials are used once in products and then discarded by consumers or industries [1]. CE methods include ways to extend the lifespan of products, for example by repairing or remanufacturing, as well as reusing materials effectively by recycling products after the end of their useful lifespan. These methods keep materials in circulation and decrease the amount of waste generated by industries and communities. Industrial side streams, such as mining rejects and steelmaking slag, can also be utilized in CE to replace virgin raw materials to decrease the environmental impact of industries [2]. Transitioning from traditional linear supply chain models to a CE based supply chain does not come without challenges: traditional models can presently offer more © IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 100–114, 2023. https://doi.org/10.1007/978-3-031-43688-8_8

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lucrative business propositions [3], circular materials might suffer from contamination from previous use or sub-par processing [4], as well as possible social and environmental risks when handling hazardous materials [5]. The literature on the product development (PD) of CE products has been expanding steadily. A search on the Scopus database shows that the number of articles published per year containing the search terms “circular economy” and “product development” in their title, abstract, or keywords has been rising annually since 2014, peaking at 45 articles in 2022. However, little research exists on the stakeholder management of the PD activities of companies focused on producing CE products. This study expands previous literature by studying stakeholder management in the PD of circular products made from industrial side streams. A case study of a company and its immediate stakeholders was conducted to study the connections between the stakeholders’ roles, their salience, and assessed impact on projects in defined parts of the PD process. Previous studies on stakeholder management and CE have focused, for example, on large-scale data and its implications to CE [6] and the effects of taxation reforms on CE stakeholders [7]. This study focuses on the management of PD stakeholders, such as material suppliers, customers, and actors directly involved in the PD process. To understand the network of stakeholders involved in PD, it is important to identify and categorize them. This is done by using frameworks developed in stakeholder management literature and analyzing the data collected from the conducted interviews. The case company’s stakeholder management activities are then analyzed and relevant stakeholders’ roles in PD activities are studied. Thus, research question (RQ) 1 is set as follows: RQ1: How are stakeholders currently being managed in a CE company’s PD? After the current state of stakeholder management in the studied case has been analyzed, it can be compared to literature and a model of how stakeholders should be managed in CE PD. This model answers to RQ2: RQ2: How should different types of stakeholders be managed in CE PD?

2 Literature Review Circular Economy (CE) principles are guidelines that aim to prolong the usable lifespan of products and keep the materials invested in them in circulation instead of discarding them [8]. In practice, this means using R-methods such as repairing, reconditioning, and remanufacturing products to keep them in circulation [9], minimizing the creation of waste by productizing industrial byproducts, and recycling end-of-life products [10]. Productizing industrial by-products requires that the products generated are environmentally safe and able to perform in their applications in the same way as traditional products made from virgin materials. Product development (PD) is the process of turning ideas into sellable products. Cooper [11] introduced the stage-gate model of PD, which includes five stages - preliminary assessment, detailed investigation, development, testing & validation, and full production & market launch, with gates separating each stage. The importance of product development activities for CE cannot be overstated as up to 80% of a products’

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lifetime environmental impacts is determined in the design phase [12]. Prior research suggests that there is a link between businesses’ ability to develop environmentally sustainable products and having systematic PD processes with strategic focus and project planning. The lack of those capabilities seems to decrease businesses’ ability to adopt environmental consideration into their PD [13, 14]. Furthermore, companies that are sustainability-oriented are not only more likely to develop new products sustainably, but they also manage industrial by-products more effectively in line with CE [15]. Stakeholder management is the act of recognizing the actors that are affected by or can affect the goals of the organization and utilizing managerial actions to reach those goals [16, 17]. Stakeholder management is essential in transitioning to CE, as interconnected systems in CE usually include several actors with conflicting interests [18, 19]. Often these systems include actors from industries, communities, and the public sector [20]. The resulting network of stakeholders with a wide range of interests and needs, which can sometimes conflict each other, can be challenging to manage. Mitchell et al. [16] introduced a model for categorizing stakeholders by three salience attributes: power, legitimacy, and urgency. Salience can be described as: “[…] the degree to which managers give priority to competing stakeholder claims[…]” and the three attributes describe the source of given priority [16]. This model proposes eight groups of stakeholders that describes them depending on the combination of salience attributes present. This model was modified by Aapaoja & Haapasalo [21] to create a stakeholder assessment matrix, which also takes the probability to impact or ability to contribute to a project into account. The model categorizes stakeholders into those that require minimum effort, those to keep satisfied or informed and stakeholders that can be considered as key players. The model also addresses the probability of stakeholders contributing positively to the goals of an organization. Previous studies have also shown a link between environmental performance and stakeholder management capabilities in both large enterprises and small-to-medium sized enterprises (SME). Buysse & Verbeke [22] found a strong link between active stakeholder management practices and active environmental strategies mainly with primary stakeholders, such as employees, customers, shareholders, and suppliers. A weaker link was also found with secondary stakeholders, such as environmental nongovernmental organizations, the media, or industry rivals. Aragón-Correa et al. [23] found that stakeholder management capabilities in general correlated positively with the development of proactive environmental strategies in SME’s. Lieder & Rashid [24] suggested that for a wide implementation of CE, a mixed approach for different types of stakeholders should be considered to achieve a compromise between regulative control and business realities. A top-down approach with rules and regulations, was recommended for legitimate and urgent stakeholders, such as society and policymakers. In contrast, a bottom-up approach, where business realities guide actions, was recommended for powerful stakeholders, such as supply chain, collaborative business, or product design stakeholders. [24].

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Literature shows that intentional early involvement of relevant stakeholders can benefit the whole PD process by addressing their claims ahead of time, instead of adapting PD at a later stage, which can result in cost and time overruns and inferior quality [25–27]. In other words, early involvement is critical in balancing and prioritizing stakeholders’ requirements to enable optimal outcomes in PD.

3 Method and Data The premise of this study is based on the need to identify how different stakeholders are involved in CE PD processes and what roles they have in it. A case study methodology with semi-structured interviews was selected to study the phenomenon in a real-life context and enable the use of empirical data from businesses [28]. Thus, the unit of analysis of this study is a typical PD process in the case company. The case company was chosen for its progressive approach to CE and openness to development and innovations. Furthermore, it is involved in multiple PD projects with several stakeholders to productize industrial side-streams by creating new CE-based materials. The study began with analyzing relevant literature in the field and compiling a short review (Sect. 2) using narrative literature review and snowballing methods [29, 30]. Then a workshop was held with the case company to identify relevant and typical stakeholders in the company’s PD process. The identified stakeholders were then interviewed utilizing semi-structured interviews that were transcribed and analyzed. The stakeholders were analyzed based on their interactions with the case company’s PD phase(s) they are involved in [11], the saliences of the stakeholders [16] and their categorization in the stakeholder assessment matrix [19]. The PD model used in the analysis was chosen for its universality, as the diversity of projects and ongoing developments required a generalized model. The salience model is used to determine stakeholders’ attributes in their relation to the case company’s PD, and to determine the main salience attribute of each stakeholder. The stakeholder assessment matrix is used to form a deeper understanding of the involvement of the stakeholders in the case company’s PD. The case company of this study, Company A, is a Finnish mining and industrial services provider that produces circular solutions. Company A conducts projects, where virgin raw materials are replaced with products derived from mining and industrial side streams. Its aim is to reduce the need for extracting natural resources and side stream landfilling. Company A is involved in CE projects in many industries including mining, ore processing, and steelmaking. The empirical data consists of 10 semi-structured interviews of Company A’s internal and external stakeholders (Table 1). The interview questionnaire was composed of questions concerning the background, requirements, participation, and future goals in the relationship between the stakeholder and Company A’s PD. Choosing the key stakeholders was done in collaboration with the representatives of Company A. Thus, the selection of interviewed stakeholders reflects the state of stakeholder management in Company A and functions as a point of analysis in this study.

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Stakeholder

Type of stakeholder

Interviewee title

Role in Company A’s PD

Company A

CE mining and industrial Operations development Operations development services company manager in Company A

Company A

CE mining and industrial R&D manager services company

Managing R&D at Company A

Stakeholder A Environmental consulting company

CEO

Environmental impact assessment consulting for new projects, industrial side stream PD

Stakeholder B CE hub

Director of CE center

Provides aid in CE projects (funding, contacts with businesses and authorities)

Stakeholder C Mining company

Construction manager

Supplies concrete for mining, CE PD with Company A

Stakeholder D Environmental consulting company

Development director

Provides R&D services for Company A

Stakeholder E Mining company

Consultant

Geopolymer development for mining

Stakeholder F Governmental road and transport agency

External project manager

Consults Stakeholder F in possibilities of including circular materials into F’s portfolio

Stakeholder G Mining company

Mining engineer

Subsidiary of Company A, PD roles in geopolymer development

Stakeholder H CE mining and industrial Production manager services group

Subsidiary of Company A, subcontractor for Stakeholder C for tunneling

4 Results The interviews were analyzed based on the stakeholders’ involvement in Company A’s PD process, their salience attributes [16] and probability to impact or ability contribute, which defines their category in the stakeholder assessment [21]. Also, the PD phases they are involved in were determined [11].

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4.1 Company A The R&D manager is responsible for all PD activities in Company A, making him also the most notable stakeholder in both salience and probability to impact or ability contribute. Power is the most prominent salience attribute of the R&D manager, but he also possesses urgency and legitimacy through being responsible for the whole PD process and its results. Thus, the R&D manager is considered a key stakeholder in PD. The operations development manager is an internal stakeholder through facilitating the continuous improvement of PD activities. The operations development manager has also collaborated with PD teams in developing systematic PD processes, which were not previously established. Thus, the operations development manager collaborates with the whole PD process and sets requirements for improving it. As the operations development manager can impose requirements on the PD process, power is the primary attribute in the stakeholder relationship. Even though other salience attributes are not present, the operations development manager is likely to affect PD, so it can be considered a stakeholder that should be kept informed. 4.2 Stakeholder A Stakeholder A is an environmental consulting company working with Company A in environmental permit processes for circular products. Stakeholder A works in projects mainly as a consultant, but also funds projects on a small scale. The collaboration with Company A mainly focuses on environmental permits, but its wide scope in environmental consulting services makes it possible to expand the collaboration. For example, Stakeholder A can partner with Company A in refining industrial side-streams into circular products. As an environmental permit is required for new CE products, Stakeholder A is an important collaborator in getting the products to market and affects the PD process from the preliminary assessment to the development phase. Stakeholder A imposes requirements to Company A’s PD regarding the standards towards environmental effects, product specifications, materials, manufacturing, and cost. From Stakeholder A’s point of view, environmental permit applications for waste-based circular products need to address these factors except for cost, which is a key business requirement to make the solution financially viable. As Stakeholder A is conducting the work on the permits, it also imposes requirements for making sure the terms to receive the permit are met. As a stakeholder to Company A’s PD, Stakeholder A holds power as its main attribute, as it holds knowledge that currently must be outsourced. Furthermore, it also possesses legitimacy, as Stakeholder A sets conditions for the collaboration that coincide with legislative requirements for CE products. Stakeholder A does not, however, possess urgency. Thus, it can be considered a dominant stakeholder with a moderate probability to impact or ability to contribute to Company A’s PD, which makes them a stakeholder that should be kept satisfied. 4.3 Stakeholder B Stakeholder B is a CE hub providing services to Company A in CE projects by assisting in finding funding and contacts as well as consulting in environmental permits. In practice, Stakeholder B assists Company A in projects when needed by helping in finding

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suitable companies or contacts to partner with as well as general ideation and development in PD projects. The type of collaboration depends on the project at hand, as Stakeholder B helps with issues that may come up in PD, but it is not directly involved in the PD process. Currently the collaboration is limited to the preliminary assessment and development phases. According to the interviewee, Company A possesses the ability to conduct most of the PD work in-house, and Stakeholder B’s services are not systematically needed. Stakeholder B does not impose any direct requirements for the collaboration with Company A, as every project is unique, and requirements vary. As a stakeholder, Stakeholder B has interests that are aligned with those of Company A and aims to work with it to enable success in different projects. Its salience over Company A’s PD can be considered low, as it has limited power and urgency over the PD process. However, Stakeholder B does possess legitimacy, as it can supply Company A with information about possible contacts and help with legislative requirements and issues. Thus, it can be considered a discretionary stakeholder. The probability to impact or ability contribute Company A PD is quite high, so Stakeholder B can be considered a stakeholder that should be kept informed. 4.4 Stakeholder C Stakeholder C is a mining company that operates in the steel industry. The interviewee is the head of a concrete factory that supplies materials for mining projects. Company A has a subsidiary, Stakeholder H, which is doing tunneling for one of Stakeholder C’s mines and utilizes the concrete supplied by the Stakeholder C’s concrete factory. Thus, the collaboration is currently limited to the full production and launch phase of PD. Stakeholder C is not directly involved in Company A’s PD but produces a significant amount of mining waste that can be turned into CE products. However, the mining waste is currently being recycled into roadbuilding materials and concrete components and the potential for further refining was not recognized in the interview. Stakeholder C does not have any direct requirements for Company A, as it is working in the role of supplier. However, stakeholder C sets requirements for the quality of concrete being produced. This, in return, affects Company A, who uses the material. The salience of Stakeholder C can be considered low, as it is not directly involved in Company A’s PD activities. However, it holds power, as it is a large mining company currently working with Company A’s subsidiary. As such, Stakeholder C can be considered a dormant stakeholder. Future projects might change the situation, as the amount of side-streams Stakeholder C produces is significant and their productization into CE products might give Stakeholder C significant legitimacy and urgency. Currently, however, its probability to impact Company A’s PD is low, and thus as a stakeholder it requires minimum effort. 4.5 Stakeholder D Stakeholder D is a consulting company that provides consulting services in research, development and planning in construction, energy, and environmental projects. It collaborates with Company A’s PD by offering its expertise in the R&D of materials and is involved in PD from the preliminary assessment phase to the development phase.

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Sustainability and circularity are key values for Stakeholder D, which is also a reason for its legitimacy in CE PD projects with Company A; the environmental standards of Stakeholder D must be met for continuing the co-operation. These requirements also extend to materials used: in addition to the need for products to be environmentally friendly and safe to use, they must also utilize waste-based circular materials. Stakeholder D is a large company that possesses knowledge that Company A does not. Stakeholder D also has a significant say in the projects it is involved in. It has power over the PD process as its main salience attribute, as well as legitimacy, as not adhering to its standards on the environmental requirements of CE products could lead to the discontinuation of the collaboration. However, as Stakeholder D also has a significant stake in the projects and its interests are aligned with Company A’s, Stakeholder D’s urgency is currently limited. Thus, it can be considered a dominant stakeholder. The probability to impact or ability contribute to Company’s A PD is moderate, so stakeholder management should focus on keeping Stakeholder D satisfied. 4.6 Stakeholder E Stakeholder E is a mining company working with Company A to productize side streams from mining operations. The interviewee works as a consultant to Stakeholder E. The collaboration between Stakeholder E and Company A is based on Company A’s expertise in the use of geopolymers, and the two companies aim to refine the use of these materials in mining. Stakeholder E is involved in all phases of PD except for development, which is done by Company A. Stakeholder E’s key requirements for Company A are that products must pass environmental requirements and receive environmental permits, they must withstand over a century of their intended use and be more environmentally friendly than traditional oil-based products. Furthermore, the use of these products must be optimized from their current form, which is the basis of the current collaboration and ongoing pilot project. The project Stakeholder E and Company A are conducting is located at Stakeholder E’s mine. Stakeholder E is involved in funding the project and possesses significant power over the PD process. Legitimacy is also present, as the size of the project is significant, and Stakeholder E is largely state-owned. Urgency is also implied through the close collaboration between Company A and Stakeholder E. The interviewee stated that information is quickly and readily available from Company A, which is very active in the project. Thus, Stakeholder E can be considered a definite stakeholder with a high probability to impact or ability to contribute to Company A’s PD. 4.7 Stakeholder F An external consultant, who researches new roadbuilding materials, was interviewed as a representative of Stakeholder F. Stakeholder F is a government agency responsible for roads, railways, and waterways. In addition to being responsible for the quality of infrastructure, Stakeholder F’s strategy includes utilizing CE methods and reducing the environmental effects of its activities. The role of Stakeholder F is clarifying its requirements towards products in the early stages of Company A’s PD, and it is involved in the detailed investigation and testing and validation phases. If Company A’s products

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pass Stakeholder F’s general applicability requirements for roadbuilding, they can be included in the Stakeholder F’s official instructions and lists of accepted roadbuilding materials. The requirements of Stakeholder F for Company A’s PD are largely related to its acceptance criteria for roadbuilding materials: The technical properties of the material and Stakeholder F’s circularity goals. Stakeholder F holds significant legitimacy as a government body deciding on criteria on roadbuilding materials, and, to a lesser extent, power over Company A’s PD through their collaboration. Collaboration with Stakeholder F can enable wide use of Company A’s CE products and ignoring Stakeholder F in the roadbuilding sector is not possible. However, the urgency of Stakeholder F is limited, as it does not have direct interests in affecting the PD process. Stakeholder F can be considered a dominant stakeholder with moderate probability to impact Company A’s PD, which makes them a stakeholder that should be kept satisfied. 4.8 Stakeholder G Stakeholder G, a subsidiary of Company A, is a mining company that aims to open a mine to produce enriched iron and copper-gold products. The interviewee works as a mining engineer with experience from R&D, infrastructure design, and logistics of refined materials. In addition to mining operations, Stakeholder G is also involved with Company A’s PD activities and has been collaborating in several projects developing new geopolymer materials. The expertise Stakeholder G brings into the collaboration are in the areas of water treatment technologies, environmental permits, and mine planning in the detailed investigation and development phases of PD. The main requirements Stakeholder G has towards Company A in PD collaboration include considering environmental and social responsibility, as well as financial feasibility. The products developed must be financially viable, environmentally friendly, and safe to use. They should also preferably be manufactured from locally sourced materials. Stakeholder G possesses two attributes of salience: legitimacy and urgency. Therefore, it can be considered a dependent stakeholder. Legitimacy is based on its experience in geopolymer materials, environmental permits, and mine planning, while urgency is related to its current projects on geopolymer development with Company A. Legitimacy can be considered its main attribute. However, Stakeholder G does not possess power over Company A’s PD, because it does not have the ability to affect PD projects directly and it is involved mainly as a consultant. The probability to contribute to Company A’s PD is low-to-moderate, which implies that as a stakeholder G should be kept satisfied. 4.9 Stakeholder H Stakeholder H is a subsidiary of Company A that offers tunneling services for Stakeholder C under a long-term contract. Stakeholder H is, however, also interested in an ongoing project in Company A, where an eco-friendly concrete product is being developed. Stakeholder H would like to test the product in its projects to study the feasibility of it in replacing traditional spray concrete. Thus, its effect on Company A’s PD focuses on the testing and validation phase of PD. Stakeholder H also has extensive knowledge on utilizing concrete in tunneling projects and could help Company A create new materials

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in the field. For example, determining material and machinery requirements are areas, where Stakeholder H would be a valuable partner in PD. Stakeholder H does not have direct requirements for Company A’s PD but has strict requirements for the materials it uses, which would become relevant if the eco-friendly concrete was used. The collaboration currently involves a single project, but Stakeholder H’s extensive knowledge on tunneling and possibility of testing products in situ makes its probability to contribute to Company A’s PD relatively high. Stakeholder H holds power over Company A with the testing possibilities and industry knowledge but lacks urgency and legitimacy, so it can be categorized as a dormant stakeholder. The probability to contribute is currently moderate and H can be categorized as a stakeholder to keep satisfied.

5 Discussion 5.1 How Different Types of Stakeholders are Involved in CE PD As the stakeholders’ role in relation to the PD phase they are involved in was not previously recognized by the case company, findings from the interviews were analyzed inductively in relation to PD process as described by Cooper [11]. This model was chosen for its suitability for mapping out the stakeholders into a single PD framework (Fig. 1).

Fig. 1. Stakeholders’ involvement in Company A’s PD with main salience attributes as per Mitchell et al. [16] shown in light grey (legitimacy) and dark grey (power) (modified from [11, 16, 21]).

An interesting finding from the stakeholder assessment is the number of stakeholders with similar medium probability to impact or contribute and medium salience. Only a few actors have a high probability to impact or ability to contribute to the PD process, which implies that the stakeholder network in this case is a mix of stakeholders that have

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nearly equal say in the PD process with some key players that have significant control over PD. This also imposes difficulties in managing the network, as changes in the key players can impose significant changes in the structure of the stakeholder network. Thus, it is important to keep potential key stakeholders, such as Stakeholder C in this example, involved in the PD process. The similarity of stakeholders also suggests that stakeholder management is not being actively considered, and many companies involved in PD activities have been chosen on the grounds of previous contacts and available network instead of being actively managed and scoped. Company A is highly affected by regulatory bodies, as the products created are subjected to environmental permitting processes and legislation. The consultants, Stakeholders A, B and G, that help Company A’s PD meet the requirements of these actors were recognized as important stakeholders, but only one regulatory actor, Stakeholder F, was identified as an important stakeholder. 5.2 Stakeholder management in CE PD The literature review showed ways to recognize and categorize important stakeholders in CE PD, which constitutes to the first finding of this study: Finding 1: Stakeholders in CE PD can be recognized and categorized based on their salience attributes and probability to impact or ability to contribute. The findings of the empirical study show that prioritization and management of stakeholders is not done systematically in the studied case. This could be seen in the rather homogenous findings discussed in 5.1. And the large number of stakeholders falling into the keep satisfied category. The selection of stakeholders was also revealing and indicated insufficient stakeholder management practices; a more systematic approach would result in a more deliberate set of recognized key stakeholders. Thus, the second finding of this study is presented as follows: Finding 2: Stakeholders are typically not properly managed, and primary stakeholders are not systematically recognized by the case company. As such, this study shows what stakeholders are presently thought to be essential in the PD processes of Company A. Improvements in this could be done, for example, by including a business case selection system, such as the analysis suggested by Kinnunen et al. [31] including market assessment, technical assessment, financial analysis, and strategic fit. This would help Company A’s stakeholder management to reflect its goals and strategy. 5.3 Proposed Model of CE PD Stakeholder Management Based on the results of the cases there seems to be little consideration for different approaches for different types of stakeholders with different salience attributes. Lieder & Rashid’s [22] model of the hybrid top-down regulative control for legitimate and urgent

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stakeholders and bottom-up business-environment driven collaboration for powerful stakeholders is present somewhat naturally in the case company, but the legitimate stakeholders are not being considered with the same intentionality as those with power as their main salience attribute. Furthermore, urgent stakeholders are not being managed systematically. Thus, implementing a salience attribute-based view of stakeholder management in PD is proposed. The results show that powerful stakeholders are currently present in all phases of PD with a slightly declined presence in the development phase. This implies, that the management of powerful stakeholders is continuous in CE PD with a slight de-emphasis in the development phase. This was also recognized by interviewed stakeholders, who saw that the development capabilities of the case company are good. Legitimate stakeholders, on the other hand, influence mainly the beginning phases of CE PD with an emphasis on detailed investigation and development phases, but they also affect testing and validation phase. This implies, that legitimate stakeholders’ demands on CE products should be addressed in the preliminary assessment, detailed investigation, and development phases of PD and affirmed in the testing and validation phase. Although missing from the empirical data of this study, it can be induced from Lieder & Rashid’s [22] model that stakeholders that possess urgency, such as nongovernmental environmental organizations and public acceptance, also exert their influence top-down, for example with demands. The beginning and end phases of PD can be argued to be of most interest to these stakeholders. The preliminary assessment often is the start of the stakeholder relationship, for example when a new project that has a clear regional impact is started. This may provoke demands from regional urgent stakeholders and the public. Likewise, the full production and launch may also evoke these stakeholders with the start of full-scale activities “in their backyard”. Thus, it is proposed that urgent stakeholders exert their influence top-down in the beginning and ending phases of the PD process. The findings are visualized in Fig. 2, which shows the legitimate stakeholders’ effects on PD from top-down and the powerful stakeholders’ effects from bottom-up. Figure 2. Also visualizes the third finding of this study: Finding 3: Powerful stakeholders exert their influence bottom-up and can be present in all phases of CE PD. Legitimate stakeholders exert their influence top-down and their involvement is focused on the start-to-middle phases of CE PD. Urgent stakeholders exert their influence top-down focusing on the beginning and end phases of CE PD. Stakeholder management should systematically recognize the influence of each type of stakeholder’s influence in each phase of the PD process. The proposed model differs from previous stakeholder management models in that it considers the special features of CE PD, where influence from legitimate stakeholders is spread widely and powerful collaborative stakeholders are present in the whole PD process.

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Fig. 2. Proposed model of managing stakeholders in CE PD (modified from [11, 22])

6 Conclusions This study contributes to the research of stakeholder management in CE PD by offering insights into the emerging industry of circular materials and its current state. The stakeholders chosen for this study were relevant and helped understand the current practices in the area, and the study’s findings provide information on the current state of stakeholder management in companies that develop and productize CE products. Key findings of this study include the types of stakeholders considered critical in a typical CE PD organization and the rather homogenous nature of these stakeholders. As such, the results highlight the lack of consideration towards some stakeholders and their potential impact on CE PD activities. A more systematic way of managing stakeholders, where their main salience attributes are considered, was suggested for CE PD. The results have clear managerial implications for organizations similar to the case company and help in creating more effective stakeholder management practices in CE PD. As in case studies in general, there is room for doubt for the universality of the results and more research should be conducted to confirm and validate the findings. Regarding internal stakeholders, only powerful stakeholders were included in the study, but urgent or legitimate stakeholders, such as human resources or internal legal department stakeholders, were missing from the studied stakeholders. Furthermore, stakeholders having urgency as their main salience attribute were missing from the study. Therefore, further research should be conducted on these types of stakeholders in CE PD.

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Understanding the Implications of Circular Business Models for Businesses and Supply Chains Melissa Marques-McEwan(B)

and Umit Sezer Bititci

Edinburgh Business School, Heriot-Watt University, Edinburgh EH14 4AS, UK [email protected]

Abstract. For the past decade, circular economy (CE) has gained widespread recognition as an umbrella of strategies that can help society better manage resources in the pursuit of sustainability. A CE favors Circular Business Models (CBMs) that cycle existing materials, extend the life of products, intensify the use of fewer resources, or dematerialize the economy. However, whilst many businesses and supply chains have tried to become more circular in recent years, not all have been successful. The limited application of circularity potentially indicates the existence of trade-offs at the business or supply chain levels; however, there is limited empirical evidence to draw upon to theorize about such tensions and trade-offs, particularly evidence from units of analysis broader than single focal companies. This study therefore aimed to address this knowledge gap by investigating the positive and negative implications of cycling, extending, intensifying, and dematerializing business models for the business and for the supply chain. The phenomenon was explored through eight qualitative case studies in different industries. Data were collected from semi-structured interviews and triangulated with secondary publicly available sources. As a result, we present our findings on the positive and negative implications of CBMs, contributing to the theory with propositions on the trade-offs of the CE. This study also has implications for practitioners by helping them understand and contrast different circular possibilities in their businesses and supply chains. Keywords: Circular Manufacturing · Business Models · Circular Supply Chains

1 Introduction Circular economy (CE) became a popular topic over the past decade, with widespread awareness of sustainability issues across society, and the emergence of several policies in many nations [1]. Many businesses have also become interested in increasing circularity, thus sparking the emergence of the sub-field of circular business models (CBMs) within literature. Business model is a broad construct, referring to the ‘theory of each business’ as they make assumptions pertaining to what the company is paid for, how their market works, what their mission should be, and what the core competencies required are [2]. © IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 115–128, 2023. https://doi.org/10.1007/978-3-031-43688-8_9

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Although literature linking CE and business models exists, this field has so far favored topics such as adapting existing frameworks to facilitate discussion on CBM innovation [e.g., 3], or the development of taxonomies for CBMs [4]. In one of such categorizations, CBMs can be seen as those that cycle existing materials, extend product life, intensify the use of fewer resources, or dematerialize the economy [5]. Often, management research follows practice before it can influence it [6]. Although it became a reasonably popular topic, discussion on CBMs still presents scope to evolve. As the global economy is currently less than 8% circular [7], the debate has not yet addressed several implementation issues [8] and lacks sufficient empirical evidence. Building the circular and sustainable businesses and supply chains of the future requires drastic transformation, but theory that can help change managerial mindsets and decisionmaking has been limited and therefore much scope for further research on CBMs remains. At the firm level, for example, research is yet to fully address the key differences between success and failure of CBMs, and the implications and issues of implementing distinct CBM strategies, to different types of firms, and in varied contexts. Few empirical evidence is also observed beyond the firm, with a debate regarding collaboration needs and potential tensions across the supply chain having only started [8–11]. Whilst scholars have investigated barriers to CBM implementation across different levels [11], further research is required to understand the effects of implementing different CBM practices and strategies beyond the firm level. Recognizing such a gap, and the need for more empirical evidence, this study aimed to investigate the positive and negative implications of cycling, extending, intensifying, and dematerializing business models for the firm and for the supply chain. Data was collected across eight cycling, extending, intensifying, or dematerializing case studies in different industries, and examined through a thematic analysis. Ultimately, this study contributes to the bottom-up development of theory on the positive and negative implications of CE, expanding CBM research. Scholars can use the findings to develop and test propositions. This study is also of interest for practitioners who wish to undergo business model transformation to include circularity, assisting in strategy development. Furthermore, it can help policymakers identify areas for intervention to further leverage the CE.

2 Theoretical Background CE is frequently viewed as an umbrella concept which aggregates strategies required for sustainable resources management [12], though its exact scope is a topic of debate. Having received contributions from multiple perspectives, the lack of consensus on the full scope of practices that should be included and excluded from the CE umbrella has led it to being classified as an ‘essentially contested concept’ [13]. For example, some view the CE as being about reducing and re-working materials (reusing, repairing, refurbishing, remanufacturing, and recycling), whereas other scholars would classify CE slightly differently as being about narrowing, slowing, and closing resource loops within the economy [4]. In some classifications, using renewable energy and including the social aspect of sustainability are also included [13]. An implication of the lack of consensus in the definition of the CE concept is the difficulty to precisely define what circular business models (CBMs) are, with this construct

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also being subjected to different views. In one of the most used definitions, [5] categorized CBMs as being those that involve cycling materials (through reuse, repair, remanufacturing, and recycling), extending product life (through long-lasting design, upgradability, and maintenance), intensifying product use (by renting, sharing, and pooling), and dematerializing the economy (by offering services instead of products, virtualizing, reducing consumption). Nevertheless, the existence of different CBMs typologies with distinct classifications [e.g., 5], implies caution in trying to view CBMs too rigidly. Every business is different, with many conducting a range of circular activities, and there might be some nuances and fluidity in how they can be classified. For instance, Original Equipment Manufacturers (OEMs) frequently remanufacture and lease equipment. Therefore, in this study CBMs are viewed as positioned in flexible continuous rather than block categories. Although research on CBMs has grown exponentially in recent years, most studies focused on classifying firms, in theoretical aspects, or in adapting classic management tools to foster circular innovation [3–5]. While such research is important, scholars and practitioners could benefit from understanding implementation issues through empirical research [8], including how value is created or destroyed when adopting different CBMs. For example, in one empirical study involving several firms, [9] demonstrated that servitization is an important element to add value in independent repair and remanufacturing businesses, with lower brand reputation. Uncovering further the diverse ways in which distinct circular practices contribute or hinder firm performance is still a gap within the literature. If addressed, this could assist existing manufacturers in deciding how to adapt their current business and help start-ups to understand how to achieve competitive advantage from CE [14]. CBMs are hard to realize in practice, as they fundamentally change the way in which materials and information flow across supply chains and beyond. CE requires a systemic shift; however, CBM research has been narrowly concentrated at the firm level. Further research is therefore required to broaden its scope to include supply chain and other stakeholders [15, 16] as unit of analysis. In a CE, additional steps are implemented in the supply chain. As a minimum, circular supply chains need to track product use to conduct maintenance, and then recover resources at the end of the product life to remanufacture or recycle [16]. These activities require collaboration, but although managing collaborative networks has been long highlighted as challenging in management theory [17], little discussion on how to coordinate collaborative circular networks exists. In one of the few empirical studies, [10] evaluated five companies in textile industry, outlining information sharing and resource sharing as the two most relevant collaborative practices in establishing circular supply chains. In another study involving twelve businesses, the barriers to CE implementation on the employee, organization, value chain, and market levels were documented [8]. In line with these recent studies, there is a gap in the literature involving continuing to broaden units of analyses beyond focal companies in CE research, for example exploring how CBMs impact supply chain performance, to uncover whether circular supply chains are viable in the long term. In summary, in considering the existing CBM literature, we observed two gaps. Firstly, contributing empirically to create and refine existing theory on the implications and trade-offs of CE to business performance. And secondly, broadening the scope of

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analysis to include elements of the supply chain and beyond [16]. Therefore, this study aimed to address these gaps by investigating the implications of CBMs to the firm and to the supply chain, through a multiple case study design, which is explained in detail in the next section.

3 Method Due to the limited ability to draw conclusions from existing theory [18], and explorative character of the research question, a qualitative research design was pursued in this study to build theory through inductive reasoning [19]. Within possible qualitative methods, case study research was particularly suitable, as it is appropriate when causes and effects are not well defined, or contextual conditions are relevant [20]. While single case studies are not uncommon in CBM research, this approach could present some limitations regarding the generalizability of any conclusions drawn from the data analysis process [21]. A multiple case studies approach was therefore selected as a more appropriate method that allows phenomenon exploration whilst ensuring a reasonable level of external validity [21]. Eight case studies were selected through purposive sampling [20], with the selection criteria being defined as businesses: from different industries to allow higher generalizability; and firms presenting specific practices that could be classified as cycling, extending, intensifying, and dematerializing business models [5]. Two firms were selected in each of the four categories to increase reliability, as demonstrated in Table 1. Table 1. Case studies Firm

Industry (market)

Size

A

Chemical recycling £4 mi (non-fossil oil) (grant)

B

Electromechanical (bespoke pumps)

£25 mi (turnover)

C

Oil and gas (asset marketplace)

£350 mi (listed)

D

Electronics (luxury audio systems)

E

Employees

Interviewee

CBM

Chief Sustainability Officer

Cycling (innovative recycling)

255

Chief Executive

Cycling (reman.)

8

Chief Operating Officer

Extending (reusing, reselling)

£17 mi (turnover)

170

Managing Director

Extending (reman. and upgrading)

Heavy equipment (project, bespoke)

£6 mi (turnover)

29

Senior Manager

Intensifying (renting bespoke equipment)

F

Offshore energy (subsea equip.)

£2 mi (turnover)

20

Executive Chairman

Intensifying (refurb., sharing, asset management)

G

Steel and chemicals £70 mi joint venture (turnover) (ethanol)

270

Chief Sustainability Officer

Dematerializing (carbon capture)

20

(continued)

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Table 1. (continued) Firm

Industry (market)

Size

Employees

Interviewee

CBM

H

Education in business management (online)

£80 mi (turnover)

1200

Executive Deputy Dean

Dematerializing (virtualizing)

All but one firms were UK-based with significant export and internationalization activities. The exception was Firm G which was a joint venture between an American multinational and a Chinese steel mill, who was attempting to export the same operation to the UK. Firm A was a start-up setting up in the UK a novel hydrothermal recycling technology, able to convert waste plastics back into oil or selected polymers. Company B also fitted into [5]’s broad cycling category, due to the remanufacturing of their own and third-party bespoke industrial pumping equipment. Firm C extended the life of products by providing a global marketplace for used and decommissioned oil and gas assets, whilst company D’s music enthusiasts market could keep their turntables and hi-fi audio systems for life, by upgrading hardware and software components. Firm E and F intensified resource use by renting and reusing modular heavy kit in large construction or industrial projects and coordinating asset sharing/swapping between firms and maintaining subsea equipment, respectively. Firms G and H dematerialized material loops in diverse ways. Firm G helped dematerializing steel and chemicals manufacturing if seen as one system, by capturing carbon emissions from steel production, replacing fossil hydrocarbons required to make ethanol, which in this case was used to produce surfactants for consumer chemicals. Finally, firm H was a higher education institution with an international presence, aiming to take full advantage of virtualization to expand its portfolio of fully online postgraduate programs. Data were collected through 60 min online interviews conducted between September 2020 and August 2022 with participants at senior management level, as detailed on Table 1. Data were subsequently triangulated with publicly available information on the companies. Once collected, the data were analyzed through thematic analyses (codes were positive and negative implications to the firm and to the supply chain) and discussed amongst the research team in several rounds to ensure that the main aspect of each case were captured, and that cross-case patterns were fully explored.

4 Findings After analyzing the eight case studies in detail, patterns pertaining to the implications of circular business models were highlighted at the firm and at the supply chain level. They are explained in detail in Sect. 4.1 and Sect. 4.2, respectively. 4.1 Implications of Circular Business Models to the Company To date, cycling through recycling has been limited to materials that can be re-processed, which even for plastics represent a small percentage of resources across the economy

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[20]. Chemical recycling is a promising emerging technology, but few plants are operational worldwide, and they use pyrolysis techniques. As a new chemical recycler in the UK, firm A has been able to receive investment, and set up its plant, based on a hydrothermal process that transforms plastic waste into oil. However, this required an expensive patent process, and although the technology allows some flexibility, the main limiting factor is the costly process of segregating materials upon arrival at the plant to get the right purity. Despite initial higher costs, the likely effect in the long term is positive for firm A as processes and the waste system are optimized. Although firm B was also a cycling firm according to [5]’s categorization, this remanufacturer’s CBM was different to firm A. Specifically, company B had a century-long history as an OEM of high quality electro-mechanical equipment. Building from a resource-based view and recognizing its remanufacturing potential, the company started to remanufacture its own brand. Over the past decade, however, with a market downturn, firm B also started to offer remanufacturing services to all brands including competitors. Although in the short term it allowed firm B to increase revenues, in the long term it diminished the brand as the company started to being perceived as an independent remanufacturer, reducing loyalty and profit margins. Firm C extended the life of oil and gas equipment by reselling them in their online marketplace. At a firm level, their business model was completely dependent on the brokerage fee involved in mediating the circular transaction. However, building the necessary required brand recognition to build supply and demand was a slow process, even though the founders had worked in the sector for most of their careers. The high risks involved in matching supply and demand puts the long-term viability of the business in question, with scalability limited due to the high dependency on the specialist knowledge and long-term networks of the founders. The CBM of Firm D was again distinct, as they extended the life of products using a different approach, through upgrades. It was an OEM of luxury ‘best quality’ home audio systems, from turntables to integrated sound systems. From its beginnings in the 1970s, D’s products were modular by design, and customers have always been incentivized to keep the product for life. Firm D made this a viable CBM in the long-term by developing and selling modular mechanical, hardware, and software upgrades, that are more profitable than new products, as they require less intensive and shorter research and development. However, explaining the upgradability to customers was an issue, requiring expensive partnerships with distributors and retailers, particularly in far markets in Asia. Firm E intensified the use of bespoke cranes and other heavy equipment by renting from construction projects to industrial applications. This required high engineering capability and modular design. The company used to sell such equipment, but recently had moved to a rental model, which allowed them to expand to new markets; however, the financial return was slower as margins in the long term were lower albeit continuous. Firm F also ‘intensified’ through sharing of resources between oil and gas operators, by informally collecting information on their subsea equipment. Through good relationships with engineers at all operators in the region, they could coordinate the swap of spare parts that they needed to conduct a repair or remanufacture to a different client. Managing information but not the assets allowed firm F to keep a low stock of parts, reducing overheads and making their business more profitable in the long term.

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Company G dematerialized steel production by capturing carbon emissions and subsequently using it to produce consumer goods (laundry detergent). This met governmental guidance for the steel mill, and generated royalties for the technology company involved in the joint venture, resulting in likely positive results in the long term from their perspective. However, organizing the initial collaboration was a complicated process, requiring the establishment of long-term relationships and high soft skills by the technology company. Lastly, firm H dematerialized postgraduate education by increasing the offer of virtual programs. This increased long term revenues from wider student reach; however, it was harder to control the student experience virtually. Table 2 summarizes the positive and negative implications of circular practices to the business model of the studied firms, including an overall exploration of the balance between these implications leading to likely long-term effects. Table 2. Main implications of CBMs to the focal firm Firm

CBM

Positive implications

Negative implications

Long term effects

A

Cycling (chemical recycling)

Interest from funders, great reputation, process allows variation of outputs

Expensive patent, expensive process to segregate materials to get to the right input specification

Positive

B

Cycling (remanufacturing)

Initially, remanufacturing any brand increased volumes and revenues

Over time, B behaved more as an independent service firm, losing brand recognition, and tightening profit margins. High stock

Negative

C

Extending (reusing)

Selling used assets in their platform generated income

It takes time to establish a Negative reputable marketplace. Difficult to match supply and demand within specific time frames

D

Extending (upgrading)

Upgrades sold at higher profit margin than new to a niche market

Upgrading had to be explained to consumers, expensive partnerships with retailers in far East

Positive

E

Intensifying (renting)

Moving to rental and reusing equipment allowed to expand from construction to other industrial markets

Required bespoke modular design and high engineering expertise. Profit margins lower in rental

Negative

F

Intensifying (sharing)

Managed asset information only with low own stock, could re-work equipment quicker due to parts swaps

Dependency on the maintenance of good relationships with operators’ engineers

Positive

(continued)

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Firm

CBM

Positive implications

Negative implications

G

Dematerializing (carbon capture)

Avoidance of carbon taxation to the steel mill. Plug-in and customizable technology with low capital costs

Difficult to orchestrate the Positive initial supply chain and the manage collaboration between steel and chemical businesses

Long term effects

H

Dematerializing (virtualizing)

Increased revenues and student reach. More flexibility for staff

Harder to control student experience online

Positive

4.2 Implications of Circular Business Models to the Supply Chain As a new emerging cycling technology, chemical recycling had a negative financial impact on the supply chain of firm A in the short term, as it requires investment to change existing waste infrastructure. It also requires energy from electricity, with implications for firm costs and for the environment depending on the composition of the energy grid. However, in the longer term, the positive impact is making the supply chain more resilient by using an abundant raw material (waste plastics), simultaneously solving the waste issue. In the case of firm B, although remanufacturing presented higher costs, the impact of this activity was positive on the supply chain, as they were not passed on to customers. Remanufacturing was indeed positive for the supply chain, mainly due to speed, as servicing equipment is quicker and avoids the disruption of ordering new equipment and changing infrastructure. The benefit of speed and minimization of disruption was also observed in firm C’s supply chain, as buying used assets was much quicker for customers, compared to commissioning new bespoke infrastructure, but it depended on standardization or adaption and conducting checks thoroughly. Also, with an extending CBM, firm D’s upgradable durable sound systems created an exporting activity and benefited distributors and retailers who could charge a high fee for knowledge and presence in local markets. Although intensifying the use of material resources through the rental of more standardized equipment could be slightly less productive in specific tasks, firm E’s supply chain benefited through a reduction in cost and higher speed of supply. The main benefits were related to an increase in speed and reduction in disruption times, rather than costs. Indeed, firm F provided an example where they could swap parts and service a client’s critical equipment component that failed. Whilst firm F was able to deliver the service in one week, importing a new component from the OEM would have taken twelve weeks, and cost dozens of millions of pounds in loss of revenue. Lastly, dematerializing also presented positive and negative implications for the supply chains of companies G and H. Overall, manufacturing a product using captured carbon rather than fossil carbon increased costs for the supply chain, which were not passed on to the consumer on this occasion and were absorbed by the product manufacturer. Nevertheless, a positive implication was that it allowed the steel manufacturer to meet governmental guidelines and reduce carbon emissions. In the future, this value

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could be captured financially and shared between partners if carbon taxes are implemented. For company H, virtualizing the product offered allowed many benefits related to talent acquisition and expanding the demand globally, but this model also involved higher variability in terms of student numbers and thus staff requirements year-on-year. Table 3 summarizes the implications of implementing CBMs beyond the firm, to the supply chain. Table 3. Main implications of CBMs to the supply chain Firm

CBM

Positive implications

Negative implications

A

Cycling (chemical recycling)

Abundant raw material (waste plastics), reducing supply chain dependence on fossil hydrocarbons

Additional costs to Neutral change the system. Environ. Benefit dependent on renewable energy

Long term effects

B

Cycling (remanufacturing)

Remanufacturing service quicker than buying new bespoke equipment

No negative impact to the supply chain (labor costs absorbed by B)

Positive

C

Extending (reusing)

Reused equipment was cheaper than buying new. Asset transfer could be arranged more quickly than buying bespoke new

Perceived increase in risk and liabilities, both for supply and for demand. Limited scalability

Neutral

D

Extending (upgrading)

Exported to loyal music enthusiasts worldwide

Higher costs (Upgrades Positive done locally in Asia and costly reverse logistics)

E

Intensifying (renting)

Rented equipment meant less hassle, part of OPEX budget, not CAPEX of clients, overall cheaper and much quicker

Interchangeability between projects was sometimes limited and could mean less specialized equipment

F

Intensifying (sharing)

Higher speed due to quick Non-official swap of parts, reducing collaboration with disruption, reducing competitors unnecessary stock of spare parts

G

Dematerializing (carbon capture)

Sustainability claims for the steel manufacturer. Scope 3 carbon reduction for manufacturers of consumer chemicals

Circular ethanol is more Negative expensive overall. Complicated message to communicate to consumers

H

Dematerializing (virtualizing)

Higher talent acquisition and retention. New partnerships with media and content developers. Market growth

Harder to predict student numbers and therefore supply needs in terms of staff and support

Positive

Positive

Positive

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5 Discussion Circular economy has been increasingly promoted throughout the past decade based on its hypothetical contributions to sustainability, job creation, and economic growth [22]. Although CE is recognized as essential in the pursuit of sustainability, recently the assumptions underpinning much of the theory have started to be scrutinized [23]. At the crux of the scrutiny of CE assumptions is the tension between environment and profit, i.e., a debate on whether CE can achieve less environmental impact whilst simultaneously delivering on economic growth [24]. Scholars have previously pointed out that the environmental benefit of CE depends on consumer behavior, or on whether reusing and remanufacturing replaces primary production instead of leading to ‘rebound’ effects [23, 24]. Whilst such debate has started, a dearth of empirical evidence currently constraints conclusions and generalizability of findings regarding implementation issues of CE [8, 12]. This study therefore ultimately contributes to such debate, by exploring the implications of CBMs to the firm and to the supply chain through eight case studies. The studied firms cycled, extended, intensified, and dematerialized material loops in diverse ways. However, crossing the information between Tables 1 and 2, it is possible to observe the emergence of patterns, which are summarized below. • Negative implications to the firm: higher costs associated with more skilled labor; time-consuming activities; expensive recycling processes; difficulty of explaining circularity to consumers; and higher dependability on relationships with suppliers and customers. • Positive implications for the firm: access to funding; increased reputation; market expansion; increased revenues; and compliance with governmental guidelines. On balance, dematerializing models appeared more positive regardless of firm size, because they help eliminating overheads whilst simultaneously allowing expansion to wider markets. In particular, virtualizing was the CBM with the most positive outcome in the long term as it would help firm H to grow; however, this model was also the one most closely associated with business-as-usual in the linear economy. In countries where labor costs are high such as the UK, the more labor-intensive activities that can be involved in cycling through remanufacturing or intensifying by providing rental and reuse services might not be compensated with higher margins or revenues. Firms adopting these CBMs can therefore experience a loss of overall competitiveness unless they can increase their brand and service offering to regain profit margin [9]. These findings contribute to addressing a lack of empirical evidence on the implications of CE to firms [11], but also highlight the difficulties of competing through CBMs. Further research is therefore required to investigate competitive and marketing strategies in the CE space. Moreover, further inquiry is required to understand potential rebound effects [8, 24] and the effect of labor costs in each type of CBMs in various locations. Patterns of positive and negative implications for the supply chain were also observed in Table 3, as described below. • Positive implications to the supply chain: increase in speed of delivery in the supply of customized equipment and products; minimization of disruption to clients; and

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increase in supply chain resilience through a diversification of the origin of raw materials. • Negative implications to the supply chain: higher unpredictability; greater complexity in managing relationships; and collaboration with competitors through a third party. The results of this study demonstrate that every CBM has both positive and negative implications for businesses and supply chains. Whilst the negative implications were aligned with previous literature on barriers to circular value chains [11, 16] relating to uncertainties and higher complexity, our findings provide new empirical findings pertaining to the benefits of CBMs to supply chains. In particular, our findings indicate that, despite being potentially less profitable to the firm, intensifying business models based on reusing and sharing can benefit the supply chain, reducing hassle and avoiding capital investment. Moreover, extending and intensifying business models can be much quicker and more customized compared to manufacturing-to-order models, reducing disruption, and increasing the efficiency of the supply chain. When contrasted with the results at the firm level (where dematerializing models appeared more effective), this finding emphasizes the importance of broadening the unit of analysis and understanding the effects of CBMs at various levels. The results from our eight case studies indicated that, on balance, dematerializing CBMs offer positive implications for firms in the long term, whilst benefits of intensifying CBMs are experienced by the supply chain. The relative comparisons between the firms analyzed in this study are provided in Fig. 1. Whilst in this study we did not observe significant effects of firm size or position in the supply chain, the potential moderating effect of these variables can be investigated in future studies.

Fig. 1. Implications to business vs supply chain

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The number of case studies, however, limit the generalizability of these results. As each product-service offer is unique, the appropriateness of each CBM strategy might differ from company to company and might also depend on how the firm competes in specific markets. Business can therefore utilize Fig. 1 to conduct their own analyses and comparisons, by plotting the long-term implications of each CBM option for their firms and for their supply chain partners. Figure 1 can therefore be used as a tool to facilitate discussion on different circular choices, promoting debate related to enterprise strategy.

6 Conclusion This study explored the positive and negative implications of CBMs for firms and for supply chains. By analyzing two cycling, two extending, two intensifying, and two dematerializing CBMs, patterns of positive and negative implications were highlighted. For example, this study found that some of the potential benefits of CBMs for firms include increased revenues, market expansion, and compliance with governmental guidelines. However, potential drawbacks of CBMs included higher costs, time-consuming activities, difficulty of explaining circularity to consumers, and higher unpredictability. As a result, this study contributes to the emerging evidence on benefits and trade-offs of CE [9, 10], providing an empirical contribution to CE theory. Furthermore, it provided empirical evidence beyond the firm, including an analysis of the likely long-term implications for the supply chain, complementing theory on circular supply chains [11, 16]. Specifically, this study provided a novel discussion on the benefits of CBMs increasing speed of delivery, minimizing disruption to the value chain, and increasing supply chain resilience, and highlighted the drawback of increased complexity in managing relationships between businesses. By contrasting the implications of CBMs for the firm and for the supply chain, this study highlights that there is no one-size-fits-all approach to CE, and that the best solution to a particular business will depend on its specific context and circumstances. Recently, the assumptions underpinning the CE concept have been questioned [25]. As CE does not have a universal definition [13], the boundaries and scope of categorizations of CBMs such as the one proposed by [5] and adopted in this article need to be constantly revised. Within each of the four CBM categories, the two businesses we studied were drastically different, highlighting the diverse ways in which circular strategies can be implemented. Therefore, each business should conduct their own analyses on the positive and negative implications of different circular options, according to their own context, circumstances, and place within a supply chain. For businesses that wish to conduct such exercise, Fig. 1 provided a practical tool that practitioners can use when comparing strategies, highlighting that benefits and trade-offs might not be obvious at the firm level and only be fully captured at a supply chain level. Ultimately, the implications of CE practices can also aggregate at the regional and at the national level. Future research can therefore broaden the unit of analysis even further, comparing how different CBMs can affect specific regions, by adding to or displacing existing activities, including the effects of potential rebound effects.

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Exploiting Information Systems for Circular Manufacturing Transition: A Guiding Tool Federica Acerbi1(B)

, Claudio Sassanelli2,3 and Marco Taisch1

, Mélanie Despeisse4

,

1 Department of Management, Economics and Industrial Engineering, Politecnico di Milano,

Milano, Italy {federica.acerbi,marco.taisch}@polimi.it 2 Department of Mechanics, Mathematics and Management, Politecnico di Bari, Bari, Italy [email protected] 3 Tech Center for Good, École Des Ponts Business School of École Des Ponts ParisTech (ENPC), 6 Place du Colonel Bourgoin, 75012 Paris, France 4 Department of Industrial and Materials Science, Chalmers University of Technology, Gothenburg, Sweden [email protected]

Abstract. The diffusion of the circular economy (CE) paradigm in manufacturing companies, also known as Circular Manufacturing (CM), has been triggered by the intensive exploitation of natural resources and the associated negative environmental impacts generated. According to CM, natural resources consumption should be minimized, and their life cycle should be extended or reintegrated into new life cycles after usage. In this context, the digital transformation of manufacturing companies, exploiting both Industry 4.0 technologies and information systems (IS), may support them not only in their process management but also in embracing CM strategies. To take this digital and circular transformative path, the synergic exploitation of IS for CE adoption is essential. The extant literature presents scattered knowledge about the potential to exploit data collected from IS to implement CM strategies. Therefore, the research objective is to support companies in using IS to implement CM strategies, by integrating into a unique CM-IS framework. First, a systematic literature review was conducted to identify the direct correlation between specific IS and CM strategies, and also the related parameters influencing a good synergy among them. Subsequently, a maturity model has been developed to provide a practical framework to assess the current state of a manufacturing company in adopting IS for CM strategies implementation. The maturity model has also been integrated with a SWOT analysis module to define a roadmap to improve the current state of the company assessed. Last, the maturity model has been applied to an industrial use case showing the benefits from IS exploitation for CM purposes. Keywords: Circular Manufacturing · Information Systems · Data Management · Roadmap · Maturity Model

© IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 129–143, 2023. https://doi.org/10.1007/978-3-031-43688-8_10

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1 Introduction The pervasive linear economy has pushed the society beyond some critical planetary boundaries, calling for the diffusion of the circular economy paradigm [1, 2] associated to the sustainable development goal (SDG) 12 [3]. This transformative need is rooted in society’s consumerism combined with growing worldwide population leading to an increase of resources demand [4]. The manufacturing sector should redesign and optimize its internal processes and strategies to meet this growing demand for goods and services while minimising environmental impacts, focusing on the extension and circulation of resources lifecycle, and the minimization of resources consumption and waste generation [5–7], thereby establishing a circular manufacturing (CM) [8]. At the same time, digital technologies have become ubiquitous, and they are influencing companies’ businesses [9]. Indeed, thanks to this innovation, it is possible to exploit data and information to obtain adequate knowledge supporting decision makers [10–14]. Several opportunities emerged from the adoption of advanced technologies [15] and information systems (IS) can be exploited to facilitate the transition towards CM [16, 17]. They rely on objective and sharable data and information to support decision makers in the adoption of CM strategies like remanufacturing, recycling, reuse, cleaner production, resource efficiency, industrial symbiosis, closed-loop supply chain etc. [18]. Nevertheless, the extant literature currently presents scattered knowledge about the potential to exploit data collected from IS to implement certain CM strategies (e.g., [16]) although manufacturing companies may have already invested in IS for their daily operations and could be interested in using IS for CM adoption too. Therefore, the research objective of this contribution is to support companies in using IS to implement a certain CM strategy based on their current state by integrating CM and IS into a unique CM-IS framework. This objective is addressed by answering to the following research questions (RQ): RQ1: Which are the information systems supporting the adoption of specific CM strategies? RQ2: How companies can use information systems to adopt CM strategies? The paper is structured as follows. Section 2 depicts the methodology employed to integrate into a unique model the existing scientific knowledge. Section 3 presents the literature review conducted to create the ground of the model. Section 4 describes the MM and its application into an industrial case to validate the results obtained. Last, Sect. 5 concludes the paper, highlighting the key practical and theoretical contributions, and the main limitations opening the way for future research opportunities.

2 Methodology The research objective of this contribution is to support companies in evaluating how to use IS to implement specific CM strategies in accordance with their strategy and resources. This goal is addressed by studying and integrating the scattered knowledge already available in the scientific literature. First, a systematic literature review was conducted to analyse the synergic usage of IS for CM-related purposes and create a unique framework enabling to support the decision-making process of managers operating in

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manufacturing companies extending the research developed in [19]. This review was conducted using the Scopus search query shown in Fig. 1, apply exclusion criteria and a snowballing method to obtain a final sample of 85 papers for the analysis and framework development (see Fig. 1 for further details).

Fig. 1. Literature review and framework development process.

Based on the review of the selected contributions, it was possible to study how proper IS can support companies in adopting specific CM strategies, leading to the dimensions and subdimensions characterizing the CM-IS framework developed. The results obtained through the literature review were integrated into a maturity model (MM) proposed in this paper. [20] was taken as reference to build the model while the dimensions were taken from the results out of the systematic literature review and the maturity scale is based on the integration of two scales from [21] and the ISO/IEC 15504 Information technology – Process assessment [22] (see further detail in chapter 4). The MM relies on a self-assessment questionnaire evaluating the current state of companies’ adoption of IS for CM and making companies improve based on their strategic goals and current needs. More specifically, it turned out that self-assessment tools can easily support managers in understanding and embracing the key concepts of sustainability [23]. Thus, the model was considered a good starting point for triggering a CM shift in organizations. In addition, considering the need to design a supporting path integrating both the current state and the strategic goals the company has, the MM has been combined with a SWOT analysis [24]. The MM has been validated through an application case. The selected company is an Italian company producing industrial and commercial vehicles, including the production of small road vehicles and the bigger sizes vehicles. This industrial case was selected to validate the MM because this company can be considered a pioneer in the Italian landscape regarding CM-oriented actions and the environmental sustainability investments. Indeed, 98% of energy consumed comes from renewable sources and the company is able to recover 96.5% of the waste produced. The key CM strategy applied by the company is remanufacturing of vehicle spare parts: complete engines, long block engine, transmission, injection systems, rotation electrical machines, turbochargers, diesel particulate filter and electronic components. To apply the MM, the IT and Production managers were interviewed (the production manager oversees all the sustainability-oriented activities happening on the shopfloor) by the authors in person.

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3 Literature Review Results The extant literature presents several research studies focusing the attention over specific characteristics of each IS. Starting from the Geographical Information Systems (GIS), this is used to collect, store, transform and display spatial data captured from the real world for several purposes. Data about a specific location can be analysed in its relationship with other locations [25]. Regarding CM, there are three key elements which can be exploited: 1) Identification of a geospatial location which analyses and correlates data in terms of location and other non-spatial attributes [26], with other types of geo-referenced information, such as data on population, on road and water networks, to produce thematic maps, allowing the evaluation on waste exploitation [27]. 2) Identification of optimal logistics solutions by minimizing transportation costs thanks to the acquisition of data about geospatial location of the product and about the quantity that needs to be moved back inside the circular loop (for instance for waste recycling) [28]. The GIS has the capability to identify the highest potential territorial areas for the reuse of biomass for the production of bioenergy, exploiting and manipulating the spatial location where the material is produced combined with the location where the material could be utilized for energy purposes [29]. 3) Supply chain analysis providing a comprehensive overview of the supply chain to optimize transportation and inventory performances and exploiting opportunities to create industrial symbiosis networks [30]. Therefore, this tool can identify material, wastes or products that have to return to the company to be reinserted inside the economic circular loop, supporting most of the products’ end-of-life strategies (e.g., recycling, reuse, and repurposing). Nevertheless, one of the main limitations of GIS is that it lacks analytic tools dealing with spatially related structured decision problems. GIS overlay operations only identifying areas that simultaneously satisfy a set of locations constraints [26]. To expand the ability of the information system, GIS has been combined in literature with multicriteria decision analysis (MCDA), analytical hierarchical process (AHP), sensitivity analysis and other methods. Regarding the Product Lifecycle Management (PLM) system, the first relation between PLM software solutions and CM strategies appears in 2017 when [31] analysed the benefits of PLM linked with product-service systems (PSS). PLM was considered useful to design products and PSS lifecycle and to assist companies in environmental and economical practices such as lifecycle costing and lifecycle revenue for the products. After that, PLM was recognized as an integrated business approach to manage a product throughout its whole lifecycle, from idea generation to the return in the economic loop [32]. The product lifecycle includes the beginning (BoL), middle (MoL) and end of life (EoL) stages, during which the IS acquires data from all the processes, facilitating decision making for CM application. Nevertheless, PLM systems have some limitations as highlighted by [33] who suggested that PLM are used mostly in large companies with an established IT culture, also due to the large investments needed.

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In the construction sector, a sister solution of PLM is the Building Information Modelling (BIM). BIM technology enables an accurate virtual digital model of buildings. This model, known as building information model, can be used for planning, design, construction and operations phases of the facility [34]. In existing buildings, BIM implementation is still limited due to several factors, such as the efforts needed for data collection, modelling and handling uncertain data and objects in buildings [35]. The BIM would enable project managers and architects to update building material choices in real time in response to changes in supply chain availability of virgin materials, ultimately supporting the implementation of the CE through the procurement of waste materials in the construction of new buildings [36]. Regarding the customers, considering them as fundamental elements to embrace CM, two key IS were identified: the customers experience-decision making information systems (CX-DM IS) and the Customer Relationship Management (CRM). According to [37] the CX-DM IS is required when the service experience needs to be monitored; e.g., subjective activities, behaviours, opinions, feelings, personalities and physiological responses. This IS might enhance resilience in terms of new product development to improve customers’ experience or replan user experience (with the goal to meet user needs and the current market trends in a context of CM strategy). The level of customer experience influences the acceptance of the solutions also in a PSS [38]. Therefore, the CRM is built up starting by loyalty with the customers, since it controls the customer’s interaction with the company. The CRM is based on the assumption that current satisfaction is a strong predictor of future satisfaction. Companies need to be aware about the customers’ needs to keep the satisfaction rates stable over time. Also, Enterprise Resource Planning (ERP) emerged as a good IS for CM purposes. [39] studies ERP to be used as part of a performance management system to track the CM performances. For example, ERP is linked to the valorisation of agro-waste in terms of its contribution to the environmental and economic sustainability of the supply chain. The ERP can be implemented to evaluate the resilience and the environmental sustainability of the entire system, looking also at the profitability of all the products of the value chain. Linked to the ERP, the Material Requirement Planning (MRP) supports materials’ management based on the scheduled production, and the reverse MRP might be useful whenever recover, repair, refurbishing and remanufacturing strategies should be implemented [40]. To support decisions, the Decision Support System (DSS) represents the main software improving the effectiveness of data analysis. [41] used the DSS for the determination and estimation of recyclable wastes in the agricultural sector. The DSS becomes an effective support system if it is exploited with a data driven approach and thus whether it is integrated with other IS. DSS lacks an integrated and comprehensive view of the supply chain, but when fed with proper data might become useful for CM purposes, for instance it might be used to assess the biomass and biofuels along the supply chain to determine the optimal quantities to supply and the optimal production capacity. Summing up, the IS list, including the key elements of each of them supporting CM strategies adoption, is reported in Table 1.

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F. Acerbi et al. Table 1. List of IS and key elements supporting CM adoption.

IS

IS tasks supporting CM identified in the literature

Geographical information systems (GIS)

Geospatial location (coordinate, attributes, volume, overlay points), spatial data management, spatial analysis as optimal logistic solution (transportation costs, logistics routes), seasonal variation, environmental impact, resource planning, sustainability and security assessment, customer service and general site management, transport quantification for reverse logistics, digitised road networks, demand-supply management for products in the circular loop, supply chain information, waste contractor information, management costs

Product Lifecycle Management (PLM)

Decision-making support for scenario evaluation of new business models with a different value-proposition, improved logistics and stakeholder’s collaboration, integrated lifecycle costs (LCC) and lifecycle revenue (LCR)

Building Information Management (BIM) Agile procurement of used material through auction’s method, fully transparent information about building, including material composition, construction and demolition waste estimation and renovation planning Customer experience-decision making information system (CX-DM IS)

User experience for product and service design as information for smart production, customer experience decision information system, information about operating procedures, reduced time to market in the new product experience

Customer Relationship Management (CRM)

Customer interaction management. When linked with a PSS, reduced environmental impact of consumption, moving society towards a resource-efficient economy, moving consumer from buying the products to leasing them

Enterprise Resource Planning (ERP)

Creation and improvement of KPI to measure CE effectiveness

Decision Support System (DSS)

Identification and assessment of biomass and biofuel along the supply chain (continued)

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Table 1. (continued) IS

IS tasks supporting CM identified in the literature

Reverse Material Requirement Planning (MRP)

Demand forecast for all components of products, data for linear and integer programming models that can accomplish cost-based objectives

Other information systems

Holistic traceability in the supply chain, data integration across various domains of the product lifecycle, more efficient back-office financial accounting and reporting capability, provisions of improved customer service, data capture, analysis and information reporting, economic and environmental performance indicators, synergies between companies based on shared information, collaboration enabling eco-innovation, combined information and material flow, enhanced planning and organization for cleaner production, feasibility analysis, timely decisions about which products can be reused, information security and system stability

In summary, this synergic interaction between CM and IS is reported in Fig. 2 which highlights the number of studies linking a specific IS with a certain CM strategy.

Fig. 2. CM-IS links

The common ground between IS and CM is the data usage and sharing. Hence, below the key categories of data to be exchanged are summarized:

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• Product: product’s characteristics of the product, initial quality, remanufactured quality, remaining life, design features specifications, Bill of Material (BoM), final status, durability, product flexibility, weight; • Geospatial and logistics: geospatial location (coordinate, attributes), type of location, aerial images, road characteristics, network infrastructure, distances, transportation requirements, timestamp of the measurement, vector assessment; • Strategy and sustainability benefits: training of the employees, competitor analysis, new business model adaptation, environment analysis, supplier selection, business model characteristics, energy used in the CE process, energy recovered by the CE process, energy potential, reduced emissions, indexes in cleaner production, hazardous substances, toxicity of the resources, sustainable development goals, sustainable Key Performance Indicators (KPIs); • Supply chain activities: network, internal and external stakeholders, stakeholder tracking, supply chain analysis, partnership with companies, supplier compliance, stakeholder brand reputation; • Finance and accountability: inventory volume, volume planning, demand planning, tangible value of the product, intangible value of the product, raw material circulation, disassembly and scheduling costs, CE processes direct costs, CE processes indirect costs, lifecycle costs, lifecycle revenue, operating budgets, Capital expenditures budgets, financial budgets; • Re-sale and customer related activities: product demand, product packaging, reverse logistic network, quantity for the re-sold product, customer requirements, customer interests, customer experience, product-related service, marketing, legal support; • Operations and technology: machine characteristics of the linear process, production scheduling, product scrap monitoring, modularity with repair services, big data, Computer Aided Design-CAD. Figure 3 depicts the synergic relationship between IS and CM relying especially on data exchange opportunities.

Fig. 3. CM-IS framework.

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4 CM-IS Maturity Model Development The above reported literature review enabled to elucidate the key elements characterizing the synergic relationship between IS and CM. In detail, it emerged fundamental to have a clear strategy in mind within which include the CM-related objectives to be fulfilled through IS exploitation. Moreover, it turns out that the synergic relationship to be established between IS and CM stands in the data collection and usage. Considering the research objective of this contribution (which aims at supporting companies in exploiting IS for CM adoption), an CM-IS MM, integrated with a SWOT analysis (to objectively design a roadmap to improve the current state of the company assessed), has been developed. According to De Bruin et al., (2005), the MM should rely on the dimensions detected through the literature analysis, that need to be mutually exclusive and collectively exhaustive and should cover the areas addressed through a questionnaire [20]. In detail, the model dimensions are: • IS CM strategies coverage, • IS CM data collection, • IS functions exploited for CM. Indeed, for each of these systems it is explored the CM strategy support, the set of data collected and used (considering eventual IS integrations), and the functionalities for which each IS is used. The literature about MMs is mature and thus, taking as reference the literature review performed by [42], the scale of maturity proposed in the ISO/IEC 15504 Information technology – Process assessment [22] has been adapted and integrated with a maturity scale developed for CM purposes by [21] as reported in Table 2. Table 2. Maturity scale and description. Maturity level

Name

Description

5

Optimizing process

ISs are systematically adopted to support the CM strategies’ implementation and they are both internally and externally (with external entities) integrated

4

Predictable process

ISs are systematically adopted to support the implementation of CM strategies and they are internally integrated (within the company across functions)

3

Managed process

ISs are systematically adopted to support the pilot implementation of CM strategies even though they are not fully integrated, nor internally neither externally

2

Performed process

ISs are considered potentially useful as supporting tools for the pilot application of CM strategies

1

Incomplete process

ISs are not used to support CM strategies adoption also because CM is sporadically applied in the company and there is not an internal strong commitment

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This maturity scale is used as reference to develop the questionnaire to perform the assessment. The questionnaire is composed by 24 questions each linked to 5-level normative answers ensuring objectivity of the interview [43]. For sake of brevity, only an example of one question with the related normative answers is reported. “Does the company have the circular manufacturing business processes digitized within an ICT system? 1. No, the organization doesn’t have an ICT system used for circular manufacturing purposes; 2. The organization has an ICT system that digitize partially the eventual circular manufacturing strategies applied by the company. The data of the processes are acquired manually and it is not integrated with other company’s activities and technological tools; 3. The organization has an ICT system used to map the circular manufacturing strategies implemented by the company. There has been installed sensors that acquire data automatically. Standards are created to manage the integration process; 4. The organization has an ICT system that digitize the circular manufacturing strategies and it is totally integrated across the enterprise. The company has set a quantitative process control; 5. The organization has an ICT system used to digitize the circular manufacturing strategies and totally integrated across the enterprise. Errors in the digitalization of the circular manufacturing strategies are identified and prevented.” These questions cover the three key dimensions of the model (i.e., IS CM strategies coverage, IS CM data collection, IS functions exploited for CM). The aggregated maturity score is given by performing the average of the scores obtained for each dimension (which corresponds to the sum of the scores obtained for each question related to each dimension divided by the number of questions per dimension). The weights of both questions and dimensions are all set equal to 1 to ensure objectivity. Nevertheless, each manager may decide to customize the tool adding specific weights either to the dimensions or to the questions. The analysis performed through the questionnaire is then integrated with the SWOT analysis to gather the overall picture of the company and provide an objective improvement path. The interview is suggested to be performed with the IT and Sustainable managers having the overall view on both domains (i.e., IS and CM implementation).

5 CM-IS Maturity Model Application Through the application of the MM, the overall maturity level of the company was 3.15 (focusing on a single strategy, remanufacturing, and having a well-structured approach, although it might be still considered a pilot initiative). Based on the interview, for the company the ERP is considered a strategic IS and is needed to manage the several business units. The ERP was set up before the implementation of circular processes and during the years it evolved to cover also new processes, as the circular ones. Indeed, the ERP aims to manage the daily business of the company. Throughout the interview, it emerged that the ERP is able to manage different relevant data referred to remanufacturing activities and it is well integrated with the IT

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infrastructure of the company capable of exploiting the latest Industry 4.0 technologies as well. Nevertheless, several remanufacturing-related data are managed by the remanufacturing dedicated business unit on a specific database which is not shared within the company. Moreover, the lack of a GIS limits the company in collecting and using geospatial information which may limit the decision-making support over the supply chain management. The company is less mature in terms of other CM strategies and therefore they are not yet considering using other ISs for this purpose. Nevertheless, they have established strong relationships with external stakeholders which mainly rely on IS like CRM for customers and EDI web portal, integrated with ERP, to automate the relationships with critical suppliers. The MM has also been integrated with the SWOT analysis. Its results are reported below: • STRENGHTS: the company has invested in several innovative technologies, deploying in its processes both IS and Industry 4.0 enabling technologies. It is also aware about the potential cyber-attacks, deciding to invest in cybersecurity. Regarding the CM-related innovation, the company is aware about the need to collaborate with external stakeholders and for this reason it has established a strong stakeholders management structure to pursue sustainable-oriented innovation within a strong and cohesive network based on solid relationships. • WEAKNESSES: the company is currently limited in terms of data analysis and data sharing. Moreover, the strategic orientation towards CM is still limited to a single CM option (remanufacturing). • OPPORTUNITIES: the company may extend the best practices adopted for the remanufacturing strategy towards other CM strategies. In this regard, they could better exploit the IS for other CM strategies by better aligning the IS and CM strategic goals. To evaluate the environmental and economic benefits from IS adoption for CM, it could be good to implement a dashboard with environmental and economic KPIs. • THREATS: the company should revise the current risk management approach and it should also engage the whole company’s organization in embracing CM to avoid to not address the strategic goals in a balanced manner across the different business units. Based on this analysis, a roadmap to improve the current state of the company has been suggested and reported below: • Step 1: the ERP should be extended and used to cover all the data needed for remanufacturing already collected inside the company. • Step 2: the remanufacturing-related data currently used only by the remanufacturing business unit could be shared across the plant. • Step 3: A GIS should be implemented to exploit all the data needed for remanufacturing activities, extending the data set already available. • Step 4: a structured data analysis process should be established to support the decisionmaking process of managers. • Step 5: the best practices already established for remanufacturing activities should be implemented for other CM strategies.

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The MM, hence, was considered a good guiding tool to exploit the already implemented IS to embrace several CM strategies thanks to the collection and usage of already available data. Nevertheless, the MM doesn’t allow to start implementing social-oriented practices, since focused mainly on environmental ones, neither enables to prioritize the most economic convenient solutions but only those most aligned with the strategic objectives of the company.

6 Conclusions This research aimed at supporting manufacturing companies in exploiting their ISs for fully adopting CM strategies. To address this objective, first a systematic literature review was performed to dig deeper in the analysis of the synergic usage of IS for CM adoption. This review resulted in a list of ISs (i.e., GIS, ERP, PLM, CRM, MRP, R-MRP, DSS, BIM) together with their characteristics, aligned with the CM adoption. Moreover, a list of categories of data was identified as common ground linking the two worlds (i.e., product, geospatial and logistics, strategy and sustainability, Supply chain, Finance and accountability, Re-sale and customer related activities, operations and technology). The results of the literature review were used to define the dimensions of the MM that has been developed in this contribution to support and facilitate manufacturing companies in using IS for CM adoption. The MM is based on a 5-level maturity scale adapted from [22] and [21]. Through a questionnaire characterized by normative answers, it is possible to explore the current synergic usage of companies’ IS for CM. Afterwards, once integrated with the SWOT analysis, the results of the interview can be used to design a roadmap to improve the current state of the company assessed. The developed model has been applied to a real case which turned out to be quite advanced in terms of circularity (having intensively invested in remanufacturing activities). Therefore, this research has both implications for theory and for practice. Regarding the theoretical implications, the MM developed facilitate the assessment of the current usage of IS to support the adoption of CM strategies. Indeed, it provides an innovative perspective respect to the previous extant models which consider either CM or IS separately, with a silos approach. Indeed, the key data categories linking IS with CM are explored and integrated in the model. Regarding the practical implications, the model can be employed by companies to concretely assess their level in terms of ISs exploitation for CM adoption and to design a roadmap to improve it. Finally, there are some limitations worth mentioning, opening future research opportunities. First, the MM has been applied to a single company located in Italy, calling for more extensive application to other companies with different characteristics (size, industry, and location). Second, considering that data are the common ground between IS and CM, future research should investigate how to elaborate the data gathered to develop ad-hoc key performance indicators on CM to retrieve relevant information. This would benefit also an advanced monitoring of circular oriented performances of those companies that started the circular transition based on non-digital solutions. Acknowledgements. This study was carried out within the MICS (Made in Italy – Circular and Sustainable) Extended Partnership and received funding from the European Union

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Next-Generation EU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) – MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.3 – D.D. 1551.11-10-2022, PE00000004). This manuscript reflects only the authors’ views and opinions, neither the European Union nor the European Commission can be considered responsible for them.

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Circularity Impact on Automotive Assembly – What Do We Know? Kerstin Johansen1(B)

, Marie Jonsson2

, and Sandra Mattsson3

1 School of Engineering, Jönköping University, Box 1026, 55111 Jönköping, Sweden

[email protected]

2 Department of Management and Engineering (IEI), Linköping University, 58183 Linköping,

Sweden 3 RISE Research Institutes of Sweden, Argongatan 30, 43153 Mölndal, Sweden

Abstract. Assembly is crucial in the automotive industry, and regulations aimed to increase circularity impact the production systems. From this perspective different strategies are emerging related to sustainability and to the End-of-life Vehicles directive, perspectives often captured by “R-words” like Reuse, Recycle, Rethink etc. This paper is based on a literature search inspired by different R-words related to circularity and assembly in the automotive industry in combination with industrial workshops on the same theme. The results explore what challenges to manage during the ongoing green transition in the context of assembly in automotive. Recover, Repair, Reuse and Recycle are the most common terms found in the literature. Furthermore, Remanufacturing stands out as of particular interest to the automotive industry. However, based on the industrial workshops, Rethink as a collective word is an important perspective as well. The conclusions indicate that digitalization can be an enabler but also that there is a need for developing a common understanding about definitions and utilization of engineering tools supporting circularity. Keywords: Assembly · Sustainability · Circularity · Automotive · Digitalization

1 Introduction Assembly is one of the core manufacturing processes in a production system and with new regulations regarding circularity and sustainability, it is important to manage and further investigate assembly from these perspectives. These regulations originate from a need for a more sustainable society and a transition towards a circular economy, which was initiated twenty years ago when the European Commission launched the ambition to facilitate a transition towards resource-efficient production [1]. In 2015, the first Action Plan towards a circular economy was launched within EU [2], followed by the European Green Deal is an initiative that has evolved aiming for facilitating the development of a resource-efficient, modern, and competitive economy [3]. These initiatives have supported the ManuFUTURE 2030 within Europe and focus on more © IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 144–158, 2023. https://doi.org/10.1007/978-3-031-43688-8_11

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sustainable and resilient solutions in manufacturing and not only on competitiveness [4]. During March 2023, the Net-Zero Industry Act was proposed with the ambition to ramp-up the capabilities to manufacture clean technologies within EU [5]. All these changes in the regulations affects the assembly of future products in many ways, e.g. in energy use, material source and selection, manufacturing requirements, traceability, joining methods, to mention some challenges related to a transition towards a circular economy. Here, assembly of future products in an Industry4.0 setting of technologies in the production system will challenge the operators even more, which has highlighted the need of reflecting the well-being of the operators, recognized as Industry5.0 [6]. The implementation of circular economy strategies differs between countries in the European Union, but some strategies are common on a higher level. Examples of such are more sustainable and circular production and waste management including utilization of recovered material, more research on digitalization that supports a circular economy and development of national policies that accelerate implementation of circular solutions [7]. However, during the decade 2011–2021 it was identified that most research were done on the macro-level, like policies and how to implement circular economy and sustainability on a regional or national level [8], but also that engineering/natural science and economic/management science were the two dominating fields. On a company level, it was identified a need to develop capabilities in managing re-using and reducing materials, implement renewable energy solutions, maintain, and repair products and machines to extend the life, reuse and recycle after end-of-life for a product, refurbish and remanufacture [8]. Other researchers have identified the need of managing such “R words”, like refuse, reuse, refurbish, remanufacturing, rethink, reduce, repair, repurpose, recycle, and recover [9–12] in a resilient way [13]. To summarize, the demands on circularity and sustainability will affect the assembly in the industrial production, and there is a need to understand the knowledge gaps existing for the industry to keep or manage their competitiveness in a green industrial development. For automotive manufacturers, this gap is highly relevant and the European union’s End-of-Life Vehicle directive [14] sets targets for the reuse, recycling, and recovery of end-of-life (ELV) Vehicles. Some automotive manufacturers, like Volvo, have created service and leasing schemes to explore user patterns and design when the use of cars is more circular and service oriented. Many suppliers are working on re-taking and refurbishing parts like electro-mechanical throttles valves [15] as well as oil filters, and with the introduction of electrical battery packs, the need for circular approaches in assembly/disassembly increases, as these are expensive and contains scarce minerals. In dialogue with automotive representatives in assembly production cluster in Sweden1 , it was identified an industrial need for mapping the impacts on assembly the transition towards implementing circularity will have. Therefore, the purpose of this paper is to explore how assembly, specifically automotive assembly, is influenced in the circularity perspective and what we know about the challenges that might occur in the ongoing transition into a green industry.

1 Cluster Assembly—Kunskapsförmedlingen (kunskapsformedlingen.se)

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2 Method To find what perspectives that influence circularity related to assembly two studies were performed: i) a workshop investigating the industry perspectives in May 2022 and ii) a literature review to find the research view of the influencing perspectives based on the industrial perspectives were performed afterwards. The qualitative data from the workshop was then compared to the quantitative data from the literature review and related to what research has highlighted as gaps and/or opportunities. 2.1 Circularity Workshop A circularity workshop was arranged in May 2022 with participants from the automotive industry part of the assembly production cluster in Sweden (see Footnote 1). The number of participants in these workshops were 11 participants (N) from three automotive companies (N = 4), three Universities (N = 4) and one research institute (N = 1). The positions in the companies were two managers, one automation competence leader and one senior engineering advisor. The participants at the universities and research institutes were six senior researchers and one researcher. The Circularity Strategy Scanner (CSS) [16] in Fig. 1 was used as a basis for the discussion in the workshop. The CSS was developed to 1) Create a comprehensive understanding of circular strategies, 2) Map current Circular Economy initiatives and 3) Generate ideas for increased circularity and was used in the workshops to discuss where industry is today, what opportunities can be seen and what is the next step/-s.

Fig. 1. A simplification of the CSS used in the workshop (Adapted for clarity from [16]).

The CSS describes five strategies: i) Reinvent, ii) Rethink and reconfigure, iii) Restore, reduce, and avoid, iv) Recirculate parts and products and v) Recirculate materials [16]. The strategies were based on literature and empirical work and the driver for the strategies are seen in Table 1. The following main three questions were discussed after presenting the circularity scanner in the workshop:

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Table 1. The drivers for the five strategies of the CSS (Inspired by [16]). Strategy

Driver

Reinvent

Enable smart business concepts through striving for full decoupling

Rethink and reconfigure

Enable smart business concepts through business model innovation (based on circularity)

Restore, reduce, and avoid

Prevent excess use, improve efficiency, and aim for circularity potential

Recirculate parts and Products

Extend use cycles that exists by capturing value/reducing value loss

Recirculate materials

Extend use cycles with new ones by capturing value/reducing value loss from continued use

Table 2. Search strings for the literature review (Inspired by [13]). R-word

Search string 1

Search string 2

Search string 3

Search string 4

REFUSE RETHINK REDUCE REUSE REPAIR REFURBISH

+ ASSEMBLY

REMANUFACTURE

+ ASSEMBLY +AUTOMOTIVE

+ ASSEMBLY +CIRCULAR

+ ASSEMBLY +AUTOMOTIVE +CIRCULAR

REPURPOSE RECYCLE RECOVER RESILIENCE

1) What do we today relate to assembly in a circular industry? 2) Which opportunities are seen in a future circular industry? 3) What can we do right now to facilitate a transition towards a circular industry? The discussions were led by sustainability experts using the CSS as a base. Post-it notes were used to capture statements by the participants that were divided into two groups. The statements were clustered to identify trends. 2.2 Literature Review To compare the results from the workshops with what has been researched in the area, a literature review of influencing perspectives related to assembly in a circular automotive industry is organized. The search strings for the literature review are presented in Table 2.

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Table 3. An overview of the number of publications in Science Direct between 2000–2022. SCIENCE DIRECT Column R-word

A +ASSEMBLY

B +ASSEMBLY +AUTOMOTIVE

C

E

Percentage Automotive in +ASSEMBLY Assembly + CIRCULAR (B/A)

F +ASSEMBLY +AUTOMOTIVE +CIRCULAR

G Percentage Automotive in Assembly (F/E)

H Percentage Circular in Assembly (E/A)

REFUSE

725

133

18%

155

34

22%

21%

RETHINK

740

190

26%

160

34

21%

22%

REDUCE

106 829

13 819

13%

22 006

2 761

13%

21%

REUSE

10 903

2 033

19%

2 070

458

22%

19%

REPAIR

13 728

2 469

18%

2 705

548

20%

20%

REFURBISH

1 626

270

17%

400

91

23%

25%

REMANUFACTURE

1 339

452

34%

411

167

41%

31% 38%

REPURPOSE

317

62

20%

122

32

26%

RECYCLE

9 652

2 139

22%

2 065

579

28%

21%

RECOVER

15 846

1 933

12%

3 683

518

14%

23%

2 648

495

19%

588

129

22%

22%

164 353

23 995

15%

34 365

5 351

16%

21%

RESILIENCE Sums / Percentage

Since the authorities are driving the transition fast towards circularity in industry, and that researchers have presented several different frameworks related to different circularity related “Rs” [9–12] the extended framework presented by Johansen and Öhrwall Rönnbäck [13] was used as an inspiration. The literature search was done in ScienceDirect using the 11 Rs [13] to identify the gaps related to assembly from a circular perspective and narrowed to publications between 2000 and 2022, where no publications from 2023 were included. The publication window was chosen to reflect the period when circularity started to grow as a concept in academia and industry. Boolean operator AND was used and the Engineering field was selected to limit the result pool. The literature search result from ScienceDirect is presented in Table 3, using the search strings in Table 2. The results can be seen as a broad overview in how assembly has been explored in the research and industrial context in combination with different keywords related to circularity (inspired by [13]). It should be noted that the literature review has not included an analysis of if the results correspond to the R-words used in Table 2 and defined elated to assembly in Table 7. When analyzing the results in Table 3, the percentage of hits that includes “Automotive” vs general industry domain has been calculated (Column C, Table 3). This gives an indication of what R-word is more investigated in research related to the automotive industry. Also, the percentage of hits with “Circular” in “Assembly” has been calculated (Table 3/Column H), as this might give an indication of what R-words is researched or used most for researchers classifying themselves as within the circularity domain. For comparison, the percentage of hits for publications with circular and automotive has also been calculated (Table 3/Column G). Looking at the results, it seems that for automotive, remanufacturing stands out as the R-word engaging researchers (34% and 41%, see Table 3/Column C & G). Recycling is also high, with 28% (see Table 3/Column G). As for what seems to be in focus when it comes to circularity regardless of industry, Repurpose (38%, Table 3/Column H) and Remanufacture (31%, Table 3/Column H)

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have the highest percentages. It seems from these figures, that remanufacturing is very much driven by the automotive when it comes to research. The R-word “Reduce” has a high hit rate (see Table 3). As”Reduce” is used in common language, it often pops up in other meanings than the circularity perspective considered in this paper, which was showed to be the case while sampling the papers. To get a better view on the distribution of the other R-words, a percentage distribution was done with and without the “Reduce” result. The results for “Search string 3 in Table 2 can be seen in Fig. 1 and Fig. 2 and results for “Search string 4 in Table 2 can be seen in Fig. 3 and Fig. 4.

Fig. 2. R-word distribution for search string 3 Fig. 3. R-word distribution for search string (+Assembly + Circular) with Reduce included. 3 (+Assembly + Circular) without Reduce.

Fig. 4. R-word distribution for search string 4 (+Assembly + Automotive + Circular) with Reduce included.

Fig. 5. R-word distribution for search string 4 (+Assembly + Automotive + Circular) without Reduce.

In Fig. 2, where automotive has not been singled out, the words Recover, Repair, Reuse and Recycle have the highest percentages with Recover being the highest with 30%. With automotive added the same four R-words are leading, but with an almost even distribution between them of 18–22% each (see Fig. 4). Notable is that Remanufacturing grows, from 3% to 7% of the hits adding automotive in the search string.

3 Workshop Summary The data collected in the interactive workshop is summarized in three tables, related to the three questions that were used as guidance in the workshop discussions, see the method section. Table 4 summarizes the responses to Question 1, i.e., the actions taken

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today for adopting to the increased need for contributing to the transition towards a circular economy. Table 4. Summary of industrial activities ongoing supporting circularity in assembly. Area

# of comments

Activities

Remanufacturing

11

Different types of reuses of material, item leftovers/components, reconversions, disassembly is started being looked on as well as processes, techniques, and business applications for remanufacturing

Recycling

8

Recycling of plastics, aluminum, heat from machines, packaging, and design of the product

Material processes

5

Material choice and redesign of products based on circularity, set demands on material from suppliers and recover material from scrap

Strategies / Routines

5

Remanufacturing, reconfigurable assembly, use of tools longer, demands on machines for energy efficiency, reduce waste

Projects

3

Actively trying to highlight and participate in projects in the field

In Table 5, data collected in the workshops related to Question 2 are presented, about identified opportunities related to assembly aspects of circularity. Table 5. Summary of identified opportunities supporting circularity in assembly. Area

# of comments Activities

Reset, decrease, avoid 16

Choose material that can be recyclable or reusable, reuse material, use digitalisation e.g. IoT and sensors to save data and reduce waste, reduce the number of product variants and decrease the buying of machines, increase optimal life time for equipment e.g. through choosing critical components, choose the right equipment and lease equipment, design for increased service and for circularity e.g. separate material and components, increase the cooperation to facilitate circular flows and introduce reconfigurable production

(continued)

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Table 5. (continued) Area

# of comments Activities

Recirculate

11

Build the digital twin with information from the use face, refurbish, remanufacture, and refresh, find a strategy for water-reuse, increase competence within remanufacturing and disassembly, design for reuse, disassembly in house, reuse components, remanufacture parts for green field production, start remanufacturing, optimize service and repair, recirculate products for same or other product lines, separate material for better reuse

Servicicate

5

Create a process for scrap material that designers can use, find economy in reuse, find businesses that matches circular opportunities, lease components that are bought today

Design

4

Design for disassembly, support designers in material and design choices for efficient assembly and disassembly e.g., consequence analysis for future environmental impacts and cost for bad design choices

As a summarizing discussion in the workshops on Question 3, information related to what is possible to do right now was collected, and these are presented in Table 6. Table 6. Summary of industrial activities that is possible to do now for circularity in assembly. Area

# of comments

Activities

Collaborate

8

Involvement in conceptual studies, collaborating in the value chain and suppliers, support in the upgrade of equipment, closer collaboration between R&D and production, and contact near departments to set new Key Performance Index’s (KPIs)

Education

5

Better education and include sustainability and circularity e.g., through a sustainability platform and learn by starting projects in this field

New ways of working

4

Design smart factories, internal reuse, recycle and sort more, lease tools, trucks, and equipment

4 Theoretical Summary The theoretical summary is based on selected papers identified in the literature search as well as some snowballing for complementary sources. The selected papers are mainly identified through the keyword’s linkage to assembly, circularity, automotive and any of the R-words used in the literature search, primarily the four most commonly Recover, Repair, Reuse and Recycle related to automotive (see Fig. 4). The R-words are defined related to the area of assembly as presented in Table 7 inspired by the structure of Potting et al. [10] and adapted to the area of assembly (inspired by [13]).

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Based on the summary from the industrial workshops and the “R-words” from the literature search, the theoretical summary explores four main areas to further explore related to how automotive assembly is influenced in the circularity perspective: (1) Green transition, (2) Circular material, (3) Emerging technologies, and (4) Organisational issues. Table 7. Overview of “Rs” related to designing and running the assembly systems (inspired by [10, 13]) R-word

Definition related to an assembly system

Refuse

There is no need for assembly or disassembly activities, instead new types of components that removes the need of assembly

Rethink

Challenging the current state of the system, the product etc. This could be designing a product for assembly that contributes to a more intensive or efficient use of the assembly system, e.g., sharing the assembly equipment in several circular loops covering the product life cycle, for example assembly, service, and repair

Reduce

Design smart and efficient assembly systems that increase efficiency and reduce investment costs

Reuse

Design assembly cells that manage different variants with the same tooling, equipment, digital support, and layout

Repair

Design a product that after assembly can be disassembled, repaired, or maintained, and thereafter reassembled again

Refurbish

Design an assembly system that can manage a renovated product with new tolerances after restoring or upgrading the parts

Remanufacture Design an assembly system that can manage used parts in assembly of a new product with the same function Repurpose

Design an assembly system that can be adopted to new types of product requirements, e.g., new materials, parts, or technologies

Recycle

Design an assembly system that can manage recycled materials and/or components in parallel with virgin material and/or new components

Recover

Design an assembly system that reduce waste, such as no packaging material, and if waste occurs it should contribute to energy recovery and the customers sustainability KPIs

Resilience

Design an assembly system that operates efficiently when assumed prerequisites are changed. Examples are societal changes, adapting to emerging technologies, capability to manage changes in customer needs, new product requirements, material shortages as well as new regulations

4.1 Assembly Aspects in a Green Transition Since the green transition demands a reduction of use of novel material, there is a need for identifying what can be reused in different ways. For automotive companies,

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planning of new production systems needs to analyze the possibilities as well as risks when reusing modules in the production system in the final assembly lines [17]. They conclude that the effectiveness and efficiency of reusing modules in the assembly system depends on at least six identified criteria: (1) the frequency of reusing, (2) risks related to reconfigurations, (3) the cost-benefit ration, (4) the effectiveness of the reused module, (5) the effectiveness of the section in the assembly line, and finally, (6) different types of human factors [17]. However, different scenarios related to reusability of modules in assembly lines need more research to support future prioritization [17]. Another way of contributing to a green transition is to remanufacture products, which demands a capability to disassemble the products, which is not as trivial as implementing a reversed assembly process [18], since joining technologies are usually selected for a smooth and efficient assembly process of a new product. However, it is crucial to identify the components with a high unit value, to be able to develop and implement a cost-efficient disassembly and remanufacturing system that can decrease the production cost and balance the workforce to the operational tasks [19]. Sustainable design of production systems aims to contribute to economic growth for the company and produce environmentally friendly products managing different users and stakeholder’s requirements [20]. To conclude, managing assembly aspects in a green transition within the automotive industry demands a system approach managing both the emerging requirements from regulations e.g., increased reusability of investments as well as material or components and at the same time secure economic growth and competitiveness for the company. 4.2 Assembly in a Circular Material Perspective Sustainable factors such as emission evaluation and social welfare are identified as a gap when it comes to supply chain operations that is enabled through support by digital twins [21]. Furthermore, it is argued that traceability achieved by a digital twin might help companies to achieve better performance in reverse logistics as well as when predicting quality, quantity and timing of the products that are coming back. Nguyen et al. [21] indicates that new research is exploring the possibility of achieving resilience in the supply chain by real-time monitoring with support from digital twins. However, Sergio et al. [19] argue for the necessity to estimate the cost for reverse logistics, both economically for the company but also from an environmental point of view. To conclude, recycling and reusing materials, shipping back to the original supplier might not always be the most environmentally friendly solution. However, decentralized logistic areas [22] can support an efficient material support to an assembly line. 4.3 Emerging Technologies Supporting Circularity in Assembly There is a trend in research called Line-less Assembly Systems (LMAS) which utilize emerging technologies in its global approach, such as Autonomous Mobile Robots (AMRs) and collaborative robots in human-robot collaborative (HRC) layouts to achieve resilience and flexibility in the production system [23]. In parallel, emerging technologies are evolving into a human-centric approach for assembly, where the technologies empower operators to be in control of robots and machines in a future resilient and smart

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factory [24]. Both trends need a digital infrastructure that supports the usage and implementation of emerging technologies, which are shown to be challenging to implement in an industrial setting due to several reasons such as computational capabilities, new hardware, knowledge, training, support, and maintenance of software as well as hardware [25]. Sergio et al. [19] argue that simulation tools for assembly layout planning still need to be more developed to support circular decisions. Ongoing research indicates possibilities to utilize web-based digital twins to trace energy consumptions and understand the control parameters influencing the energy consumption to promote sustainability in an assembly line [26]. The use and implementation of advanced technologies can support a sustainable production system and contribute to an increased productivity as well as support an assembly system that can manage requirements as made-to-order [20]. Automation can be a support in managing both flexibility as well as agility in an assembly line that manages a high mix of products in low volumes [27], which might be the case in made-to-order. However, if the assembly should be automated-assisted, there are gaps to manage in the implementation in industry [27], such as assessment routines for identifying which type of flexibility needed, implementation of sensor-based automation to secure a safe workplace in collaboration with robots, knowledge about emerging technologies, intuitive interfaces to digitalization tools etc. To conclude, emerging technologies can support assembly from a circular perspective, but there are several implementation challenges to manage in an industrial assembly layout. 4.4 Organizational Aspects on Assembly in a Circular Industry Implementing industry 4.0 and different emerging technologies in a final assembly line indicates a need for deeper understanding on the human aspects in such layouts, and how the technologies and the operators are interacting to reach operational flexibility [28]. When implementing circular perspective into an assembly line, disassembly tasks might also be integrated into the production system, which might contribute to more manual tasks as well as a rebalancing of workforce between different operational tasks [19]. In manual tasks, such as assembly, there is a lack of research related to digitalized assistance for information sharing to operators in a systematical way during the ramp-up of a production [29], compared with a lot of ongoing research related to cognitive aspects for operators in volume production [29, 30]. To conclude, assembly in a circular perspective will challenge the operator from both a technological perspective as well as from a cognitive perspective. But it seems that the future operators also must be able to judge the value of products in a circular production system as well.

5 Discussion By combining the literature review, with the workshop results and the literature in the theoretical summary, the authors explore how the automotive assembly is influenced by the circular perspective and what challenges that occur in the transition to circularity which was the purpose of the paper.

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The responses in the workshops include many of the R-words that were highlighted in the literature study [10, 13], such as recycle, reuse, repair, refurbish and remanufacturing but not explicitly resilience but identified a need for education to manage future requirements. The companies voiced that reusing materials or remanufacturing is being done, and that disassembly is more in focus than before circularity impacted their operations. Remanufacturing is one term that stood out from the literature review as prevalent in connection to automotive, and “Reuse” was one of the R-words with high results (18% when removing “Reduce”, see Fig. 4). Redesign was also mentioned (see Table 7), which can be categorized as part of the “Rethink” perspective. Here, “Rethink” can also be linked to the identified activities related to changes in the material processes with new demands on the suppliers as well as recover material from scrap, contributing to an ability to build resilience in the value chain [21] or in an automated assisted assembly layout [23]. However, “Rethink” has a very low hit rate in the literature review, and it might be that neither companies nor researchers categorize redesigning activities as “Rethinking”, especially when reusing assembly modules in a production line [17]. Recycling and Recover was mentioned several times, which is also supported by the findings in the literature review. It is, however, unclear how the companies use the terms, as they can be used interchangeably if not properly defined. The impact on the production system was also highlighted, with production systems components like tools and machines being mentioned as enabler for energy efficiency [26] and flexibility [23, 27, 28]. This is supported by the “opportunities” listed in Table 6, where Digitalization and digital twins are prevalent in the discussions, which is supported by the theoretical frame [21]. And again, redesign and design for disassembly and remanufacturing is mentioned, highlighting the important link between product design and the success of the production system. The workshops indicate that there are ongoing strategic activities related to remanufacturing which corresponds to the literature search where that is one of the top four perspectives. Here, the literature indicates a need for developing supportive solutions to manage low volumes in a high mix [27], which categorizing the products entering a remanufacturing process [15]. Circular business support frameworks and tools are mentioned in the workshops as possibilities to manage the transition towards circularity, for example creating design support for designers to reuse scrap materials or leasing machines. This might be part of the Rethink perspective, which as stated before lacks findings clearly linked to the assembly perspective for the automotive industry in the literature search. Looking at digitalization many companies know that advanced technologies are needed to utilize to be competitive as well as manage to be a supplier to the automotive industry. Many opportunities are possible to explore through the utilization of emerging technologies connected to Industry 4.0. However, the implementation of the technologies is difficult and demands skill and infrastructure, which challenge many companies to identify how to begin [31, 32]. Adding on the trend towards more human-centric production, known as Industry5.0, one of the main challenges for the implementation is to understand the knowledge demands for humans in the system [33]. This means that methods and methodologies should be developed to improve the facilitation of design production systems, such as assembly activities, so that humans can be supported [6]. The workshops indicate an industrial understanding that the way forward is in-line with

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this due to the insights of a need to develop new ways of working utilizing the possibilities to digitalize activities, extend collaboration and up-skill the organization. Unfortunately, this study has not been able to address how “Reduce” is perceived by automotive in assembly settings, as the term is obscured by its common usage. “Reduce however is the focus of many rules and regulations impacting automotive, and for many or the R-words, like remanufacture, repurpose, and recycle, the sole purpose is to “reduce” waste, energy consumption etc. It would be interesting to follow-up the quantitative literature review with another quantitative literature review, focusing on selected papers with a “Reduce”-perspective, and follow how these link to the other R-words to get a better understanding of how “reduce” is defined and possible to achieve in an assembly setting.

6 Conclusions Assembly, and specifically automotive assembly, is influenced by circularity from several different perspectives. Already in the product design process, decisions contribute to the future possibility to act circular. Furthermore, the challenges to utilize digital information and manage both the assembly as well as disassembly in a productive and quality assured way must also be considered. Based on the literature search including the industrial contribution in the workshops the following challenges needs to be considered to manage the green transition: • Companies in general, even automotive companies, are revising their overall operation to manage the ongoing green transition, but there is no common understanding in how to categorize the activities related to the theoretically identified Rs. • Circularity increases the need for “DfX”, especially in assembly/disassembly operations, but more industrial examples illustrating the implementation effects are needed. • Automotive has the same circular perspective prioritization as other manufacturing industries, but remanufacturing might be slightly leading within this industry. • Digitalization is identified as an enabler, but support is needed to understand the holistic value proposition as well as the implementation challenges to manage a more diversified material and component supply chain to the assembly cell. This study has shown that there is a need to define and agree upon the meaning of different “R-words” within an industrial company or throughout a supply chain that includes assembly activities to manage a green transition. By agreeing upon definitions as well as their purposes contributing to the assembly tasks, it is expected to be easier to implement and utilize emerging technologies for traceability and flexibility as well as for support to the operators. Acknowledgements. The research was carried out within the assembly production cluster; the network and support for running the cluster work is gratefully acknowledged. Thanks to Hanna Lindén and Jutta Hildenbrand that held the circular economy workshop where the empirical results for this article were gathered. Furthermore, the SPARK research environment at Jönköping university funded by the KK-foundation is acknowledged.

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Circular Production Equipment – Futuristic Thought or the Necessity of Tomorrow? Malin Elvin(B)

, Jessica Bruch , and Ioanna Aslanidou

Mälardalens University, Hamngatan 15, 63220 Eskilstuna, Sweden [email protected]

Abstract. With a growing population and increased use of resources, there is an urgent need to transform towards sustainable production in order to stay competitive. Prior studies suggest that circular thinking positively impacts the environmental impact of products. However, few studies have investigated the implications of applying circular thinking to the design of production equipment. We address this research gap by looking at what circularity is and how it can be perceived in the context of production equipment. Our research reveals that different circularity requirements need to be implemented in different phases of the life cycle of the production equipment. However, to succeed the requirements need to be considered already early in the design phase of the production equipment. Further, since the development of production equipment is a co-creation between the equipment with the manufacturing company, i.e. users of the production equipment. The circularity thinking between the two partners needs to be aligned and coordinated. Our findings emphasise the need for a holistic approach with system thinking implemented early in the life cycle of production equipment. Keywords: Green design · Production Development · Manufacturing Technology · Sustainability

1 Introduction In a world with a growing population and increasing resource use [1], it is becoming a more pressing matter for companies to reflect upon how to make less impact on the environment to remain a competitive business. With EU goals set of being the first climate-neutral continent and doing so by 2050 [2], it will be especially important for industries to consider their contributions to achieve this. One way of reducing the environmental impact is to think circular, which revolves around value retention loops [3]. The area of circular thinking or circularity has been researched fairly much with over 500 000 published articles found at ScienceDirect between 2010–2023 if using the search string (“circularity” OR “circular”). Despite the high recognition of the area, the lack of knowledge and tools that companies can use to successfully implement it to make their production more sustainable is noticeable [4, 5]. Today, to tackle the problems to reduce waste in volume production programs such as lean production systems are implemented. Such programs do not necessarily need to © IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 159–173, 2023. https://doi.org/10.1007/978-3-031-43688-8_12

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be directly connected to sustainability but are an output of the work. Saetta and Caldarelli [6] describe it as “lean allows green”, but lean does not equal green. This means that the already existing tool lean has been adopted by several companies in multiple lines of business. Even though lean could have positive sustainability impacts, it is not developed to be a sustainability tool. To address the problem, variations of lean have been presented such as green lean [7, 8] and it has also been merged with circular thinking [9]. Such actions could bring not only environmental benefits but also economic benefits [10]. Other preferred efforts to implement circularity into production are through supply chain management [11] and also resource conservative manufacturing which also addresses the supply chain but includes other aspects such as product design as well [12]. Although most scholars would agree that lean is beneficial for sustainable production, it is not sufficient to achieve climate neutrality. To date, the majority of prior research has either focused on improving sustainability issues already running production [7] or focused on how the organisation overall could work with circularity mainly regarding their products [13]. However, a narrower perspective of the production itself and more specifically the production equipment is an unexplored area in comparison and requires further research [14]. In this article production equipment refers to the machinery included in a production system which also includes other factors such as human, information and material [15]. To a background where circularity is stressed as a key to climate neutral production [16], this conceptual paper aims to shine a light on circular thinking in the context of the design of production equipment, i.e. the activities that are conducted before new production equipment is built and implemented into production [17]. The limited attention to this issue in prior literature is somewhat remarkable because sustainable improvements in production equipment can be an important enabler to achieving a more clean and climate neutral production [18]. The purpose of this paper is to advance the understanding of how circularity can be applied to the design of production equipment. This includes both the sustainable point of view as well as the economical even though the focus will be on how to decrease the environmental impacts. The focus of the research is the total life cycle of the production equipment including but not limited to the initial material use, commissioning, repairs and decommissioning of production equipment.

2 Method The study is based on desk research based on secondary data to map out and gather information on the area [19]. Collecting and understanding the existing secondary data is important to be able to conduct primary research [20]. The methods for collecting secondary data follows four steps [21] presented in Fig. 1. The desk research included an integrative literature review with both peer-reviewed papers and grey literature to map the area and expand the theoretical foundation of circularity linked to production equipment [22]. Including grey literature can be beneficial since it can contribute to gathering information from a wider range of sources [23]. To broaden the search the snowballing technique was used on the initial findings to explore more relevant literature leading to that time affected the sample size [24]. The initial term to search for information was

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circular, circularity, circular economy, sustainable, sustainability, production, production equipment and industry. Not all terms were used in all search combinations and were altered along the way to find more relevant literature based on the initial findings.

Fig. 1. Method used for collecting data adapted from [21].

The research method used for this paper is explorative to gather a broad view of the chosen area of research. Even though the research aims to be broad some limitations do apply. Firstly, the search engine used was mainly Elsevier’s ScienceDirect in combination with Google Scholar. This was to limit the number of findings from the search. Grey literature such as reports from the European parliament was found through searches with the search engine Google. Secondly, no geographical limitations were set to the literature. No consideration was taken concerning the size of eventual case companies or the area of business.

3 Circularity as a Concept This section begins with a description of the term circularity and presents definitions of circular economy (CE) followed by an explanation of the R-imperatives that can be used to achieve circularity. Moreover, key things to consider when adapting the design of production equipment towards circularity are presented starting with challenges identified for implementing circularity including a brief introduction to the collaborative aspects that need to be considered when implementing circular thinking. Lastly, a part regarding the effect of digitalization on circularity is then brought up. 3.1 Circularity and Circular Economy The word circular describes the state of a shape and is not particularly linked to sustainability according to dictionaries [25]. The connection to sustainability has been made in later years with the uprising of CE [26]. Still, the connection does not make it synonymous with sustainability or directly linked to sustainable development. Publications in CE that identifies themselves with these two areas are only 38% [27].

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There are many ways and takes on CE and in Table 1 some definitions of CE are presented to give an understanding of the variations. Although differing in their point of view and level of detail, all the definitions point towards a framework for extending the life cycle of products and minimising their impact on the environment. The definitions also have diverse perspectives regarding the focal points and could be divided into three main categories: Technical, social and environmental, which is in line with the triplebottom-line (TBL) [33]. Depending on the setting of publication the focus seems to address the area that is most interesting for the scene but does not exclude either. The concept also has various descriptions used by the industry, even though they might not refer directly to the expression CE [34]. CE also plays an important role in connection to the UN’s Sustainable development goals (SDG) [35]. All goals, except one, are related to CE [27]. Walker, Opferkuch [36] found that companies perceive CE as an overlaying concept that is implemented to achieve sustainability and the SDGs and the differences in the concepts are not a priority to consider even though a strong driving force for implementing CE still is the aspect of economic growth [27]. Table 1. A selection of CE definitions. Publication

Definition

European Parliment [28]

“The circular economy is a model of production and consumption, which involves sharing, leasing, reusing, repairing, refurbishing and recycling existing materials and products as long as possible. In this way, the life cycle of products is extended.”

Kirchherr, Reike [29]

“We defined CE within our iteratively developed coding framework as an economic system that replaces the ‘end-of-life’ concept with re-ducing, alternatively reusing, recycling and recovering materials in production/distribution and consumption processes. It operates at the micro level (products, companies, consumers), meso level (eco-in-dustrial parks) and macro level (city, region, nation and beyond), with the aim to accomplish sustainable development, thus simultaneously creating environmental quality, economic prosperity and social equity, to the benefit of current and future generations. It is enabled by novel business models and responsible consumers.”

Ellen MacArthur Foundation [30] “The circular economy is a systems solution framework that tackles global challenges like climate change, biodiversity loss, waste, and pollution.” (continued)

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Table 1. (continued) Publication

Definition

Oxford English Dictionary [31]

“circular economy n. an economic system in which the journey of a product, material, etc., leads back in some way to where it began; (now esp.) a system or process which seeks to minimize or remediate harm to the environment by recycling, reusing, or regenerating products or materials, as a means of reducing waste and more sustainably or efficiently continuing production; cf. Linear economy n.”

Korhonen, Nuur [32]

“CE is a sustainable development initiative with the objective of reducing the societal production-consumption systems’ linear material and energy throughput flows by applying materials cycles, renewable and cascade-type energy flows to the linear system. CE promotes high value material cycles alongside more traditional recycling and develops systems approaches to the cooperation of producers, consumers and other societal actors in sustainable development work.”

3.2 R-Imperatives The R-imperatives is a term that often occurs when discussing circularity [29]. The most common of them all is the 3R:s reduce, reuse, and recycle, but it could occur in several ways with slight differences in both amount and terms starting with R [29, 37]. In Table 2, a more extensive framework with 10R is presented and it could be argued that this includes the whole life cycle in a better way [38]. The higher up in the table the circularity increases and becomes closer to a CE and further away from a linear economy. These are currently mainly adopted to fit products and are in need of adaptation to fully suit production and production systems [39]. First Segment: Smarter product use and manufacture. The parts described in the first segment of the 10Rs, refuse, rethink and reduce, could be linked to the design phase in the life cycle and to some extent the investment phase. Not and investing hazardous materials [37] and to make the need for new equipment redundant [38] can both be referred to as a part of refuse. It is in the design phase the most significant implications of circularity can be found since the impact in this stage is locked into the product [40]. In these first steps of the development of production equipment, the supplier can be involved to share knowledge and can address eventual problems early [41]. Second Segment: Extend Lifespan of Product and Its Parts. The second segment involves the most steps which indicate that prolonging life of production equipment could be done in several ways. One of them is remanufacturing which could be defined as “restoration of used products to a like-new condition, providing them with performance characteristics and durability at least as good as those of the original product.” [42]. There are tools to calculate the optimal time to remanufacture production equipment

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for the most profit considering the production plan, remanufacturing plan and the spare parts used. In addition to this, the cost can be analysed to decide whether to invest in new equipment, recondition or continue to run the machines with a higher risk of breakdown [43]. However, research is needed to also evaluate the sustainability part of these actions and not only to find economic benefits [44]. Further, the tool is presented to be implemented in the later parts of a production equipment lifecycle as a sort of maintenance and not in the primary design phase. Third Segment: Useful Application of Materials. This segment is the last and the actions could also be considered as a last resort and is close to the thinking of a linear economy which also can be seen in Table 2. Recycling should still be high-grade since it is the most used circular strategy [38]. Table 2. R-Imperatives. Redrawn from [38]. Strategies Circular economy

Increasing circularity ↑

Smarter product use R0 Refuse and manufacture

Make product redundant by abandoning its function or by offering the same function with radically different product

R1 Rethink

Make product use more intensive (e.g. through sharing products, or by putting multi-functional products on the market)

R2 Reduce

Increase efficiency in product manufacture or use by consuming fewer natural resources and materials

Extend lifespan of R3 Re-use product and its parts

Re-Use by another consumer of discarded product which is still in good condition and fulfils its original function (continued)

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Table 2. (continued) Strategies R4 Repair

Repair and maintenance of defective product so it can be used with its original function

R5 Refurbish

Restore an old product and bring it up to date

R6 Remanufacture Use parts of discarded product in a new product with the same function

Linear Economy

Useful application of materials

R7 Repurpose

Use discarded product or its parts in a new product with a different function

R8 Recycle

Process materials to obtain the same (high grade) or lower (low grade) quality

R9 Recover

Incineration of material with energy recovery

3.3 Implementing Circularity Circular thinking needs to be implemented in the organisational structure at companies in order for it to have an effect [45]. However, this could involve needing to overcome barriers including among others institutional, social, technological and market or economic barriers [34, 46]. Another term used in the same context is circular tensions [47] does not necessarily need to be perceived as something negative but could occur due to unsystematic work towards CE or simply the conflict between the circular and the linear thinking of economy. These tensions are divided into three categories, institutional-industrial, organisational-technical, and ethical, normative and behavioural. The presented barriers and tensions interconnect regarding where they appear such as on the institutional level. Another important factor for implementing circularity is laws, policies, regulations and the impact of governments [27, 34, 48]. In addition to this, the supply chain perspective regarding CE and how the two areas are linked also needs to be discussed [49]. Supply chains have long been considered as

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one-wayed strategies of how produced goods shall reach the end user which is referred to as the linear model or forward supply chain and when adding circularity the flow also goes backwards with the return aspect [50]. This introduced a new way of looking at the supply chain as multi-directional but also how the actors included should work together. The buyer-supplier relationship is important even without circularity involved [51] and by introducing elements such as return and reuse a new even more collaborative way of working is needed [50]. Circularity and Digitalization. Being on the edge with digitalization and new technologies combined with CE could lead to a competitive advantage [52] as well as improved sustainability [53–55]. Despite this, it is a rather unexplored area of what synergies smart production has with CE [56] and environmental sustainability [57]. Digital technologies to help improve circularity will be promoted by the European Commission [2]. However, there is a need for further collaboration between governments if digitalization will be able to serve as an enabler for CE [58].

4 Discussion In the following chapter several aspects regarding possible research areas and identified obstacles. It discusses to some extent how it could be addressed and certain aspects that could be of value to consider in the given topic. The structure starts with the implementation of circularity on a general level where the suggested framework also is introduced. This is followed by a discussion regarding the terminology and challenges specifically linked to this. Moreover, the possible topics related to the design phase are addressed which continues with a discussion on how collaboration and the general relation between supplier and buyer could be affected if circularity is implemented. Lastly, a short reflection about in what terms a successful implementation of circularity is measured. 4.1 Implementation An important aspect to consider is where circularity should be implemented in the life cycle and also how to implement it. Depending on where in the 10R framework the work focuses different tasks need to be considered. A suggestion is that all the levels should be considered already in the design phase of production equipment and have a plan for possible outcomes. This would require a lot of steps in the early phases, but all actions do not need to take place at the same time. In Fig. 2. a suggested framework is presented with the main part of the lifecycle of production equipment combined with the 10R framework as a guide. The framework was developed through input from the conducted literature study that combines the current practice with a common framework (10R) implemented to achieve circularity. For this paper, the four phases of Design, Investment, Operation and End of Life were chosen to illustrate the primary life of production equipment. There are several variations on how to present a life cycle depending on what needs to be in the focus [59]. Since design is considered as a key aspect of this conceptual paper it was chosen as the first step. The investment phase could include several parts but refers to

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the step where it is time to buy the equipment, the actual acquisition. Operation refers to all actions during the use of the production equipment up until the last part, end of life, which refers to the activities put in action after it has served its initial purpose. To achieve a circular way of thinking there needs to be activities done in every part of the lifecycle and suggested research input in each phase is also presented in Fig. 2. Here digitalization is seen as a tool to ease the work in every phase and therefore not connected to any specific part of the lifecycle. An overlay for this is the business models that could be seen as a tool as well or as a driving force to make implementations of certain circular activities possible. Among the R imperatives, remanufacturing was added as a separate step to show how it could be brought back in the cycle since it could be performed in more than one way. The presentation of the proposed research for CE is linear to show the relation to the thinking of the linear economy of today and to show that there is no need for a radical change of mindset. Instead, it aims to showcase that small changes in each step could contribute to a circular way of thinking. However, it could occur circular tensions in these steps if activities of linear and circular thinking collide [47]. The next step could be to look at it holistically with a system approach instead of separating the steps into smaller tasks and to see how implementation on a higher level could affect the equipment. Instead of looking at a component level, the fundamental structure of thinking will change when circularity is implemented. Since barriers such as institutional, social, technological and market or economic barriers [34, 46] it becomes a question of how much improvement in sustainability could occur. This is presupposing that all the involved stakeholders are willing to change their thinking towards circularity and collaborate with these types of questions. Terminology. Companies perceive CE as an overlaying concept that is implemented to achieve sustainability [36] and reach the SDGs, yet only 38% of the publications in the area identify themselves with either sustainability or sustainable development. This shows that the companies’ perceptions and the academia do not fall under the same understanding of the two concepts. This could be an issue when implementing the thinking of circularity and research needs to be aware of this when proposing frameworks to the industry. This in combination with the lack of a common understanding in industry due to vocabulary differences [34] makes the research within the area difficult to conduct since companies could have experience with the CE but are using a different terminology. This is an important aspect to consider since companies might be more aware of some activities linked to circularity than it appears because they are using other terms. A suggestion is to state the definition of circularity in the particular research context to avoid misunderstanding and misconception while collecting data. Another thing to consider is the use of EoL or spelt out as End of Life. This gives the impression that something is ending. A proposed term is instead End of Use which instead is more linked to the current abilities.

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4.2 Design for Circularity Design changes made on production equipment today will affect the end-of-life in 10– 20 years if not even more. Therefore, it is crucial to make it climate smart from the start since the consequences of the decisions will take time before it has an impact. Remanufacturing could be used at the end of the initial use of the production equipment. There are tools to evaluate if it is economically beneficial [43] but there is still a need for methods to evaluate sustainability [44]. If the possibilities of remanufacturing were considered already in the design phase it could enhance the opportunities when it reaches EoL by having a plan for what is possible and what is sustainable. When investing in production equipment, the optimal running time and maintenance could be declared just as any other specification to ease the work with remanufacturing. The main difference in the design for circularity is not only that the use phase needs to be considered in the initial design phase but EoL as well which makes it more complex than today. However, this does not mean it has to be more complicated. If looked upon as any other step in the design process it could be integrated with already existing methods as an extra step to setting design criteria. Just as the user is defined for the first life cycle the second life user could be described as well. 4.3 Relational View Collaboration seems to be a keyword whilst discussing successful implementation of circularity [50]. This part could be one of the most important for this paper to prove the point that there has to be work done on all levels with a holistic approach to a way of working with the production equipment throughout its lifecycle, presented in Fig. 2. This requires an exchange of information as well as a willingness to pass it on to other actors. If multiple stakeholders are involved in a supply chain expertise could be gathered from several areas to achieve the most sustainable and beneficial action in each step of the R-framework. By involving multiple actors, the equipment would not necessarily need to stay in-house when reaching EoL. This creates opportunities for a “second-hand”market for production equipment. Production equipment that is no longer needed could be sold to other companies, but this requires an interest in both selling and buying these types of machines which could require a change of mindset. New production equipment does not necessarily has to be newly produced. The relational point of view also becomes important in the early phases of the lifecycle. As discussed earlier there is a need for change from the start already in the design phase. To have all the competence needed to specify demands for production equipment that will have at least a second life requires experience and knowledge. 4.4 Measure Success Successful business measurements will have to change from economical aspects primarily to sustainable. Since the economic aspects are still a strong driving force it is of utmost importance that the economic benefits that can come from circular and sustainable methods are highlighted. Bensmain, Dahane [44] evaluated the optimal time for remanufacturing of production equipment seen to the cost and profit. The actions of

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Fig. 2. Implementation of circularity throughout lifecycle of production equipment

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remanufacturing do fall under actions that could be considered circular, but the model is also proof for circularity does not always equal sustainability. Thus, a fundamental part is to consider not only how something is measured but also what is measured. Prolonging the lifecycle might not be the best alternative to achieve the highest sustainability, but this does not mean that production equipment should be removed as something other than a last resort.

5 Concluding Remarks Circularity is a relatively new term when it is described in relation to sustainability and CE could be looked upon as an overlaying way of thinking but need specific tools in order to achieve it. There is a limited amount of research in the area of circularity connected to production equipment. New tools are needed to create a circular thinking for production equipment throughout its lifetime, and not only for the making of its parts. A suggestion is to plan for the aftermarket already in the design phase using a holistic approach with a system thinking for the lifecycle of the production equipment. This could include the material and the ability to recycle and reuse specific parts but also whole systems. Sharing is caring – what if that was the case for companies as well? Shared resources and knowledge could help to create a circular flow. A proposition for research is to start to map how both suppliers and customers of production equipment work with circularity and what their understanding of the area is today. Additionally, the information flow between the parties is essential to understand if a change of thinking from linear to circular would affect how companies work together. Lastly, studies should be conducted on tools and frameworks to achieve as high sustainability as possible with as little loss of profit (or preferably more profit) as possible. To reach the goal set by EU to become a climate neutral continent by 2050 [2] the change needs to happen now with the production equipment that will be in use for several years. The work towards circular production equipment is in its early phase and it might be considered as a futuristic thought to have completely circular thinking. However, with the demands and regulations from legislators, it is becoming a necessity of tomorrow. Hence, this research aims to contribute with knowledge and an understanding of how circularity could be implemented in the praxis of today to achieve circular production equipment for the industry of the future. Acknowledgement. This research is funded by the VINNOVA as a part of the project Green Design.

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Systematic Green Design in Production Equipment Investments: Conceptual Development and Outlook Seyoum Eshetu Birkie1(B) , Zuhara Zemke Chavez1 , Emma Lindahl1 , Martin Kurdve2 , Jessica Bruch3 , Monica Bellgran1 , Lotta Bohlin1 , Mikael Bohman5 , and Malin Elvin4 1 KTH Royal Institute of Technology, Stockholm, Sweden

[email protected]

2 RISE Research Institute of Sweden, Gothenburg, Sweden 3 Mälardalen University, Västerås, Sweden 4 Mälardalen University, Eskilstuna, Sweden 5 AstraZeneca Sweden Operations, Södertälje, Sweden

Abstract. This paper explores the concept of green design in the context of production, focusing on investment projects for production equipment design and acquisition by a manufacturing firm. Research towards making manufacturing and production related activities more sustainable is increasing. In the manufacturing sector, environmental sustainability tends to be more commonly approached from the operations perspective. However, the decisions taken in the design phase of the production equipment significantly impact the operations phase. Therefore, proactive design approaches for sustainability applied in product design settings could be transferred to the design of the production equipment to build in green aspects from the outset. This study explores the research questions of what green production equipment design entails and how the concept of green design has evolved in the context of production. Overall, this conceptual paper highlights the importance of incorporating green design principles from the outset of the production design. Transferable methodological issues are also explored for further detailed investigation in the production equipment design context. Strong collaboration between equipment suppliers and the buying manufacturer that aims to integrate sustainability as part of requirements is proposed as an enabler for the way forward. The paper also provides insights into the evolution of the concept in this context for possible future research. Keywords: Production equipment · Design for sustainability · Green design

1 Introduction As knowledge about the impact of industrial activities on the planet improves, the breadth of the research discourse on sustainability and green considerations in manufacturing and production also increases [1, 2]. This development could be viewed concerning © IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 174–188, 2023. https://doi.org/10.1007/978-3-031-43688-8_13

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the integration of conceptual elements, theoretical frames and perspectives adopted, methodologies and tools applied, and the broadening scope of empirical research and industrial application. In the manufacturing context, environmental sustainability (commonly referred as green) is often approached from the operations execution perspective rather than from the design (or development) perspective [3]. However, it is well known that decisions taken in the design phase greatly impact the operations phase. First, studies have established that broad-based proactive approaches aiming at different activities from design to manufacturing to managing supply chains are important for achieving tangible sustainability gains [4]. Second, decisions made during the design phase are found to limit most alternatives applicable in subsequent stages. These proactive approaches frequently applied in product design could hence be transferred to the design of the production system to build green aspects from the outset. The production system is generally highly complex, comprised of facilities, equipment and machines, people, material, information, logistics, and management systems to organize and manage the production system. Although design for X approaches could be applied to the production system broadly [5], they suit the investments in production equipment and machines very well since they are often one-piece products or interconnections of several stand-alone machines. Consequently, a green approach to production equipment design could be a promising way to reduce the environmental impact of the production system during not only the operations phase but during its whole life cycle, and the life cycle of items produced using the manufacturing equipment. Many manufacturers engage in a continued effort of designing and acquiring production equipment, which involves multi-competence team organization for large manufacturers, and is very resource intensive. Growing interest has been noted recently in leveraging environmental impact reduction potentials through managing the production equipment design process. This could be reasoned out from two perspectives. Fulfilling sustainability goals requires systems thinking; in manufacturing, one needs to consider not only the design of the product but also the production equipment from the early design phases. Large manufacturers also continuously engage in these high-investment projects. Lack of a systematic means to bring sustainability as part of equipment design requirement makes green design in production a critical area that must be investigated. Considerations regarding the green design approach on production equipment and systems investments could be viewed in terms of product features, material use, and energy consumption issues as generic areas of interest within the green design. However, parameters of interest within each of these and their potential interdependencies are complex. Early considerations on green design often focused on waste prevention and (hazardous) material avoidance and reduction in fulfilling required features/functions of a product [6, 7] which apply to a broader context for even better value. Gong et al., [8] argue that green design should pursue maximum economic efficiency and minimum environmental impact. Green design is a broad (and, to some extent, contextually dependent) term that needs to be defined and described further. What does green design for sustainability mean? Does it include the broad concept of circular economy in production? Researchers have developed tools for green design of various products, including complex manufactured products [9]. A crucial stage in green product

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design is understanding how the end user uses it. In production equipment design, one needs to understand how the buying manufacturer uses the production equipment. Such integration includes aligning important and pre-established ways of working, such as green design integration to lean product design and production [9] for a holistic green manufacturing system [10]. This conceptual paper explores the tenets of green design in the context of production, particularly production equipment investment. The study sets out to answer the following research questions: RQ1-What is green production equipment design? RQ2- How has the concept of green design evolved in the context of production, and what lessons can we learn from this evolution?

2 Green Design for Better Sustainability in Production Context 2.1 What is Green Design? In general, the term green design has been applied in product development, especially for consumer products [7] while to a lesser extent to the design/development of production equipment and machining tools as part of the overall production system design process. When applied to mechanical equipment (hardware), green design implies considerations of environmental friendliness during the design phase. However, as explained later, green design must be extended beyond the design phase to include the development process to volume production. As many other research publications do, the description assumes that being environmentally friendly is a feature that different stakeholders, including the end user, appreciate. From the description, extrapolations can be made regarding what needs to be included to ensure environmental friendliness and economic efficiency. It could imply that some sustainable production principles and practices must be followed [11]. Here manufacturing and production are used interchangeably. To establish a common standpoint, we first look at different definitions of concepts in the vicinity of green design and design for sustainability. One of the older definitions of green design comes from OTA [6]: “a design process in which environmental attributes are treated as design objectives, rather than as constraints. A key point is that green design incorporates environmental objectives with minimum loss to product performance, useful life, or functionality”. Dowie [12] states that green design entails design such that products can be disassembled and recycled. Similar ways of defining green design with a focus on the product can also be found also in recent papers, such as: “to make the products environment-friendly, safe to use and energy and resources saving” [13]. Similarly, sustainable manufacturing and green manufacturing are respectively defined as: “the actions, initiatives and techniques that positively affect the environmental, social or economic performance of a firm; helping to control or mitigate the impacts of the firm’s operations in the triple bottom line” [11], and “the production processes that use input with relatively low environmental impacts” [13]. A relatively more comprehensive concept than green design is design for sustainability. Design for sustainability refers to the triple bottom line where social and economic needs are as important as the environmental dimensions. So, it can be understood as “to

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address all dimensions of sustainability, looking at bigger systems and asking more fundamental questions about consumption and production” [14]. Design for sustainability can be defined as “technical and product-centric design towards a focus on large scale system-level changes, in which sustainability is understood as a socio-technical challenge” [1], and “a rational and structured process to create something new for solving sustainability-related problems” [15]. Circular product design strategies, often called R-strategies, are developed based on work from [16, 17]. Re-think strategy is often premiered as one of the top strategies of the resource hierarchy perspective and could be seen as a green design feature at a relevant system level. Key elements of interest can be extracted from these and similar definitions regarding critical principles, strategies, and relevant approaches for green design (see Sect. 4.2). 2.2 Developments Around Green Design According to extant literature [1], the early examples of green design in practice mainly focused on lowering environmental impact by redesigning individual product qualities. Environmental legislation, corporate image and public perception, consumer demand, and rising waste disposal costs pushed manufacturers towards green design in the ‘90s [12]. Companies with proactive strategies for sustainability have recognized that instilling structured ways of environmental impact improvement is needed to maintain better financial performances [18]. However, what can be included as environmental improvement action in manufacturing companies has broadened from time to time through understanding the cause-effect chains and interdependencies happening in industrial activities concerning different flows: material, energy, and information. For example, internal energy losses in manufacturing equipment might appear very small compared to the more visible losses due to planning or usage-related inefficiencies [19]. However, with a holistic view, considering a production line that the equipment is part of could reveal interdependencies that could exacerbate even external losses if not addressed at the root-at-design stage of equipment. These may even indicate the further potential of saving both energy (e.g., saving of hot exhaust gases) and material (e.g., choosing efficient equipment, or reducing chips and scraps as well as associated contaminations). Historically, green design is a dominantly product-focused approach. Primarily, it makes sense to focus on the design stage of a consumer product to facilitate source reduction and management of waste along the different phases of the product life cycle, including considerations for remanufacturing, as recognized decades ago [20]. It is also a relatively more manageable way to justify the business case of environmentally oriented initiatives, such as redesigning functional aspects of a product. Improving manufacturing processes with a green focus has eventually caught up. The trend seems to have followed a move from a reactive approach (e.g., how can part of an existing process be marginally improved to save energy or wastewater) to a more proactive approach, e.g., how can manufacturing equipment be designed so that it has the features to save energy or other inputs in its operational life [21]. This indicates an evident expansion of the green design focus, from mainly focusing on the product to the production equipment design. Green lean is another stream of literature heading for green production equipment or system design. Under this approach, the focus is given on integrating green thinking

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into process efficiency - improving initiatives to a limited extent [9], and the design of production for an efficient energy use at a production facility level [19]. Built-in technologies for pollution control are becoming common practices in manufacturing equipment and process design. In addition, green-focused design considerations beyond the manufacturing shop floor could be found, extending to, for example, design for multi-use and modularity of packaging in manufacturing [22]. A challenge observed in extant literature from a conceptual point of view is that with these developments and various points of departure, the conceptual underpinnings of a holistic “sustainability-dominant logic” [23] are often undermined. Batista et al., [23] offer theoretical propositions for a sustainability-dominant logic for circular economy and sustainable operations at product, firm, and supply chain levels which could be used further to explore developments for green production equipment and system design. This could have implications for the circular economy for green design by enhancing an upstream and proactive focus rather than just downstream-focused strategies such as reuse, repurpose, and recycle [24]. As mentioned earlier, the development can be explained using sustainability-dominant logic at the product, manufacturing firm, and production value network levels. Essentially the transition seems to be towards behavioral change-based thinking in design for system innovations leading to more sustainable future manufacturing [1]. In their paper, Gaziulusoy et al., [25] present a scenario method linking product development to the visions of sustainability at the societal level. The aim is for companies to relate product development to the organization and society, compared to standard product-centered design and development approaches. According to the authors, the method allows for generating innovation paths for product development to respond to changes for sustainability at the system level. Briefly, the answer to the question, what is green design? And what about its related concepts? It depends on the perspective, context, and overall societal changes and trends. The answer requires exploration across adjacent areas of application. That is why an explorative literature survey has been applied in this paper.

3 Methodology This study employed a systematic literature review [26]. Steps from planning the review to conducting and presenting results have been followed as prescribed in [27]. The review started by picking a few seed publications based on the intended research questions and researchers’ experience. These papers have been used to set search protocols and set initial keywords in the search query. Inclusions and exclusion criteria has been set as follows. Engineering, environmental studies, business, and management publications have been selected as a priority from subject areas. Based on the initial list, screening has been done to exclude medical and chemical subject areas. Only journal articles and book chapters written in English have been considered at this stage. Initial Search Query Include from title, abstract and keywords: (“green design *” OR “green process design” OR "eco* design” OR “sustainable production principles” OR “environmental design” OR “design for sustainability”).

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Exclude from title, abstract and keywords: (*buildings* OR medic* OR bio* OR agricultur* OR crop* OR chemical* OR *buildings* OR urban OR architectu* OR “food production”). The literature search has been conducted using the Scopus® database. We executed the query in Web of Science® and the results were much fewer compared to the ones obtained via Scopus. Being aware of also the possible limitations and considering that Scopus indexes a slightly higher number of journals than Web of Science, we decided to continue only with Scopus. The primary literature search focused on publications from the year 2000 to capture the most recent developments. According to Smart et al., [26], the output of the information search should be a full listing of articles and papers on which the review will be based. Skimming through titles and abstracts enabled us to shortlist for detailed review. In addition to the detailed review of the topically shortlisted papers, snowballing of essential papers, as suggested in Thome et al., [27], has been done to identify potentially relevant additional publications that were not included after the screening. Ultimately 69 titles from the main screening, and eleven titles from back- and forward tracking have resulted in 80 titles (as of 1 May 2023) for final review (see Fig. 1 for a summary of the screening process).

Fig. 1. The screening process for review

Figure 2 illustrates the trend of publications from the final shortlist covering the search period. The years that most reviewed titles were published in 2022 (14%), 2021 (10%), followed by 2017, 2013, and 2010 equally at 9%. Journal of Cleaner Production (50%) and Sustainability (Switzerland) (22%) journals contributed by far the highest proportion of papers included in the review. Analytical exploration has been done based on three areas of interest, as seen in Sect. 4. The first is the exploration and identification of tenets of green design. The intent has been to observe developments and change patterns in the application context. The second relates to methodological issues relevant to the investigation and discussion of

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Fig. 2. Distribution of reviewed publications per year

green design research. The third is the exploration of theoretical underpinnings relevant to the green design discourse with the intent of eventually establishing conceptual framing to guide future research in systematic green design for production equipment as well as the broader capital investment for developing or updating production lines.

4 Findings from the Literature Review 4.1 Tenets of Green Design Starting with the sample definitions presented in Sect. 2 and a preliminary review of the extant literature, one can observe that several aspects of green design become apparent. We consider asking these questions to support the exploration: (1) the bases of green design (what); (2) the purpose and motives of considering green design (why); and (3) methodological aspects: methods, approaches and tools of implementing different green design initiatives (how). The first two categories are presented in this section and in Table 1, while the how is described in Sect. 4.2 and in Table 2. The reviewed literature indicates that guiding principles and corporate strategies should guide how this process is organized for optimal results. However, environmental objectives are not sufficient to keep businesses viable. They must be coupled with reasonable economic gain and not compromise social objectives. Streamlined green strategies can help an outward (market interest) focused linkage for green design initiatives [28, 29]. On the other hand, principles [11] are more inclined towards creating coherence among different elements of design-related processes in the organization that also could include decisions and relations with suppliers. Principles and strategies for green design often manifest in practices employed or established processes (see Table 1). From the reviewed literature, it is apparent that green

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design is often seen as a structured process in which environmental objectives must be an integral part of the deliverables of the design process [6, 13]. While a large portion of the literature analyzed views this process within the realm of designing products such as consumer goods [13], a large-scale system view of design consideration is becoming a recent trend. Alternative terms such as design for sustainability [1] have been coined to reflect this broadening scope with a perspective that it should help address the underlying problem of un-sustainability [15]. Design for sustainability encompasses processes and sustainable design and production practices [30] related to, for example, eco-design, design for efficient machining, assembly and disassembly, green innovation, green manufacturing, and so on [3, 31]. Recent studies have emphasized the importance of organizational elements beyond purely technology-focused design thinking, such as information exchange, monitoring, and collaboration with suppliers (also known as green purchasing). Table 1. Summary of key tenets in green design What

Why

• Principles [11] • Strategies for green design: e.g., modular green design, eco-design [32] • Practices and people skills [30] • Established processes [9]

• Regulatory requirements: policies, local rules, global standards [33, 34] • Pressure from partners and stakeholders on environmental initiatives [35] • Performance improvement motives in multiple dimensions [28] • Company internal sustainability goals and pressure as drivers [36]

4.2 Methodological Aspects in Green Design Research This section provides an inventory of methods and approaches used in green design as compiled from reviewed literature. Diverse research papers present a toolbox of different methods and approaches (see Table 2). In most of the reviewed papers discussing tools for green design, notions of “eco-design tools” has been dominant as a collective name for various tools used to capture especially environmental aspects in design. Under the umbrella of this notion, variants of life cycle (LCA), environmental benchmarking and several others have been mentioned. Several of the reviewed papers discussed life cycle costing (LCC), multi criteria analysis as well as analytical hierarchy theory, value stream mapping (VSM). Table 2 showcases some of the identified methods and tools for green design in the review, and related implications. Some methods and tools are data intensive and time-consuming (e.g., LCA, LCC), and others (VSM) are more customized and hands-on but still with their own limitations [3, 4]. The methods can be applied individually or in combination to alleviate the shortcomings and support different purposes within the design of products and production systems.

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This approach invites hybridization [37], where it is relevant to analyze the design methods not as unified tunnels of the process but as toolboxes containing components that could be mixed and matched across methods. Table 2. Green design methods and tools in the reviewed literature Methods/tools

Purpose

Implication

• Industrial big data analytics • To design intelligent • Using IoT to collect and [38] equipment analyze environmental data • To estimate and improve the supporting sharing and • Life cycle assessment [3] environmental impact of collaboration with • IoT for energy efficiency manufacturing equipment [38] designers/suppliers • Life cycle cost analysis [39] • To visually assess manufacturing sustainability • Together with design for • Sustainable VSM, environment approach, it Environmental VSM [4] can be used for improving equipment design requirements • It allows to assess of the sustainability performance of manufacturing systems but requires developing a future state for improvement implementations

4.3 Theoretical Frames Accompanying Green Design Research The dominant theoretical perspectives in green design research reviewed in this study include the dynamic capabilities view, institutional theory, resource dependency theory, and relational view. A recent study has proposed a “sustainability-dominant logic” [23] to theorize the phenomenon surrounding green design and circular economy. From a dynamic capabilities perspective, integrating internal and external resources along with resource building and reconfiguration create dynamic capabilities with a stronger sustainability orientation [2]. In this framing, eco-design and green innovation foster market performance. Resource building and reconfiguration can be viewed in terms of hiring and developing environmental competencies, reconfiguring team arrangements in design projects, and improving processes for supplier relationships [28]. External resource integration can be interpreted to encompass orchestrating knowledge and competence resources from suppliers about materials, manufacturing process technologies, and environmental impact to embed better environmental performance as part of design requirements. While the technical competence of machine and equipment suppliers plays a vital role in designing more sustainable manufacturing facilities, mainly suppliers of production inputs for realizing the product (rather than the production process itself) have been emphasized.

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In line with the external resource integration idea of the dynamic capabilities view, resource dependency theory posits that companies rely on resources in their business environment much more than the resources they possess, and essential resources are acquired through collaborations and enhanced relationships. This is partly because it is not feasible to own and have control over all strategic resources. An area of interest for environmental collaboration in manufacturing is, for example, environmental information sharing, education, and support to machine suppliers. Supplier selection based on environmental certification and fulfillment of environmental requirements in design specifications could be exploited via strong external collaborations [30]. Theoretical frames on intermediaries in sustainability transitions are relevant to understand the role of equipment suppliers and other actors as intermediaries and platforms [40]. These intermediaries can positively influence sustainability transition processes [41] by linking their related skills and resources or by connecting transformation, transition visions, and future demands to create momentum for socio-technical system change [40, 42]. Intangible resources revealed as the result of collaboration and negotiations are sources of coevolution of business actors in their sustainability effort [29]. Similar ideas of having long-lasting relationships based on trust and collaboration, even in competitive contexts, are addressed in the relational view theory [43].

5 Discussion and Future Outlook for Green Design in the Production Context Design for sustainability has evolved from encompassing form and functional elements of a product in the early stages to include many socio-technical considerations and systems thinking to collaborative design thinking in recent developments [1, 15]. The application focus of a dominant sustainability logic has broadened to include levels from product to value creation/institution on the supply chain level, which seems consistent for design for sustainability and circular economy [23]. Building on the work of Batista et al., [23], we can argue, based on our conceptual exploration, that a sustainabilitydominant logic seems to provide a coherent frame of reference for understanding the phenomenon of green and sustainable production equipment design in the broader sense than just a singular product item. In its broader form, a sustainability-focused production equipment design encompasses collaborative approaches built on coherent strategies and principles for a synergistic environmental impact reduction from design (equipment and machines), manufacturing, and other support processes, including purchasing. In manufacturing equipment, the purchasing function is essential role in integrating of resources and capabilities from the manufacturer to those of the equipment suppliers involved in the investment projects, from design to commissioning to execution of the production facility. Challenges and knowledge or awareness asymmetries as to what is possible and required to convert sustainability ambitions into technical requirements and functional capabilities of new production equipment are inevitable. Working through close relationships to bring out higher levels of innovative knowledge towards a greener production equipment design shall be a way forward for manufacturers and equipment suppliers designing and commissioning the production equipment.

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Further developing and clarifying the definition of green design and related terms such as circular design, green lean, sustainable design, etc., and their relation to the application area of how to design production equipment or even the whole production system for environmental sustainability and circularity is an interesting area for future research. Expanding the requirements on existing production systems is forcing the industry to engage in also the renewal of their production systems. This renewal entails a shift in how production systems are designed and developed, including the physical setup of production equipment and machines and investments in infrastructure and facilities necessary to operate a factory. Integrating green targets as part of the requirements specification to be fulfilled in the designated production system is an important and practical way of managing the green design target. Material use, technology use for impact reduction for design and execution of the production system and change in awareness and competence development are needed for a more sustainable production environment. Collaboration among key value chain actors across both the product and production system lifecycle seems to be a significant evolutionary development of green production design [23]. Opportunities to keep production equipment efficient and environmentally sustainable – leading to the realization of greener products– are better exploited if sustainability has already been built into the production equipment. Small incremental changes in the operations phase will not influence the same level as will the initial design decisions. Collaboration in the manufacturing value chain often focuses on function in the execution stages, in both cases, not necessarily on sustainable design [43]. To a considerable extent, social sustainability aspects related to ergonomics and accident reduction/avoidance in manufacturing environments are recognized in production design requirements. However, it seems only recently that environmental requirements are on the agenda for equipment purchase decisions. Environmentally superior innovative ideas can be generated due to green design practices internal to the manufacturers and in interaction with knowledge and technology resources residing at the equipment supplier [2, 10, 15], which existing literature only rarely recognized. The design principles, practices, tools and approaches, despite their differences in scope or applicability, imply more or less a consistent message. That is, pursuing sustainability on many fronts to improve environmental impact of production activities is relevant. If there are good and applicable practices in product design, we should enhance and use them at broader scale for production equipment design for even better environmental value [5]. At the same time, we should not forget about the social and economic aspects of sustainability but rather embed them as much as possible in the design for balancing gains. The target customer and context of use for production equipment are different from that of consumer products. This focus requires special care about which tools would be more suitable, and what standards and legal frameworks can positively influence the move towards green production equipment design. The EU directives for eco-design have been points of interest in some reviewed papers. There is an opportunity to explore recent developments in policy instruments (e.g., harmonized standards of the EU), specifically in equipment design for manufacturing.

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6 Conclusion This paper presented a conceptual exploitation of an emerging thought and discourse for green production equipment design. Some dominant theoretical underpinnings supporting conceptual development within green production design include systems view and relational views that describe the phenomenon of how closely the actors involved in the production equipment value chain (design, acquisition, operation) have to work to leverage their higher-order competencies and capabilities for a greener production equipment design and execution. Both equipment suppliers and manufacturers buying and using the equipment need to engage in strong collaboration with green design as a top priority. Increasing awareness both at equipment supplier as well as manufacturer for green design appears an important element if collaboration for green design is to be effective. The exploration of key tenets of green design and identification of applicable theoretical underpinnings is an important contribution of this study to push development of detailed frameworks and guidelines to advance green thinking as main constituent in production equipment design. Cross-disciplinary design events are examples that could be a point of future research interest in this direction. The process of such guidelines development can be backed up by selective use of green design approaches. Different tools currently at use in product design have a potential to enable a more proactive application of principles and strategies for green design to a broader scope of green production equipment design. The investigation in this study can be regarded as a preliminary exploration. As such, limitations in terms of identification and stipulation of some essential details will likely be rectified in a continued exploration. For instance, the systematic literature review could be expanded, including additional databases and potentially non-English literature. Further research avenues include detailed conceptual analysis in line with a sustainability-dominant logic in green design and empirical exploration to further develop knowledge around the phenomenon [2, 23]. Together with conceptual developments, empirical investigation into the efforts of the collaborating actors is a vital element for expanding the discourse around green production equipment design. Acknowledgements. This research has been financially supported by the Swedish Innovation Authority - Vinnova (dossier no. 2022–01338).

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Towards a Circular Manufacturing Competency Model: Analysis of the State of the Art and Development of a Model Marta Pinzone(B) and Marco Taisch Department of Management Engineering, Politecnico di Milano, Lambruschini 4B, 20156 Milan, Italy [email protected]

Abstract. Nowadays the transition to circular economy represents a significant challenge for both manufacturing companies and society as a whole. Despite their importance, little attention has been given to the human aspects and the role that people-driven factors play in the transition. This study aims to address this gap by outlining a set of competencies for circular manufacturing, identified through an examination of existing literature and subsequently verified through semi-structured interviews with academic and industrial experts. In doing so, this research expands our current understanding of the essential competencies required for circular manufacturing. This knowledge can also prove valuable to practitioners seeking to identify the necessary skills for updating or creating new job roles, assessing employee competency levels, and implementing training and improvement initiatives to bridge significant gaps. Keywords: circular economy · sustainability · competency · competence · skill · manufacturing

1 Introduction In recent times, there has been a growing global interest in the concept of the circular economy. Recognizing the vital interplay between the environment and the economic system, various stakeholders such as policymakers, companies, consumers, and research centers have increasingly focused on the circular economy as a viable alternative to the traditional linear economic model [1]. In the pursuit of sustainability, the adoption of closed-loop production and consumption cycles (e.g., reduce, reuse, recycle, recover, redesign, remanufacture) has been actively promoted within the manufacturing sector. Numerous frameworks and practices have emerged to support manufacturers and their value networks in implementing circular products, processes, and business models [2] to extend the lifespan of products and eliminate waste [3]. Nonetheless, the transition to circular manufacturing continues to face several challenges [1]. Notably, limited attention has been given to the “human side” of this transition © IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 189–199, 2023. https://doi.org/10.1007/978-3-031-43688-8_14

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and the pivotal role of people-driven factors [4–6]. Specifically, even though it has been highlighted that circular manufacturing requires new skills and the lack of competencies is often cited as a key barrier (e.g. [7]), only a few studies have delved into the specific competencies required for this purpose (e.g., [8–10]). To contribute to fill this gap and support interested stakeholders in progressing towards circular manufacturing, the present research study aims to answer the following research question: “What competencies are needed by industrial companies for the transition to and implementation of circular manufacturing?”.

2 Research Design To achieve the above-mentioned objective, the research process was organized into two main phases: 1) literature review; 2) expert interviews. The materials and methods applied in each phase are illustrated in the following paragraphs. 2.1 Literature Review The literature review was carried out following the recommendations of the PRISMA 2020 statement. Consequently, the initial step involved formulating the research question that this study aimed to address. As previously mentioned, the research question is as follows: “What competencies are needed by industrial companies for the transition to and implementation of circular manufacturing?” To compile the review, articles were sourced from the Scopus database using a specific keyword combination. The search criteria involved looking for articles with the terms “competenc*” or “skill*” in the title, and “circular” or “green” in the abstracts, titles, and keywords. Only articles, reviews, and conference proceedings written in English and published between 2012 and 2022 were included. Initially, 394 items were identified through this search. Subsequently, the results underwent three refinement steps: • Step 1 – Application of Scopus database filters on subject areas and keywords to exclude not relevant items. For instance, papers related to nursing, medicine and hospitality were excluded. After the first step, there were 193 papers in the sample. • Step 2 - Screening of titles to exclude items that did not specifically address skills and/or competencies, lacked a focus on circularity, or were not centered on workers and professionals. • Step 3 - Reading of abstract and full text and exclusion of not relevant items. In this step, the same criteria used in step 2 were applied. After completing these steps, a total of 14 papers remained in the sample. Additionally, references from these identified papers were reviewed, leading to the retrieval of three additional publications for analysis. Furthermore, due to the novelty of the topic, a survey of grey literature was conducted, which included consultancy reports and practitioner-oriented publications. Two additional competency frameworks were

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discovered and added to the final sample. Consequently, the final sample consisted of 19 publications, which were thoroughly examined and analyzed. Key competencies were extracted from each study and organized in an Excel file. Finally, a cross-analysis was performed to identify similarities and differences, eliminate redundant competencies, and group similar competencies in an inductive manner. 2.2 Expert Interviews The set of circular competencies derived from the literature underwent validation through expert interviews. Four semi-structured interviews were carried out, involving representatives of academia and manufacturing. The interviewees included an Italian assistant professor responsible for coordinating several EU projects related to circular economy in manufacturing, as well as three Sustainability/CSR Managers from Italian manufacturing companies. Each interview lasted approximately one hour, during which the list of competencies was presented and discussed with the experts. Their feedback on the relevance, clarity, and comprehensiveness of the circular manufacturing competency model was carefully gathered and noted (Table 1). Table 1. Interviewed experts Expert

Role

Industry

Expert 1

Assistant professor and Coordinator of EU projects

Academia (Automotive and Electronics)

Expert 2

Sustainability/CSR Manager

Food

Expert 3

Sustainability/CSR Manager

High-end furniture

Expert 4

Sustainability/CSR Manager

Fashion

3 Results 3.1 Descriptive Analysis In this stage, the formal characteristics of the collected papers were analyzed and assessed. Information about the number of articles published per year, the geographical coverage, the type of source and the list of journals are summarized in the followings. First, Fig. 1 represents the frequency distribution of published articles over the period. As it is shown in Fig. 1, despite the limited number of publications on the topic produced so far, there has been an increasing growth pattern over the last six years. Regarding the geographical origin, Fig. 2 shows that fourteen papers have a first author affiliated to a European institution, while only five papers are from extra-European countries. Therefore, it is possible to underline that greater attention is devoted to circular manufacturing– related competencies in Europe than in other geographical areas.

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Year 0

1

2

3

4

5

6

7

2016 2017 2018 2019 2020 2021 2022 Fig. 1. Articles by year

Country (First author affiliation) 0

1

2

3

4

Belgium Brazil Denmark France Hungary Italy Malaysia Morocco Netherlands Spain United Kingdom Fig. 2. Articles by country

Finally, as shown in Fig. 3 and Fig. 4, most articles are published in specialized journals addressing environmental management, sustainability and cleaner production, while manufacturing and training-oriented journals are less represented in the sample. Specifically, 58% of the papers are published by two journals: Journal of Cleaner Production (7) and Sustainability (Switzerland) (4). It means that the discussion is still restricted to a niche of academics and professionals interested in sustainability.

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Source Type 0

2

4

6

8

10

12

14

16

Conference Proceedings

Journal

Other

Fig. 3. Articles by source type

Journal 0

1

2

3

4

5

6

7

Business Strategy and the Environment Field Actions Science Reports Journal of Cleaner Productiom Journal of Technical Education and Training Journal of the Association of Environmental and Resource Economists New Technology, Work and Employment Sustainability

Fig. 4. Articles by journal

3.2 Circular Manufacturing Competency Model From the analysis of each publication and benchmarking of their content, a Circular Manufacturing Competency Model was elaborated. The Circular Manufacturing Competency Model consists of seventeen competencies grouped into three clusters: two basic competencies, eight technical-managerial competencies and seven transversal competencies. Basic competencies can be described as foundational skills related to the circular economy, which represent the base on which the other competencies can be built upon.

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Technical-managerial competencies refer to the main, generic processes and tasks characterizing circular manufacturing. Transversal competencies are not related to a specific domain but are key competencies with respect to lifelong learning, creativity, citizenship and taking initiative and responsibility [11] (Fig. 5).

Fig. 5. The Circular Manufacturing Competency Model

Table 2 reports the seventeen key circular manufacturing competencies identified from the literature review and validated by experts. 3.3 Interviews with Experts Interviews with four experts were performed in order to get a first evaluation of the relevance, completeness and clarity of the proposed model. During the interviews, respondents were asked to examine the circular manufacturing competency model, to evaluate it and to provide their suggestions for improvement. Regarding relevance, respondents

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Table 2. Key Competencies for Circular Manufacturing Cluster

Competency

Description

Ref.

BASIC

Fundamentals of sustainable development and circular economy

Clear understanding of the holistic concept of planetary boundaries, sustainable development, and principles of circular economy

[9, 11-15]

System thinking

Being able to approach [11, 12, 14, 16, 17] sustainability from all sides; to consider time, space and context in order to understand how elements interact within and between systems

Design and management of multiple product-service life cycles

Ability to design product-service systems that have multiple use cycles, to use secondary raw materials, to ensure efficiency in the use of all resources along the lifecycle

[7-9, 12, 17-21]

Design and management of circular operations and production systems

Ability to develop and operate innovative transformation technologies, circular processes and systems to optimize sustainability impacts

[7, 9, 11, 13, 16, 17, 20-23]

Design and management of circular value chains

Capabilities for planning, setting up and improving industrial symbiosis, integrated reverse logistics and circular value chains

[7, 11, 12, 15, 20]

Sustainable Marketing, Re-Marketing and Re-introduction in the market

Ability to plan demand [8, 9, 11, 15, 17] for recovered products on secondary markets, use sustainability-oriented communication strategies, pricing and incentives to engage consumers

Assessment of circularity and environmental, social and economic impact

Capability to map, analyze and evaluate the environmental, social and economic impacts of systems across their lifecycles and assessment of circularity levels of business models, processes, products, etc.,

TECHNICAL-MANAGERIAL

[8, 9, 11, 12, 15, 17]

(continued)

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Cluster

TRANSVERSAL

Competency

Description

Ref.

Circular Value Propositions and Business Models

Capability to develop Circular Business Propositions and Models, to create a vision and engage collaborations through value networks, to implement a roadmap toward the new circular configuration

[8, 9, 11, 12, 15-17]

Analysis of external context, trends and future scenarios

Capability to identify, analyse and anticipate the influence of external factors – such as environmental trends, CE policies and regulations, standards, etc. – on the company’s present and future scenarios

[11, 14-17, 21]

Development and use of digital solutions as an enabling factor for the circular economy

Capability to develop and implement innovative solutions based on digital technologies to support the transition to circular manufacturing

[7, 11, 12, 15, 17, 19]

Critical thinking

Being able to assess information and arguments, identify assumptions, challenge the status quo, and reflect on how personal, social and cultural backgrounds influence thinking

[11, 14, 17]

Problem Framing and Solving

Capability to formulate [11, 12, 14, 15, 17, 20] current or potential challenges as a sustainability problem in terms of difficulty, people involved, time and geographical scope, to identify suitable approaches to anticipating and preventing problems, and to mitigating, adapting and solving already existing problems

Creative thinking

Being able to see situations, objects and problems from another point of view, to give space to one’s imagination, make original connections and think outside the box

[8, 11, 17, 23]

(continued)

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Table 2. (continued) Cluster

Competency

Description

Ref.

Entrepreneurial mindset

The ability to act on ideas and opportunities and to transform them into values for others

[11, 17]

Multidisciplinary Teamwork/Collaboration

Ability of understanding [8, 9, 11, 12, 15, 20] partnerships, enabling new ways of collaboration and dialogue across disciplines

Lifelong learning

Being able to learn how to [11, 17] quickly learn things from different and fast evolving thematic areas

Adaptability

Being able to manage transitions and challenges in complex sustainability situations and make decisions related to the future in the face of uncertainty, ambiguity and risk

[11, 14, 17, 23]

confirmed that the model fully includes competencies relevant to circular economy in the manufacturing sector and can cover the main areas of interest of a manufacturing company. Furthermore, interviewees stated the model is complete and did not propose any additional skill. However, with reference to whether the identified competencies can be useful to support their human resources management activities, some experts perceived the model as too advanced for the current status of their companies and recommended the definition of an evolutionary path with different stages or levels.

4 Discussion and Conclusions The present study is one of the first that uncovers the essential competencies necessary for the transition and successful implementation of circular manufacturing. It identifies seventeen key competencies crucial for circular manufacturing, which hold the potential to aid companies in enhancing their production and consumption practices, ultimately fostering more sustainable systems. In doing so, the article provides an answer to the call raised by numerous researchers (e.g., [7]) and industrial stakeholders (e.g., [12]), and it makes a valuable contribution to our current knowledge by going beyond the insights provided in previous studies which were mostly limited to a single process (e.g., skills for design [8, 9]) or an industry (e.g., plastic [20], energy intensive industries [18]). Moreover, it expands current studies on “green” skills (e.g., [14]) by incorporating the perspective and specificities of circular economy in the context of manufacturing. The present article provides a valuable contribution to the field of manufacturing practice. The identified circular manufacturing competencies serve as an initial set of

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guidelines that managers can utilize to identify the necessary skills within their respective companies. These competencies offer a straightforward reference for assessing competency gaps, enabling the creation of targeted learning and development programs [24]. Additionally, the competencies can be leveraged to enhance existing job descriptions or create new job profiles. Furthermore, they can assist in streamlining the recruitment process by facilitating the identification and evaluation of candidate characteristics for hiring purposes. However, it is important to acknowledge the limitations of this study and interpret the findings accordingly. Researchers interested in the realm of circular manufacturing are encouraged to address these limitations in future studies. First, despite following the recommendations of the PRIMA statement, there may still be some inherent bias affecting the obtained results from the literature review. Second, enhancing the robustness and generalizability of the findings can be achieved by increasing the number of interviews and diversifying the participants. Third, enriching the list of competencies extracted from the literature with empirical data from industrial case studies would be beneficial. Finally, adopting a design science approach, researchers could develop new (digital) artifacts aimed at describing, organizing, and assessing relevant competencies [25]. Defining proficiency levels for each competency and establishing an ontology of circular manufacturing competencies are particularly promising avenues for future research. Acknowledgements. This study has been partially funded by the Horizon Europe project DaCapo - GA number: 101091780.

References 1. World Manufacturing Foundation. Digitally Enabled Circular Manufacturing. World Manufacturing Foundation (2021) 2. Acerbi, F., Taisch, M.: A literature review on circular economy adoption in the manufacturing sector. J. Cleaner Prod. 273, 123086 (2020) 3. Fantini, P., Opresnik, D., Pinzone, M., Taisch, M.: The interplay between product-services and social sustainability: exploring the value along the lifecycle. In: IFIP International Conference on Advances in Production Management Systems, Springer, Cham, pp. 567–574 (2015) 4. Oliveira, M., Arica, E., Pinzone, M., Fantini, P., Taisch, M.: Human-centered manufacturing challenges affecting European industry 4.0 enabling technologies. In: International Conference on Human-Computer Interaction, Springer, Cham, pp. 507–517 (2019) 5. Bertassini, A.C., Ometto, A.R., Severengiz, S., Gerolamo, M.C.: Circular economy and sustainability: the role of organizational behaviour in the transition journey. Business Strategy and the Environ. 30(7), 3160–3193 (2021) 6. Marrucci, L., Daddi, T., Iraldo, F.: The contribution of green human resource management to the circular economy and performance of environmental certified organisations. J. Cleaner Prod. 319, 128859 (2021) 7. Jaeger, B., Upadhyay, A.: Understanding barriers to circular economy: cases from the manufacturing industry. J. Enterprise Information Manage. 33(4), 729–745 (2020)

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8. Jabbour, C.J.C., et al.: Who is in charge? A review and a research agenda on the ‘human side’of the circular economy. J. Cleaner Prod. 222, 793–801 (2019) 9. Sumter, D., de Koning, J., Bakker, C., Balkenende, R.: Circular economy competencies for design. Sustainability 12(4), 1561 (2020) 10. Sumter, D., de Koning, J., Bakker, C., Balkenende, R.: Key competencies for design in a circular economy: exploring gaps in design knowledge and skills for a circular economy. Sustainability 13(2), 776 (2021) 11. Schlüter, L., Mortensen, L., Drustrup, R., Gjerding, A.N., Kørnøv, L., Lyhne, I.: Uncovering the role of the industrial symbiosis facilitator in literature and practice in Nordic countries: An action-skill framework. J. Cleaner Prod. 379, 134652 (2022) 12. Janssens, L., Kuppens, T., Van Schoubroeck, S.: Competences of the professional of the future in the circular economy: evidence from the case of Limburg, Belgium. J. Cleaner Prod. 281, 125365 (2021) 13. Association of Nordic Engineers: Towards a Circular Economy. Skills and Competences for STEM Professional (2021) 14. Cabral, C., Dhar, R.L.: Green competencies: construct development and measurement validation. J. Cleaner Prod. 235, 887–900 (2019) 15. Bianchi, G., Pisiotis, U., Cabrera Giraldez, M.: GreenComp The European sustainability competence framework. Joint Research Centre , Seville (2022) 16. Fodor, S., Szabó, I., Ternai, K.: Competence-oriented, data-driven approach for sustainable development in university-level education. Sustainability 13(17), 9977 (2021) 17. Vona, F., Marin, G., Consoli, D., Popp, D.: Environmental regulation and green skills: an empirical exploration. J. Association of Environ. Resource Economists 5(4), 713–753 (2018) 18. Branca, T.A., et al.: Skills demand in energy intensive industries targeting industrial symbiosis and energy efficiency. Sustainability 14(23), 15615 (2022) 19. De los Rios, I.C., Charnley, F.J.: Skills and capabilities for a sustainable and circular economy: the changing role of design. J. Cleaner Prod. 160, 109–122 (2017) 20. Phung, C.G.: Implications of the circular economy and digital transition on skills and green jobs in the plastics industry. Field Actions Science Reports, pp. 100–107 (2019) 21. Zubir, M.Z.M., Lai, C.S., Zaime, A.F., Lee, M.F., Ibrahim, B., Ismail, A.: Dimension of green skills: perspectives from the industry experts. J. Technical Education Training 13(1), 159–166 (2021) 22. Bozkurt, Ö., Stowell, A.: Skills in the green economy: recycling promises in the UK e-waste management sector. New Technol. Work and Employment 31(2), 146–160 (2016) 23. Guelle, K., Caroly, S., Landry, A.: The remanufacturing activity: skills to develop and productive organizations to rethink. In: Lecture Notes in Networks and Systems (2022) 24. Burger, M., Stavropoulos, S., Ramkumar, S., Dufourmont, J., van Oort, F.: The heterogeneous skill-base of circular economy employment. Research Policy 48(1), 248261 (2019) 25. Pinzone, M., Fantini, P., Fiasché, M., Taisch, M.: A multi-horizon, multi-objective training planner: building the skills for manufacturing. In: Advances in Neural Networks (2016)

Implications of Improving Resource Efficiency When Utilizing Residual Raw Material on Trawlers Producing Head and Gutted Fish Per Solibakke(B) Møreforsking AS, Ålesund, Norway [email protected]

Abstract. Utilizing available raw materials to improve resource efficiency remains an important topic across industries. While land-based industries have been focusing on a circular economy, fisheries have not seen a similar transition. In this study a mass balance and cost-benefit evaluation of the utilization of residual raw materials on factory trawlers was performed. The study therefore firstly analyses in depth the mass and value of Atlantic cod (Gadus morhua, AC), secondly shows the complexity of factory trawlers, and thirdly identified practical implications of utilizing residual raw materials; its main focus was the storage, the efficiency, and the variability for factory trawlers. The findings in this study suggests that utilizing the entire AC compared to only head and gutted AC (HG AC) could increase the value generated by 3–10%. However, if the utilization were to proportionally reduce the production of HG AC, a decrease in value creation would occur in all scenarios. Handling and storing AC heads, thus reducing HG AC proportionally, would lead to a decrease of approximately 17%. The utilization would also involve unquantified investment costs, additional factory space and a larger workload for fishermen. Increased numbers of voyages to accommodate additional storage and processing requirements may be an option, but the value generation would need to surpass the additional costs of operations. Developing and deploying sensor technology for enhanced automation or incorporating modular sub-factories to accommodate variability for seasonal operations would be potential solutions in the future. Keywords: Factory trawlers · resource efficiency · modular factory · residual raw material

1 Introduction 1.1 The Atlantic Cod (AC) The AC, its fisheries and their export revenues have been important in Norway for centuries and remain important in the present day [1]. The AC is so important that it has received its own place on Norwegian currency (the 200 Norwegian Krone [NOK] bill) [2]. According to statistics from the Norwegian Seafood Council (NSC) [3], the AC © IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 200–214, 2023. https://doi.org/10.1007/978-3-031-43688-8_15

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along with all its product categories (e.g., whole fish, headed and gutted [HG], fillets, etc.) generated export revenues of 9.64 billion in NOK in 2020, 9.80 billion in 2021 and 12.22 billion in 2020. These values correspond to product weights of 190,196 tons in 2022, 198,308 in 2021 and 172,089 in 2020, valuing one kilogram (kg) of AC at 51.43 NOK on average over the last 3 years. To provide an indication of the number of whole weight (WW) fish caught each year in Norway, the Norwegian Directorate of Fisheries (NDF) has established conversion coefficients to convert product weight to WW. The AC can generate 22 products with a unique conversion coefficient to WW, and some additional sub-categories exist [4]. Furthermore, an additional 15 product categories can be derived from the AC; however, these do not possess conversion coefficients because they are defined as secondary components (residual raw material) of the AC. Based on the substantial number of product categories, the valuation of AC based on one average product weight is an approximation with serious drawbacks. All these product categories have different production procedures and a unique identifier (XXXX-XXX-XX). The first four digits define the fish species, the middle three define the state and the last two define the conservation method. For example, 1022-21099, describes a product derived from an AC which has been gutted and the viscera has been removed. 1022-513-99 describes a product of an AC from which only the fillets remain. The conversion coefficient logically increases as the fish becomes increasingly processed. Following NDF guidance, each kg of HG AC (1022-211-99), can be converted to 1.5 kg of WW AC, while each kg of cod fillets (1022-513-99) is converted to 3.25 kg of WW AC. Furthermore, the remaining components becomes more valuable for each processing step [3]. 1.2 The Norwegian Fishery The Norwegian fishery, together with aquaculture is responsible for the largest share of food export revenue for Norway [5]. The fishery is often separated into three classifications: 1) gear used to catch fish, 2) fish species targeted to be caught and 3) the method of processing and preservation. For all classifications, the fishery is regulated using quotas specifying the number of fish each fishing vessel can catch. This process is further monitored by mandatory catch reports from the vessels during voyages [6, 7]. According to catch reports for 2021 from NDF, the four main groups of gear caught a total of 2,591,000 tons of fish [8]. Table 1 shows the total catch in 2021 separated across the four different gear classifications. Each class of gear is further separated into subgroups and requires different systems and equipment installed on-board to function [9]. The trawl group, describing factory trawlers, represents the largest share of the total catch in the fishery. Separation by species and method of preservation is not included in Table 1. However, for the average Norwegian factory trawler, HG fish is produced and frozen at sea [3]. Some factory trawlers produce fillets [10, 11], but they represent a smaller portion of the export revenues generated from Norwegian trawler fleet. Because the primary export of Norwegian seafood is non-filleted fish [12], the Norwegian trawler fleet is responsible for nearly 50% of the entire fishery in Norway

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Table 1. Total catch in tons of the Norwegian fishery in 2021, separated across the main groups of gear [8]. Main groups of gear

Total (tons)

Percentage of the total catch

Trawl

1,235,965

48%

Seine fishing

727,428

28%

Yarn and line fishing

467,418

18%

Other

160,854

6%

Total

2,591,664

100%

(Table 1); the AC is most valuable species landed. The present study focused on HG AC processed on factory trawlers. 1.3 The Factory Trawler On-board factory processing of whole fish to HG fish occurs in a precisely calibrated facility. Although it follows the same principles of land-based facilities, it is accurate to describe the conditions and requirements for the production as two different worlds. In comparing them, the following four concepts are important to introduce. Storage. A land-based processing facility has an array of options if an increase in storage capacity is necessary at a short notice. Options such as renting third-party storage facilities and expanding current capacity are often available and can be implemented relatively quickly. By contrast, on a factory trawler, increasing storage capacity within a reasonable timeframe and budget is extremely difficult. Such an operation would include a complete overhaul of the entire ship [13]. Factors such as the ship’s buoyancy and stability as well as engine efficiency are some of the many specifications that must be accommodated when overhauling a ship. Efficiency. In general, a land-based facility has an intermediate storage capacity for pre-processed products and a constant production flow. On a trawler, one trawl catch may be approximately between 5–15 tons of fish, occurring 2–4 times per day. Once brought to the ship, the fish must be beheaded, gutted, rinsed, cleaned, sorted, frozen and stored within a few hours. Figure 1 provides an overview of the primary process segments used in a trawler when producing frozen HG fish. The factory within the trawler must be extremely efficient in handling large quantities of fish, while avoiding quality reduction [1]. Variability. In each trawl catch, a variety of fish species (e.g., AC, tusk, haddock, pollock, halibut) of all sizes is expected [14]. As a result, the factory must accommodate this variability while maintaining efficiency and avoiding quality reduction. The fishermen. The manual labour required by fishermen during a voyage includes many unique tasks [15]. While the Norwegian trawler fleet is highly advanced as compared to most of the world in automation within the trawler factory, manual labour provided by fishermen remains essential. In addition, the increased in automation requires

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the crew to have increased technical insight. Most of the segments visualized in Fig. 1 are semi-automatic, but still require manual labour to function. The handling of secondary components would introduce further process segments and tasks. Finally, the North Sea, where the primary processing of HG fish occurs, cannot be described as a stationary work environment.

Fig. 1. Visualization of the primary process-segments in a trawler producing frozen HG AC.

1.4 Resource Efficiency Resource efficiency and circular economy are two concepts which are generally associated with improved economic, social and environmental impacts. Waste is either reduced or transformed into valuable flows in the production line, promoting a more circular value chain. Improving resource efficiency on factory trawlers by utilizing secondary components has limitations based on the four concepts mentioned in Sect. 1.3. The whitefish species group, which includes AC has exhibited the lowest rate of utilization of secondary components compared to other groups of fish species [16]. This deficit has promoted an increase in research and development projects regarding the topic in recent years [17–20]. As a result, possibilities for utilization of secondary components have increased and include utilization of AC heads [21, 22], of AC backbones [23], and by-catch [24, 25] as well as stabilization possibilities [26]. While the possibility for utilization has been present, these techniques have not been implemented to a great extent on factory trawlers. Moreover, the quantified effects of utilization of secondary components on the economic, social and environmental aspects have not been studied extensively [27]. This study was undertaken to improve knowledge regarding the challenges and opportunities arising from the utilization of secondary components of AC. The concepts of storage, efficiency and variability were evaluated from a bottom-up system perspective [28].

2 Materials and Methods This study involved a mass balance analysis and a cost-benefit analysis (CBA) of HG AC. The overarching goal of this study was to provide insight into the complexity involved in implementing increased utilization of secondary components on factory trawlers. The

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study therefore had three objectives. First, we mapped the primary flow and the residual raw material flows of HG AC on factory trawlers. Second, we evaluated the direct benefits of exploiting various parts of the AC. Third, we aimed to evaluate the costs and practical limitations of exploiting some or all components from the AC. 2.1 Inspection of Norwegian Factory Trawlers This study was based on inspections of multiple factory trawlers, varying in size and year of construction. All trawlers originated from Møre og Romsdal and produced 25-or 50- kg frozen blocks of HG fish as their primary products. The process lines (see Fig. 1) were inspected and are discussed in detail below. When applicable, processing of shrimp and secondary components was also inspected. After the inspections, correspondence was preformed to gain further insight and to discuss further possibilities or issues. 2.2 A Bottom-Up System Perspective The evaluated system was the factory trawler, specifically from processing to storage of HG AC. When evaluating the synergies and trade-offs for utilizing secondary components of AC, system-thinking methodology was employed [28, 29]. This methodology attempts to capture dependencies by iterating between gaining insight and using insight on the system. This study deployed a bottom-up approach in which one concept was initially introduced and evaluated. Additional concepts were subsequently included, and dependencies (synergies/trade-offs) evaluated. 2.3 Functional Unit A functional unit is a unit which has some arbitrary value in a system and is often denoted as the reference unit. In the assessments, the functional unit was used to compare existing reports in the literature and make the assessment comparable for future research on the topic [30]. One metric ton of WW AC was the functional unit for this study. For processes manufacturing a product X from the input Y with an unexploited waste stream W, Y would be defined as functional input, X as the functional output, and W would not be considered a functional flow. 2.4 Mass Balance Analysis and Multifunction Processes A mass balance is defined by the functional unit and the system boundaries, describing the physical inputs and outputs of the system [31]. Figure 2 illustrates a system with the functional unit F, inputs (I1 , I2 ,…, In ) and outputs without a value (W1 , W2 ,…, Wn ). For this study the primary product (HG AC from WW AC) and the secondary components (head, gonad, liver and viscera) were evaluated. Eq. 1 calculates the mass balance for all physical inputs and outputs of the system: mI =

j i=1

mi =

z y=1

my = mO

(1)

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where mI is the total mass of the inputs, j is the number of inputs, z is the number of outputs, mi is the mass of input i, my is the mass of output y and mO is the total mass of the outputs. A multifunctional process describes a system in which two or more of the outputs are functional flows. If W (see Fig. 2) is utilized, both X and W would be functional outputs in the system. When exploiting additional components besides HG AC, the on-board processing for the factory trawlers becomes multifunctional.

Fig. 2. Visualization of a system with multiple processes (Pn ), inputs (In ), and waste flows (In ). Fn is the intermediate functional unit and F is the functional unit of the system.

2.5 Resource Efficiency When evaluating the resource efficiency of a production system the mass-based utilization of the inputs compared to the functional outputs can be expressed [32]. Using the mass balance and the functional outputs defined for the system, the resource efficiency can be calculated as follows: mfo × 100 (2) µ= mI where µ is the resource efficiency in percent and mfo is the mass of the functional outputs. Waste streams are excluded in the calculation. The mass-based utilization rate only accounts for inputs which are directly involved in production. Indirect inputs such as energy requirements or supporting infrastructure are not considered. 2.6 Market Values and Potential Value Creation Established market values [3] and the mass balance were used to calculate potential value creation from AC. Equation 3 describes how the potential value of a single component was calculated: PV j = MV j mj

(3)

where PV j is the potential value of component j, MV j is the market value of component j and mj is the mass of the component j.

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In evaluating a multifunctional process, the total value creation is the summation of all valuable flows: n PV T = PV j (4) j=1

where PV T is the total potential value of the system. The NSC does not have established market values for the secondary components of AC. As a result, an additional literature review to evaluate potential values was conducted [33–35]. For components with values defined only from minimum prices set at regional harbours, an increase was calculated in correlation with HG AC as follows: PV c = MinV c ∗

MinV HGAC MV HGAC

(5)

where PV c is the potential value of component c. MinV c is the minimum value of component c, MinV HGAC is the minimum value of fresh HG AC and MV HGAC is the market value of HG AC. 2.7 Evaluation of Costs Quantifying all associated costs for new developments is complex. These costs are often categorized as operational, labour, development or infrastructure costs [36]. Because factory trawlers have limited storage capacity, reduction in value creation when utilizing components of AC was calculated by estimating the reduction in storage capacity for HG AC. Equation 6 describes the effects on storage capacity: n ST = SE HG S HG + SE i Si (6) i=1

where ST is the total weight-based storage capacity on the factory trawler and constitutes the limiting factor. SE HG and SE i describe the mass-based storage efficiency of HG AC and secondary component i, respectively. ST and Si describe the weight-based amount of stored HG AC and secondary component i, respectively. Thus, the potential value creation for one voyage can then be calculated as follows: n PV FT = PV HG SE HG S HG + PV i SE i Si (7) i=1

where PV FT is the potential value generated on the factory trawler from one voyage. To compare the potential value creation when utilizing secondary components of AC different storage scenarios were evaluated. Hereunder, the effects of only exploiting specific components and the combined effects of exploiting multiple components was conducted.

3 Results 3.1 Mass Balance (Anatomy) of AC In accordance with the NDF’s conversion coefficients, one metric ton of WW AC would produce 670 kg HG AC and 160 kg AC heads; the remaining weight would be distributed across the liver, gonad and guts. This approximation serves as an effective estimate in

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catch reports. However, the weight distribution of components in AC varies greatly. Table 2 provides a more accurate description of the weight distribution of HG, head, liver, gonad and gut components in relation to WW AC [4, 21, 22, 25, 33, 37–39]. Table 2. Mass distribution of an Atlantic cod (Gadus morhua), separated into the products (or components) of HG, head, liver, gonad and guts. Values in bold represent weights based on the conversion coefficients provided by the NDF [4, 37]. Products

Weight per WW

Unit

References

WW AC

1000

kg/ton

[4, 37]

HG AC

670 (530 – 740)

kg/ton

[4, 37]

AC head

160 (140 - 220)

kg/ton

[4, 21, 22, 37, 38]

AC liver

50 (20 – 70)

kg/ton

[25, 38, 39]

AC gonad

50 (0 – 70)

kg/ton

[38]

AC guts

70 (50 – 80)

kg/ton

[38]

The ranges provided in the table indicate the variability in evaluating the mass balance of AC components. As an example, the gonad is a seasonally based component and is not present in AC in certain parts of the year. This difference results in a large reported weight distribution of 0 to 70 kg per ton WW AC. The end points of the ranges indicate the minimum and maximum occurrences per component in reference to WW AC. Following only the minimum or the maximum occurrences would make the mass balance inconsistent, as the combined weight must correspond to the WW. For example, if the AC head is estimated to constitute 220 kg per ton WW, 60 kg per ton would need to be taken from one or more of the remaining AC components to maintain a consistent mass balance. 3.2 Market Values of Products Derived from AC The metrics from the NSC regarding the export of Norwegian seafood provide an accurate representation of market values for established fish products [3]. The average market value over the last three years (2020–2022) for HG AC and fillets of AC is presented in Table 3. The market value of AC heads has not been defined by the NSC and was instead derived from a report dating to 2000 [34]. This valuation also is only valid for AC larger than 3.75 kg. Minimum prices for AC gonad and liver were taken from SUROFI [35], while no concrete value for AC guts was found. 3.3 Potential Value Creation from WW AC The potential value of one kg of WW AC when producing HG AC is reported in Table 4. Complete exploitation highlights the potential value of selling HG AC with all secondary components. HG AC is a substantial part of the potential value, ranging between 90% and 97% of the entire value of the WW AC. The remaining 3–10% is derived from combining

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P. Solibakke Table 3. Overview of potential values of components derived from AC [3, 34, 35].

Product

Market value

Minimum price in 2023

Unit

Frozen HG AC

40.19 [3]

N/A

NOK/kg

Fresh/cooled HG AC

40.20 [3]

34.23 [35]

NOK/kg

Frozen fillet of AC

82.88 [3]

N/A

NOK/kg

AC head

1.00 [35]

NOK/kg

AC liver

5.00*1 [34] 5.87*2

5.00 [35]

NOK/kg

AC gonad

9.39*2

8.00 [35]

NOK/kg

AC guts

N/A

N/A

NOK/kg

* 1 The weight of the Atlantic cod would need to exceed 3.75 kg for the head to be exported and

sold at the given price, which corresponds to approximately 36% of the catch AC [34] * 2 Symbolizes the calculated value increase from the minimum price

all secondary components. Norwegian factory trawlers can possess storage capacities ranging from 300 to 700 tons. These storage approximations are also defined per kg HG AC, corresponding to the volumetric space requirement of frozen HG AC. Under the assumption that the trawlers only catch AC. Exploiting the secondary components could generate an additional value of 0.4–1.1 million NOK per trip. Table 4. Potential value distribution of a WW AC when producing frozen HG AC, using established market values from Table 3. The bold values and percentages represent the average weight from the mass balance in Table 2. The minimum value occurrence of AC components was derived from the minimum weight occurrence from Table 2 and the maximum value occurrence was derived from the maximum weight occurrence from Table 2. Product

Potential value per WW

Unit

(%)

Complete exploitation

28.49 (23.77 – 30.65)

NOK/kg

100

HG AC

26.93 (21.30 – 29.74)

NOK/kg

95 (90 – 97)

Secondary AC comp

1.56 (0.91 – 2.47)

NOK/kg

5 (3 – 10)

AC head

0.80 (0.70 – 1.05)

NOK/kg

3 (2 – 4)

AC liver

0.29 (0.12 – 0.76)

NOK/kg

1 (0 – 3)

AC gonad

0.47 (0.09 – 0.66)

NOK/kg

2 (0 – 3)

AC guts

N/A

NOK/kg

0

However, this approximation does not include the additional costs and practical implications arising from the handling, sorting and storing of the additional components.

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3.4 Utilization of Secondary AC Components As mentioned previously, the expected value increase of 3–10% from Table 4 is a simplification. This section addresses the concepts described in Sect. 1.3 to indicate the related implications. Storage. Additional storage must be allocated for each new component utilized. AC heads account for approximately 3% of the value and 16% of the weight of AC. Storage AC heads would substantially reduce the storage capacity for HG AC. Table 5 presents scenarios for potential value creation for two trawlers, with storage capacities of 300and 700 metric tons, respectively. Only HG AC is stored in one scenario, whereas both HG AC and AC heads are stored in the other. Moreover, it has been assumed that one kg of AC heads occupies the same volumetric space as one kg of HG AC. From these calculations, combining the potential value increase of 3% from exploiting the AC heads and reducing the stored HG AC by 16% would reduce the overall value returned with almost 17% compared to only storing HG AC. Using the same approach to evaluate the utilization of the AC liver and AC gonad, the reduction in value creation is not of a similar magnitude. Although the AC liver is estimated to represent only 1% of the value, it also represents only 5% of the weight. Similarly, the AC gonad contributes to 2% of the value and only 5% of the weight. These characteristics results in a decrease in value of approximately 6% for AC livers and 5% for AC gonads per trip following the same volumetric assumption that was considered for AC heads. Table 5. Potential value creation when utilizing 100% of the on-board storage capacity of a factory trawler when storing only HG AC or both HG AC and AC heads. Trawler

HG AC

AC heads

Value estimation

Difference

A (300 ton)

300 tons

0 tons

12.06 mNOK

0 mNOK

242 tons

58 tons

10.01 mNOK

- 2.05 mNOK

700 tons

0 tons

28.13 mNOK

0 mNOK

565 tons

135 tons

23.37 mNOK

- 4.76 mNOK

B (700 ton)

The Factory and Efficiency. Implementing additional handling procedures for secondary components within the factory is challenging. Figure 3 is an extension of Fig. 1, visualizing the primary process segments for the utilization of secondary components when producing HG AC. These segments are typically manual or semi-automatic, and each segment would occupy additional space in the factory. The available factory area is already highly committed to processing the primary flow. Additional space-demanding segments would most likely impact the efficiency of handling the primary flow. The incorporation of these segments would also involve investment costs and re-ordering of the factory process lines. A general Norwegian trawler producing HG fish has three direct gutting machines1 , that can process fish between 1 and 12 kg. Each machine has 1 BAADER 444.

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a capacity of 15–35 fish per minute, depending on size. Following the diagram in Fig. 3, utilizing all secondary components of AC in addition to HG AC an additional 45–105 heads and 45–105 sets of viscera must be handled simultaneously. Variability. In considering the storage, factory, and efficiency above, only catching AC has been assumed so far. When accounting for the variability occurring with each trawl catch, the utilization of secondary components becomes significantly more complex. A trawl catch consists of a great variety of fish species and sizes. Additional semi-automatic gutting machines to process large fish (12 or more kg) and machines to accommodate different fish species are already implemented in the process lines in a factory. Utilizing the secondary components from all fish species would involve significantly more sorting and allocation of storage for each unique fish species and its various components.

Fig. 3. Visualization of the process segments associated with the handling secondary components when producing head and gutted fish (HG fish). This visualization is an extension of Fig. 1. Roe (female) and milt (male) are seasonally based products and can only be extracted at certain times of the year.

4 Discussion Further on-board processing of both the primary and secondary components of fish is possible and is currently conducted on some factory trawlers [10, 11]. The production of low-value fish oil and fish meal from silage or fillet production are examples of this process. For this study, these additional processing possibilities were not quantified in a similar manner to that of the separated components of HG AC. Nevertheless, following a similar approach to evaluate the costs and benefits of these additional processing possibilities would be important to describe the entire system. The storage capacity for the primary product would be affected by utilizing secondary components of the caught fish; however, the quota would not change. Conducting additional trips to accommodate the utilization of secondary components while maintaining

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the original production quota is a potential option to address this condition; such a strategy would introduce an increase in operational costs and would need to be quantified. Furthermore, the assumption of exploiting 100% of storage capacity on each trip is inaccurate, as catch rates in the fishery vary greatly. During periods of low catch rates, maximum storage capacity for HG fish would not be reached; in these cases utilizing the secondary components of AC would be more lucrative. To avoid further sorting processes, reductions in storage capacity and increases in manual labour when utilizing secondary components, the simplest solution would be grind all such components, independently of species, and release the mass back to the sea or conduct a fish silage process. Finally, the fisherman’s tasks should be discussed. Although Norwegian factory trawlers have seen modernization in terms of automation in the recent decades, the workload of a fisherman is still extensive. Allocation of storage, addition of technical equipment and operation of further processing lines and components would increase this workload. In addition, the crew receives income in correlation with the value created. Higher utilization of secondary components at the expense of the primary flow would reduce income for the involved crew. Thus, utilization would increase workload and reduce income. The sorting of HG fish is generally conducted manually based on species and automated based on weight. According to this process, a fisherman places HG fish in containers predefined according to species. To utilize fish heads, a similar sorting procedure would be required. To utilize the AC liver or AC gonad, the same initial sorting of the fish species would need to be performed. Additionally, the separation of liver or gonad would then need to be conducted. Developing and deploying sensors able to determine and sort fish based on species, despite the large volumes, could produce an improvement in a fisherman’s workload. The handling of residual material such as heads would also be significantly improved, as a major part of handling is associated with sorting. Developing modular sub-factories for factory trawlers could constitute a new approach to improve both the efficiency of the primary production flow and the capacity for accommodating the variety of secondary components. The sub-factories could be changed out upon returning to land, depending on which species are to be targeted on the next trip. To achieve such a development, collaboration between boat owners, factory producers and research institutions would be essential. 4.1 The Effects of Resource Efficiency In the present evaluation, the economic gain from the exploitation of all or one of the secondary components of AC (head, liver or gonad) appears questionable. The economic benefits, compared to the potential losses are highly dynamic as market values must be high and the catch rates and operations costs must be low to constitute an economic gain. In scenarios involving high catch rates, the utilization of secondary components is not economically feasible, as the focus would be on the primary flow during processing, and the entire storage capacity would need to be allocated to HG fish. However, in times of low catch rates the utilization of secondary components would potentially generate a positive net value if the additional operation costs were not excessively high.

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The social impact of the additional utilization is difficult to quantify. However, considering the current value potential and the additional manual labour required, the social impact is expected to be negative. The environmental impact has not been evaluated to a significant extent. For production involving biological material, residual raw material represents a different category of waste from that of waste from plastic or inorganic chemical production. If the head and viscera are ground and released back to the ocean, it would be returned to the same ecosystem from which it had been taken. If the components are utilized, then larger operational costs per WW AC would be a result, increasing emissions. However, the improved resource efficiency could reduce the demand for other protein sources with worse impacts on the environment.

5 Conclusion The potential value creation and the practical implications of exploiting secondary components of AC was evaluated in this study. Utilization of secondary components of AC would exhibit a positive value creation of 3–10% as compared to exploiting only HG AC. If the secondary components reduce or impact the primary flow per trip in terms of amount or quality, then their utilization would reduce profitability. In times of low catch rates, the utilization of secondary components would not have a significant impact on the primary flow: however, the potential value from the secondary components would need to exceed operation costs. The social impact from utilizing secondary components would be expected to be negative as the tasks involved in processing these components are still highly dependent on manual labour. Additional trips, deployment of sensors for sorting and development of modular sub-factories can be seen as possibilities to reduce the complications of utilizing secondary components in the future. While the complexity has been highlighted, the quantified synergies and tradeoffs, particularly those relating to social and environmental aspects must be evaluated in greater detail. These synergies and trade-offs are aimed to be further evaluated in activities planned for the Fisk 4.0 research project. Acknowledgements. Data for this work was collected in the research project “Fisk 4.0 – Industrialization of the marine value chain” [17]. The author express thanks to a Norwegian trawler company operating multiple freezing trawlers for tours of their vessels.

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Selective Complexity Determination at Cost Based Alternatives to Re-manufacture Sotirios Panagou(B) , Giuseppe La Cava, Fabio Fruggiero , and Francesco Mancusi School of Engineering, University of Basilicata, Via Ateneo Lucano 10, 85100 Potenza, Italy {sotirios.panagou,fabio.fruggiero,francesco.mancusi}@unibas.it

Abstract. The rise in demand and price for raw materials is pushing manufacturing industries to look for new ways to secure parts and products for their production while keeping the expenses low. Remanufacturing as the manifestation of circular paths can contribute towards sustainability (in terms of extension of use and reduction of waste). This work proposes a decision-support system to select, through adaptability, product and parts for remanufacture. It makes use of complexity analysis and production capacity (i.e., demand) to quantify costs. The complexity level was related with (i) characteristics of components and (ii) disassembly transitions. The decision system is conceived to assess, through inspection, the potential of a product/part for re-manufacturability based on failure pattern and recoverable rate. The re-manufacturability is evaluated, on cost alternatives, as per regeneration using additive or subtractive manufacturing, reuse of components/parts, recovery of materials, disposal. The additive alternative was analyzed over complexity (suggesting alternatives while estimating costs vs. complexity) which increases interest in the applicability to recover complex forms of limited (unplanned) demand. To demonstrate the applicability, authors quantified costs involving the remanufacturing of gear pumps parametrizing parts on a complexity amount after a relative product recyclability selection. Results evaluate (dis)assembly and regeneration across main feature assessment. Keywords: Remanufacturing · Design Complexity · Process Complexity · Additive Manufacturing · Subtractive Manufacturing

1 The Circularity of Remanufacturing Increased demands for raw materials, fluctuations and uncertainties in market and customer demands are nowadays forcing a service approach to manufacturing. End of Life (EoL) and End of Use (EoU) products as waste are slowly replaced with the approach of evaluating returned EoL products for recycling or reuse and remanufacturing [1]. Although remanufacturing is supported, in terms of performance and quality, by advances in technology and robotics (Industry 4.0), still it represents a small portion in heavy industries. The introduction of Industry 5.0 aiming at human centric solutions and sustainable development, is forcing the implementation of the circular economy © IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 215–228, 2023. https://doi.org/10.1007/978-3-031-43688-8_16

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strategies, adopting closed loop supply chains and reverse logistics. Remanufacturing combines recovery of EoL and EoU products and restoring/reuse of products within safety standards and performance equal to that of new products. Moreover, by considering the possible reuse/remanufacturing of products as a design requirement, it is possible to adopt usability of materials and extension of useful life for product and process sustainability. One major uncertainty of remanufacturing is the suitability of returned products for reuse. Conditions of components (at their return after different - unpredictable -stressor factors), varying demand, connection type and variety (mutually dependent from conditions of block and parts), accessibility and mating of blocks in bill are uncertainty drivers for alternatives selection and cost convenience [2]. Recyclability, durability, manufacturability, supply role, product weight and material, modularity and interchange-ability, disassembly patterns are main elements to be evaluated for a sustainability analysis [3]. Return products, because of their state/condition/appearance/demand, manifest limited adaptability to standard remanufacturing conceived as consumable [4]. This has to be considered as a consequence of the design based on the logic of optimizing costs and functionality while neglecting and underestimating the reusability/recycle of the used components. Thus, as sustainability is a desired goal and is being advanced, researchers are investigating the implementation of product design principles for remanufactureability. Adding characteristics adhering to disassembly transitions and part transferability to a main sustainable development in product design could reinforce remanufacturing and its impact in the returning phase [5]. Shahbazi and Jonbrink (2020) elaborated features for disassembly easiness without product destruction, inspection propensity and better accessibility for component replacement [6]. Sustainability of remanufacturing is influenced by: durability, cleanability, recovering capacity, cost (related to) and complexity, product modularity [7]. Although remanufacturing could be a solution for sustainable development and a central part of circular economy and reverse logistics, its dependability on market acceptability and customer demand are still open issues. It, still, requires a value interpretation and convenience estimation, for customers. In this study, a decision support tool - based on two round analysis for selective complexity determination to value estimation - with application on remanufacturing is presented. The proposed tool aims at assessing the suitability for remanufacturing, by estimating remanufacture-ability and quantifying costs, while considering application of subtractive or additive technology. Complexity is used as a key parameter for product recovery and re-manufacturability. In the next section, a literature background is remarked with the main intent to identify complexity measurement methods for remanufacturing purposes. In the third section, the methodology and a case study is resulted and showcasing the decision strategies. Finally, conclusions are presented along with future steps.

2 Managing Complexity for (Re)Manufacturing The demand and arriving time of EoL products, useful life and EoU conditions, component state and structure are unpredictable and constitute an uncertainty driver for an effective and efficient circular model. It is mainly associated to no predictability of

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demand and timing, generally treated as per replacement of maintenance parts. Circularity requires assessment of conditions, selective disassembly and identification of production strategies for component replacement. Conventional approach generally applies Subtractive Manufacturing (SM), involving the use of subtracting material in order to produce an end product, technologies adopted for mass product and cost capitalization make use of a production capacity (made from equipment effectiveness) as a performance indicator over different time horizons. Cost of production in SM is inversely proportional to the production quantity. Through specificity of parts, Additive Manufacturing (AM), referring to adding material to create an object and is usually referred to 3D printing, product-associated costs are counted with volume of parts. They change according to product dimension, materials and energy requirements. Direct and indirect costs are, (independently from technology) in AM, time dependent allocated for each layer and build over a small (chamber based) lot allocation [8]. Unit production cost suffers from unconsolidated technologies (mainly, for metal products), high machine and material costs, reworks, limitation of volume in chamber and its lack in saturation of space, slow building rate, unclear quality. As demand (i.e., capacity) increases, production costs for AM rise significantly up with uncompetitive market solutions. Notwithstanding, using complexity of product and regeneration of shape (with inhomogeneous morphology, weight reduction) in process perspective can provide new alternatives, especially whenever additive methods are applied to remanufacturing strategies [9]. Product complexity and operational related tasks depend mainly on: the total numbers of design characteristics [10], number of parts and blocks and their degree of dependency, interdependence of technologies [11], shape and structural differentiation of blocks and, accessibility of components and connectivity of blocks [12]. Greater complexity requires more tasks, multiple set-ups, different jobs and setting and paths (as per conventional manufacturing) and, therefore, the cost increases [13]. Cost related to the production of AM does not depend on component complexity. This influences the decision for convenience evaluation between AM and SM, while no transitions for disassembly are considered. In the next section, the concept of complexity with the potential for recovery. The aim of this paper is: to investigate, while quantifying on case test analysis, costs related to complexity in the case of Additive and Subtractive application; to evaluate the potential use (in comparison with subtractive) of additive technology for application in the remanufacturing (limiting to spare features) domain. Thus, this paper proposes evaluation of returned products and cost complexity for remanufacture-ability across a two stages approach as defined in Fig. 1. 2.1 Component Complexity Estimation Per Remanufacturing Recyclability of parts and blocks, reuse of materials, upgrade of components, refining of design, recouping of costs involve remanufacturing as a success driver for sustainability [14]. As sustainability deeply involves circular economy and remanufacturing1 , usually economic and environmental aspects are used to determine the transition from linear to circular models [15]. Those models change as per the econometric conditions, price 1 BS 8887–220:2010. Design for manufacture, assembly, disassembly and end-of-life processing

(MADE) The process of remanufacture. Specification.

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fluctuation and geopolitical circumstances. However, complexity still remains related to manufacturability [16] much more than “renovation” and “reuse” [17]. Several case studies and reviews reported the difficulty in finding a metric to describe the suitability [18] and the potential recovery of materials in product [2]. According to Venegas et al., 2018, dis-assembly time can provide a measurement of operational complexity, however, it does not implicitly contain useful information on complexity of BoM, modularity of blocks, hierarchy in structure, and extraction of parts and blocks [19]. The remanufacturing propensity mainly depends on the product and its structure (as per the union of blocks and parts). The value mass and its level in Bill (as a mating face) is a static information of how much effort is required to extract/rearrange/reuse. It subsequently interacts with the connection type (its variety and quantity) to determine the production strategy from a cost oriented perspective. The Static product Based Complexity: First Level Analysis One metric that can be potentially used to assess the complexity issue in remanufacturability of parts points on leveling the subcomponents in the product layer, which can give information on the absorption of the disassembly process. Thus, the material and the spatial part distribution can provide an objective “burden” to the “complicatedness” of part [20]. Here entropy, a thermodynamics concept, could provide a metric for part regularity and complexity in treatment [21]. Entropy measures the degree of disorder in a system; low entropy value indicates greater degrees of order. As such, statistical entropy can provide information on the dependability of parts according to its design and material. Entropy is affected by the product structure and as: (i) number of different subcomponents in BoM raises complexity and disorder, (ii) easiness for disassembly through how different subcomponents were connected during manufacturing process, and (iii) ratio of different subcomponents in the product. To measure product complexity, Rechberger et al., (2002) proposed the formulation (Eq. 1) derived from Shannon’s entropy (1948) [22]: Nm ci ∗ log 2 (ci ) (1) H =− i

where Nm is the number of materials, and ci represents concentration of the i-th material in the product. It represents the ratio between mass of the i-th material and the total mass Mi . of the product: ci = M p Roithner et al., (2022), combined formula of Eq. 1 with the relative component distribution (if the product can be disassembled in sub-blocks) in product and as such, a concentration value: ci,j measures the amount of material to a specific block/part: M ci,j = Mi,jj where Mi,j is the mass of i-th material for j-th assembled block in product and Mj is the total mass of j-th block/part from 1 to Ne total number of blocks/parts in product [23]. The statistical can now be evaluated as per the Nm concentrations:  entropy   m c . The entropy of the final product (Hp ) is computed as c ∗ log Hj = − N i,j i,j 2 i per the mass weighted average (mass fraction of product part j equal to Mj ) of all Ne parts of the product (mass of the product p equal to Mp ). Here, the lower the value of Ne entropy in product (Hp ) the greater is the intrinsic recyclability: Hp = j mj ∗ Hj and mj =

Mj Mp .

From the above equations, the relative entropy of the product can be

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calculated in terms of number of materials (Nm ) and the maximum entropy value that Hp . corresponds to the value for equal concentrations: Hmax = log 2 (Nm ) and Hrel = Hmax With the relative entropy known, the authors proposed the evaluation of the Relative Product Recyclability (RPR) index as: RPR = 1 − Hrel =

Nm j=1

mj ∗ RPRj and RPRj = 1 −

Hj Hmax

(2)

changing from 0 to 1. Value of 0 is assigned whenever no recyclability pattern, in comparison with the highest product entropy, is resultin. It reports that the product has low suitability for recovery. Thus, the formulation, as per Eq. 2, of static complexity respects what Roithner et al., 2022 stated: a product with a long bill gets a huge complexity based on its entropy; the recyclability index, as per disassembly, reports higher values in bolted connection than on gluing; the more are the material in production the higher is the entropy amount with a consequence complexity in production cycles. The Cost Based Manufacturability Complexity: Second Level Analysis In case of a product classified as suitable for recovery after a static (intrinsic) entropy analysis (near to 1 of RPR value), then it is assigned to a second evaluation phase concerning cost function versus complexity. Graphical analysis can provide information on which method better suits for recovery, in terms of costs, for remanufacturing purposes. At this stage, complexity is evaluated based on the product’s form, entropy and operational complexity. Fera et al., 2018, proposed a formula to calculate product complexity 1 ∗ [IV + (1 − CI ) + OC] ∗ H where IV (α) based on those parameters: a = PC = 30 stands for information variety, Cl is form and OC is the operational complexity, H is a factor that has a maximum value of 10. PC acts on costs and it mainly weighs (from now on we are using α as per PC amount) production alternatives. Product factors influence production costs in terms of number of required tasks, group and lot assignment for wait to move allocation, number of set-ups, direct energy depreciation. Both AM and SM total costs vary on production volume that, over remanufacturing interpretation and under spare parts features, may change dynamically with returns [24]. In Additive Manufacturing (AM), the total cost of production is the summation of the cost of (i) materials, (ii) operator, (iii) processing, (iv) post-processing and (v) energy consumption. This paper makes use of the Watson and Taminger (2018) proposal for cost modeling analysis [25]. Material cost (C m,AM ) depends on mass m in [kg] calculated on Volume V and V ∗ρ Density ρ as per m = 1000 ; mass of supports ms ; mass of wasted material mwp ;   percentage of over material %s as per Cm,AM = Cp ∗ m + ms + mwp = Cp ∗   m(1 + %s) + ms + mwp where C P is the cost per kg of the specific (applied) material. Operator cost (C o,AM ) is assumed proportional to the design effort as per t progr,AM . It linearly depends on complexity level α. Processing Cost (C lav,AM ) is estimated as the product of working time (tlav,AM = V ∗(1+%s)∗60 )- calculated on Volume V and machine velocity v – and hourly cost Ci,AM =   v 1 - dependent on warranty cost W, depreciation D, maintenance Cip D + W + Cr ∗ d ∗c∗U cost C r , initial investment C ip , and yearly [days] d * [hours] c* [usability] U.

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  Post processingcosts Cpl,AM is function of the finishing factor β which determines the finishing time Tfin . We assumed finishing time depends on tolerances and this operation is equal in AM and SM.

V 10 ∗ β ∗ 100 per 2 ≤ β ≤ 10 Tfin [min] and Cpl,AM = Cfin,AM 60∗Tfin 0 per β = 1 with Cfin,AM = Co + 10 ∗ β the hourly cost of finishing on a C o reference.  Energy cost (Ce,AM = Ce ∗ PAM ∗ tlav,AM 60 ) is computed on machine power (PAM in kW) and time tIav,AM . The total hourly cost for the AM is so defined as proportionally (K) to the product complexity and can be unit computed considering the capacity rate of system, it has to be defined in relation to the specific part volume ( see Fig. 5) with a slicer approach. We considered that the production cost changes on geometric volume of the parts to be built and the build volume printing rate, setting parameters is proportionally to product complexity:  Co   Ci,AM  ∗ (K*a) + ∗ tlav,AM CTOT ,AM = Cp ∗ m(1 + %s) + ms + mwp + 60 60 Cfin,AM tlav,AM ∗ Tfin + Ce ∗ PAM ∗ (3) + 60 60 Using Subtractive Manufacturing (SM), the cost is assumed as related to the geometric volume of the raw material and the material removal rate determined, as per reference 26, by summing different entities as follows:  Material cost Cm,SM as per volume reduction is determined considering Volume w ∗ρ (V) and mass (mw ) and cost of material (Cw )(i.e., Cm,SM = Cw ∗ mw = Cw ∗ V1000 ).     changes on time to design t and to transform the part Operator cost C o,SM progr,SM    on complexity α as per Co,SM = Co 60 ∗  thistime dependent   tIav,SM . We assumed tprogr,SM + tlav,SM = Co 60 ∗ X ∗ α + tlav,SM where X is experienced based for AM and SM. We can assume that complexity affects designing  for SM (because  time of its multiple steps) more than AM. Again, tlav,SM = VQc ∗ 1 + eα 100 where V c is the volume of part we have to remove, Q is the removal capacity as per multiplication of cutting speed (vc ) and cutting depth (ap ) and round feed ( f n ). Working cost (C lav,SM ) is linearly dependent on machine cost (C i,SM ) as estimated per AM. We assumed that SM production gets finishing cost (C pl,SM ) as per finishing requirements (C fin,SM  ) and working time (T fin ). Analytically, this cost is determined + 2 ∗ β. Cpl,SM = Cfin,SM 60 ∗ Tfin with Cfin,SM = Co  Energy cost (C e,SM = Ce ∗ Pc,SM ∗ tlav,SM 60 ) is estimated on power requirements    kc ∗Q c Pc,SM = 60000 with kc = kc1 ∗ h−m ∗ 1 − γ0 100 where kc1 is the cutting specific m factor, hm is the chip thickness, γ0 is the cutting edge. In subtractive manufacturing, we quantified the use of tools as per Sandvik-Coromant manual on rotating tool [26]. This cost is exponentially related with complexity and It needs a direct input on product base as per working of 50 units as per the case test analysis of Fig. 5)   size(supposed Cut,SM = 11α ∗ 1 + eα 1000 + 726α and based on mortgage rate due to multiple usage.

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The total cost is, finally, defined as: 

  Co Vc Vw ∗ ρ α e C + 20α + ∗ 1+ CTOT ,SM = Cw ∗ 100 + fin,SM 60 ∗ Tfin 1000 60 Q  (4)



1 Ce Vc α α e e ∗ Pc,SM ∗ ∗ 1+ + 100 + 50 [11α ∗ 1 + 1000 + 726α] 60 Q

3 Measure the Two Levels Complexity for Remanufacture-Ability In order to demonstrate the usability of the decision support process, a case study was selected to decide which manufacturing process, additive or subtractive, should be used in terms of cost effectiveness for a returned EoL/EoU product over product complexity alternatives. We considered as uncertainty evaluation criteria the characteristics of components (reported on trapezium shape) as per (a) accessibility and different disassembly pattern in component evaluation; (b) mating and connection type in disassembly pattern (Fig. 2, 3, 4). The economic making-decision making model is sketched in Fig. 1. At the start of the process, information about the product and its components are collected. The model makes use of CAD design to identify different the disassembly paths. Space inference matrix along the six directions (X+ , X− , Y+ , Y− , Z+ , Z− ) are elaborated along with inference matrix to generate feasible Disassembly Process as per Jin el al., 2015 [30]. The model has been tested using a case study based on a gear pump (as per Ramirex et al., 2020). We evaluated the convenience for three - different structure - gear pumps (GP1, GP2 and GP3 respectively, as per [27]). The GP1 can be disassembled in 24 parts (Fig. 2) while GP2 and GP3 have groups that cannot be disassembled since they are designed to be considered in block format (Fig. 3 and 4). Most of the components are made of stainless steel AISI316, which has high corrosion and salt resistance (see Table 2 for details about components). Moreover, in the chemical composition there is chromium (18%), nickel (8%) and molybdenum (3%). The gear pumps, also, consist of rubber components, such as gaskets, and fluorine polymer components. The description of 24 components (grouped per color in Fig. 2, 3, 4) is reported in Table 2.

Fig. 1. Flow Chart of the two stages complexity estimation for cost competitive alternatives.

For the case study, GP2 has the following main components (see Fig. 3): (i) group 1 consists of components (1 to 7), (ii) component 2 (8), (iii) group 3 (9 to 17, except 12),

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(iv) component 4 (12), and (v) group 5 (18 to 24). The GP3 (see Fig. 4) components are based on the numbering of GP2 components: (i) group 1 (including components from 1 to 8), group 2 (including components from 9 to 17), (iii) group 3 (including components from 18 to 24). We calculated the statistical entropy (Hp) and RPR of each gear pump (Table 1). The relative entropy (Hrel) and constant maximum entropy (Hmax) are estimated based on the RPR index. The RPR is used to select product for remanufacture ability based on cost estimation. The GP1 value of RPR is higher than GP3. GP1 has lower entropy (Hp) indicating greater structural complexity.

Fig. 2. GP1 (a) Assembly Product and (b) BoM explosion

Fig. 3. GP2 (a) Assembly product and (b) BoM Explosion

GP1 was selected for a cost based analysis. GP3 reported the lowest RPR index, having the least potential for recovery of the returned product, mostly due to design and planning phase not having considerations for recovery. Based on the RPR value, GP1 is selected, and part number 9 is intended to be reused for cost evaluation versus complexity function. SLM280 machine is selected as the additive resource. In the subtractive approach, Universal Machining Centre is used. To measure the total cost, a table was created with all the available information on sub-costs collected (Table 3). The volume data was measured from the GrabCad Community CAD model where the outer diameter measures 50 mm, the inner diameter 32 mm and the depth is 36 mm. The volume of the envelope is defined as the cylinder of external diameter while the actual volume of the piece is known from the table, so the volume of chips represents the difference of the two. The percentage of metal surplus outlines a further hypothesis to be posed and is taken into account with a factor of 0.3 (Fig. 5).

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Table 1. Recyclability index for GP1, GP2 and GP3

Fig. 4. GP3 (a) GP3 Assembly Product and (b) BoM explosion

Table 2. Gear pump’s GP1 (24 components) - Properties of all components #

Component Name

Material

Volume

Mass

Density

[mm^3]

[g]

[g/cm^3]

1

Bolt A

Stainless steel A4 [AISI 316]

1243,07

9,76

7,852

2

Bolt B

Stainless steel A4 [AISI 316]

1243,07

9,76

7,852

3

Bolt C

Stainless steel A4 [AISI 316]

1243,07

9,76

7,852

4

Bolt D

Stainless steel A4 [AISI 316]

1243,07

9,76

7,852

5

Bolt E

Stainless steel A4 [AISI 316]

1243,07

9,76

7,852

6

Bolt F

Stainless steel A4 [AISI 316]

1243,07

9,76

7,852

7

Cover

Stainless steel A4 [AISI 316]

95973,49

753,39

7,850

8

Seal

Rubber NR

5496,27

5,22

0,950

9

Gear A

Stainless steel A4 [AISI 316]

21301,72

167,22

7,850

10

Gear B

Stainless steel A4 [AISI 316]

21301,72

167,22

7,850

11

Shaft A

Stainless steel A4 [AISI 316]

6430,7

50,48

7,850

12

Base

Stainless steel A4 [AISI 316]

273755

2148,98

7,850 (continued)

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S. Panagou et al. Table 2. (continued)

#

Component Name

Material

Volume

Mass

[mm^3]

[g]

Density

13

Tree B

Stainless steel A4 [AISI 316]

22560,02

14

Tie rod A

PTFE

3243,59

7,14

2,201

15

Tie rod B

PTFE

3243,59

7,14

2,201

16

Tie rod C

PTFE

3243,59

7,14

2,201

17

Tie rod D

PTFE

3243,59

18

Tie rod E

Stainless steel A4 [AISI 316]

14456,27

19

Cable gland pin A

Stainless steel A4 [AISI 316]

20

Cable gland pin B

21

[g/cm^3]

177,1

7,850

7,14

2,201

113,48

7,850

998,08

7,83

7,845

Stainless steel A4 [AISI 316]

998,08

7,83

7,845

Nut A

Stainless steel A4 [AISI 316]

289,52

2,27

7,841

22

Nut B

Stainless steel A4 [AISI 316]

289,52

2,27

7,841

23

C nut

Stainless steel A4 [AISI 316]

289,52

2,27

7,841

24

Nut D

Stainless steel A4 [AISI 316]

289,52

2,27

7,841

4 Conclusions Additive Manufacturing, as recognized in literature, sustains production for circularity and re-manufacturability [12, 19]. Closed loop approach and reverse strategies facilitate reuse and recovery of EoL/EoU parts. However, the uncertainty of returns is an issue to support the convenience in cost. Here, the decision about technology (Additive vs. Subtractive) has to be estimated. In this paper, a decision support process was proposed and tested, based on evaluating complexity and entropy of returned products and cost needed for remanufacturing. We used a complexity quantification, including product and group analysis, for part adaptability to remanufacturing. The Relative Product Recyclability index is proposed based on calculation of statistical entropy as a first evaluation and indication of a product’s suitability for recovery. If RPR sustains reusability, then a second evaluation is performed on the product to calculate the cost function based on the complexity of the product to remanufacture. A comparison between AM and SM costs (see Fig. 4 for the selected parts in GP1 product) was performed to showcase the decision support process, based on a scenario of

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Table 3. Relevant factors for Cost Based Complexity estimation in gear pump (Case GP1) TYPE OF MATERIAL DENSITY

SPECIFIC CUTTING FORCE

CUTTING PROCESSING

METAL CONSTRUCTION REMOVAL SPEED RATE AM

MATERIAL COSTS

Dens

Kc1

Mc

Kc

Vc

Ap

Fn

Q

v

C_polv

7,85

2300

0,21

2788

105

3

0,4

126

12

111,6

5,58

[g/cm^3]

[N/mm^2]

/

[N/mm^2]

[m/min]

[mm]

[mm/rev]

[cm/min]

[cm^3/h]

[€ /kg]

[€ /kg]

AM machine FINISHING FACTOR

PART DIMENSIONS

C_wp

SM machine

MAIN COST OPERATOR COST

ELECTRICITY COST

MACHINE Cost

NET POWER

MACHINE Cost

MAXIMUM POWER

stock thickness

beta

C_op

C_elet

Ci_AM

P_AM

Ci_SM

21,3

70,65

49,35

0,3

4

32,55

0,05

31,61

4,5

12,76

28

[cm^3]

[cm^3]

[cm^3]

/

/

[€ /h]

[€ /kWh]

[€ /h]

[Kw]

[€ /h]

[Kw]

volume_part volume_wp volume_chips

P_SMmax

three hypothetical gear pumps, with different design, composition and group classification. RPR values are calculated, followed by cost expectation for additive or subtractive manufacturing use in remanufacturing. Demand for replacement/remanufactured was supposed related to failure patterns as per spare part features. The specificity of spares in then considered as per complexity amount. The numerousness and heterogeneity of potential customers, with frequency and variety of requests, is supposed arranged on the paucity of yearly demand (as per Knofius et al., 2019 [28]). Consequently, we adapted the failure rate as per the yearly production volume of Fig. 7. Against the excessive costs for additive manufacturing of components as per rise in demand, a high diversity of variants (in form and dimension and shape) can be made of a single build (Fig. 6). We reported, additive has a higher cost than subtractive manufacturing under low to medium complexity rate. However additive costs are almost the same despite the rise in complexity, while subtractive has exponential growth. In this case, AM based strategies reports superior to conventional manufacturing (with the biggest fixed cost source) in terms of total variable costs. With regards to the sustainability issue, AM has lower (the biggest source is related to raw materials used for AM manufacturing) carbon emission if compared with the “iterative” movement of parts and block over different workcenters as per SM. Moreover, the cost based estimation, over different technologies, is influenced at fixed RPR as per entropy determination, by the demand capacity that in turn acts on the lot sizing. Considering aggregation, for nesting issue, of different products per unit cost quantification (i.e., Cu,SM and Cu,AM ) we can estimate how the product complexity α acts on process factors (e.g., saturations as per different nesting alternatives) and unit cost. Whenever enveloped shapes are considered (Fig. 6), the convenience between AM and SM can be set on production volume over different α (Fig. 7). As α increases in terms of aggregate group of products, if characteristics of component and disassembly transition are not altering the RPR value (we made the hypothesis that lot is combining different parts of selected re-manufacturing components), the unit cost changes per adaptability between components (where Ku is the rate between Unit cost for SM and Unit cost per AM). Then, the second level complexity based on cost is supporting AM of different production Volume. In the AM, the high production costs,

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Fig. 5. Competitive analysis as per complexity estimation in for GP1 related to Gear A.

Fig. 6. Combination of part (A, B, C shape) on a single remanufacturing order

Fig. 7. Unit cost convenience between SM and AM over different volume as per aggregate Lot Complexity (α was here estimated combining different envelopes, and quantity, of components (e.g., Fig. 6)) estimation

which will likely decrease in the near future, is currently compensated by unpredictability of stock based on the order of magnitude and the timing of request (typical of EoL and EoU parts) [29]. The aim of this paper was to quantify and investigate, on a cost base perspective, about the potential use of additive in the re-manufacturing domain. We proposed a way to quantify the recyclability role of part as related to the geometric, volume and entropy (as per the manufacturing BoM). The proposed calculation methods was limited to demand events results in limited capacity of different, cumulated, Poisson requests. The model does not include stock (inventory and supply alternatives are not considered). The current approach mainly depends on the cost of the complexity that is currently investigating by the author over an experiment analysis. The proposal makes use of time and references to assign hourly cost. There is no experimental data, at the moment, over differences between AM and SM on structural composition and weight reduction possibilities. Authors are now trying to validated the assumptions done over the cost comparison on real experimental data. It is now conducting new cost formulation while considering the optimization of scheduling for AM machines.

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Towards a Green Transition: Preliminary Steps of a Quantitative Model Federica Costa(B)

and Alberto Portioli-Staudacher

Department of Management, Economics, and Industrial Engineering, Politecnico Di Milano, Via Lambruschini 4/B, 20156 Milan, Italy [email protected]

Abstract. Recently, environmental sustainability has been tackled several times due to the impending climate change the earth is facing. Numerous techniques have been applied to reverse the direction companies were going into. In this paper, it is explained the importance that Lean Manufacturing tools and Industry 4.0 technologies can have on the sustainable side of a company. The aim of this work is to fill the scientific gap related to studies deepening the combination of these different paradigms, Industry 4.0-Lean-Green, which have been scarcely investigated together and proposing the preliminary results of a quantitative model that helps towards a green transition. Thus, a Systematic Literature Review has been performed to detect which were the key variables of these three fields and then, it was studied what their interaction was. Then through mean of pair wise comparison matrices the first steps of the quantitative models are shown that investigate the relationship amongst the key variables identifies showing also transitivity relationship. This study is giving the opportunity to understand the main variables of Industry 4.0 and Lean manufacturing on which companies have to act in order to have an impact on green variables and their overall environmental sustainability. Keywords: Digital Technologies · Green Paradigm · Lean Manufacturing; Industry 4.0

1 Introduction Nowadays, the demands of customers and the concerns of the environment are driving industries to strive for greater precision, accuracy, and speed [1]. Customers seek products more customized, flexible, durable with the least possible cost and the highest possible quality while also meeting their personal and social needs [2]. On the other side, there is a pressure for the reduction of resources’ usage and the release of the minimum possible pollution, tackling the climate change and working to preserve the ecosystem [3]. As a result, industries, as part of our society, are pushed by an urgent call for action. To answer to these combining needs, companies select and team up different approaches. The incorporation of Industry 4.0 (I4.0) technologies, driven by the demands of customers and the concerns for the environment, has become a trending approach for industries, enabling them to achieve greater precision, customization, and competitiveness © IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 229–240, 2023. https://doi.org/10.1007/978-3-031-43688-8_17

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while addressing sustainability challenge [4, 5]. The digitalisation enhances operational improvements, improving operational performance [6] and can play a role in achieving superior social and environmental sustainability levels for organisations [7]. Similarly, the principles of Lean Manufacturing (LM) driven by a philosophy of continuous improvement and waste elimination, have become the foundation for revolutionizing the manufacturing industry and increasing customer satisfaction [8]. Indeed, the concept of waste in the LM context aims to make products and/or services of higher quality at the lowest possible cost in the least time possible by eliminating waste [9]. Furthermore, the Green Paradigm (GP) is characterized as a path leading to waste and pollution reduction by minimizing the consumption of natural resources and maximizing the recycling and reutilization of materials considered as waste [10]. The objective of the GP is to diminish environmental risks and impacts while enhancing ecological efficiency and eliminating environmental waste within organizations, thereby assisting them in making environmentally sound decisions [11]. Given the importance of I4.0 and LM in achieving superior level of environmental sustainability there is a lack in the literature of a comprehensive model that include the practices of the different approaches and directs towards a green transition. Previous studies have examined the drivers for LM and GP [12], as well as the barriers for LM and GP, using interpretive structural modeling (ISM) to understand their relationships [13]. Additionally, Vinodh [14] developed a model based on ISM to analyse the barriers to integration of LM with I4.0. However, no studies have been found that address the utilization of both LM and I4.0 approaches for green transition. Therefore, the following research question was formulated: “How can the integration of I4.0 and LM practices within a comprehensive model enhance companies’ ability to achieve a green transition and attain a superior level of environmental sustainability?” Therefore, this works aims develop a preliminary model that integrates I4.0 and LM practices and examine how this integration can enhance companies’ ability to achieve a green transition and attain a superior level of environmental sustainability.

2 Systematic Literature Review (SLR) The SLR represents the starting point and it is beneficial to answer to the focal question of this work. Firstly, to guarantee valid results, each of the three paradigms was defined. Secondly, to study all the three topics, it has been decided to focus the attention on three investigations made by the combination of the three paradigms: LP, I4.0 and GP. The database selection was fundamental to base the research on a reliable source. Papers have been selected from Scopus containing renowned publications like Emerald, Taylor and Francis, Springer, IEEE, and Elsevier. The keywords have been chosen with the aim of reaching more inherent articles possible. The filtering performed after the research was based both on inclusion criteria as document type (review), subject area (Business, Management and Accounting; Engineering; Energy; Environmental Science; Social Sciences; Decision Sciences; Computer Science; Economics, Econometrics and Finance) and language (English) and on additional screening techniques looking at the Journal Quality type (Q1), title, keywords and abstract. Finally, the reference analysis was done checking the inherent topic, Q1, title, keywords and abstract of the cited documents.

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The initial total amount was of “110” Documents. These ones have been filtered on the inclusion criteria obtaining just “19” reviews and then, they have been analysed on the quality of the journal, the title, the keywords and the abstract to understand the relevance of the content. Three reviews came out and the analysis of each related references have been pursued: afterwards, 30 articles have been considered. As a result of single research about the three topics, the I4.0 and the LM represent analysed and well-defined topics in the literature, while the boundaries around the GP result still more blurred, because addressed in recent years. Then, the involved combinations have been the following: the understanding of how i4.0 technologies and LM practices link each other, how digital technologies (DTs) can influence the GP and how LM practices impact on the GP. 2.1 I4.0 and LM The analysis related to the combination of I4.0 and LM concepts saw the selection of “Lean AND Industry 4.0 AND Literature review” as keywords to conduct the research. The initial quantity of found documents was 110. After filtering them and taking into account references, 30 articles have been considered. I4.0 and LM tools are presented as complementarities to support system design and improvement. Although LM practices can be carried out without the help of IT tools, manufacturing digitization is crucial to the LM implementation and its continuous improvement to achieve benefits in term of productivity improvement. The literature evidences that is unclear, on a quantitative basis, which practices could be combined, which ones are complementarities, and which contradict each other, concluding with the lack of a framework quantifying all the possible links between I4.0 and LM [15, 16]. 2.2 I4.0 and GP In order to analyse the I4.0 and Green context, the keywords have been “Green AND Industry 4.0 AND Literature review”. The research has given 201 documents as a result. After the filtering phase, 9 papers remained, and the next phase of the reference analysis added 31 more. Through sustainability practices (sustainable production, sustainable purchasing, sustainable performance measurement and management, sustainable governance, sustainable marketing, sustainable design and circular economy), I4.0 contributes to sustainability performances (environmental, social and economic ones) [17]. Such practices offer advantages such as manufacturing productivity, resource efficiency and waste reduction and control of energy consumption. Overall, DTs will have a positive impact on the environmental performance, even if fully automated production could lead to a higher energy consumption or an increased demand for scarce raw materials. 2.3 LM and GP For the analysis between the LM and the GP, “Lean AND Green AND Literature review” as keywords have been chosen. This time the research led to 38 documents of which just 5 were selected according to the inclusion criteria. The final number obtained from that investigation resulted to be 44, because other papers were added after the reference

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analysis phase. Researchers conclude that LM and GP are overlapping in terms of obvious similarity: the LM considers as the main objective the reduction of time and waste, as well as the GP consider the reduction of the environmental footprint, but the definition of waste represents a conflicting point. LM focuses on workforce and space reduction to increase flexibility, while GP aims at reducing, recycling and reusing (3Rs). Additionally, LM aims at reducing non-value adding activities which could be translated as the reduction of energy and natural resource consumption. Considering the LM practices, the Value Stream Map is one of most used tool because it is adaptable to the context with just a change in the meaning of the type of non-value added activity. 5S allow to achieve less defects, cutting the environmental waste; the Human Resource Management has a positive effect considering the training and commitment of the people. Total Productive Maintenance also has a positive effect through the proactive and preventive maintenance, reducing emissions, resources and scraps; Just-in-time is one of the most conflictual, in both positive and negative way [18]. Tools, principles, resources, practices and strategies, proper of each of the three cited paradigms, can be promoted as variables to achieve the common goal: the exploitation of the I4.0 and LM paradigms to impact the GP. The Table 1 showed the three clusters of key variables. Table 1. Three clusters of key variables. I4.0

LM

GP

VAR 1: Cyber Physical System

VAR 10: Lean Waste Management

VAR 22: Sustainable production

VAR 2: Cloud

VAR 11: Just-in-Time

VAR 23: Sustainable performance measurement and management

VAR 3: Internet of Things

VAR 12: Kanban

VAR 24: Sustainable governance

VAR 4: Big Data Analytics

VAR 13: 5S

VAR 25: Sustainable design

VAR 5: Additive Manufacturing

VAR14: Single Minute Exchange of Die

VAR 26: Green supply chain

VAR 6: Artificial Intelligence

VAR 15: Value Stream Map

VAR 27: Circular strategies

VAR 7: Augmented Reality

VAR 16: Customer engagement

VAR 8: Blockchain

VAR 17: Supplier collaboration

VAR 9: Autonomous Robots

VAR 18: Total Predictive Maintenance VAR 19: Kaizen VAR 20: Total Quality Management VAR 21: Human Resource Management

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3 Methodology Decision-making in complex environments necessitates identifying and understanding the system elements and their interrelationships [19]. The SLR revealed that if both the I4.0, LM and GP have been studied in the academia and applied in many contexts, the links among them is not so easy to be tackled, even more if looking at the quantitative point of view. In order to boost the understanding of the SLR results, the methodology applied linked the interpretive structural modelling and cross-impact matrix multiplication analysis (ISM–MICMAC) approaches to model and analyse dependencies and impacts among the twenty-seven key variables identified. In order to accurately and reliably determine the GP susceptibility, it has been necessary to identify and benchmark the important factors characterizing each of the three concepts analysed, output achieved through the SLR, and to clarify the relationships among them. The Interpretive Structural Modelling (ISM) plays an essential role in such conditions and is a well-known methodology for understanding interactions between definite factors that describe a problem to solve. ISM is a qualitative, reliable and widely applied decision-making approach [20] that facilitates the explanation of complex relationships among variables of a real system and transforms them into a meaningful visual model [21, 22]. The ISM method was developed by John N. Warfield, between 1971 and 1974 and is based on the principle of pair-wise comparison [23]. Indeed, the ISM output is a hierarchical diagraph with nodes and directed arcs, wherein the nodes denote the variables of the system, and the arcs indicate the direction of the associations. Graphical displays improve the performance of decision-makers in tasks such as identifying trends or detecting patterns in the relationships among variables [24]. The method is illustrated in Fig. 1.

Fig. 1. Methodological steps for ISM approach.

4 Analysis of Results The SLR allows to identify twenty-seven key variables (Table 1). One important aspect to bear in mind is that the impact of each variable does not depend solely on the commitment that the organization dedicates to its specific performance, but it can also depend on how the actions foreseen in other variables are carried out. Consequently, it is important to include not only the 27 variables identified, but even the interconnections and interferences existing between each of them. The relations between variables are defined and developed based on what have been discussed in the literature to overcome the drawbacks of ISM which uses experts’ opinions to determine the relationships among variables [25]. Additionally, since the focus

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is about the understanding of how the GP variables are affected, the relations analysed in this study comprehend the ones between the I4.0 and the GP, the LM and the GP and the ones between the I4.0 and the LM. This latter is required to have the comprehensive view about how industrial and LM variables relate each other’s giving a shape to the GP in the manufacturing context. Four out of the 27 variables will be elucidated in terms of their impact on other variables. For example, for Cyber Physical System (CPS) (VAR 1) the following interactions have been identified: – VAR 12: CPS has high flexibility and can be applied in different ways and for various purposes [26], enabling the reduction of inventory levels and enhancing the productivity by facilitating interconnectivity among productive processes. – VAR 15: As an adequate application of VSM requires a large amount of information [27], the incorporation of CPS could provide more accurate data, being used as supportive technology and having a high proliferation across the value stream. – VAR 18: CPS allows to share data with network to optimize preventive maintenance [28]. – VAR 21: The assignment process of employees for different operations, based on their availability, is assisted by CPS [29]. – VAR 22: CPS is used to address energy-efficient production to reduce the energy use and contribute to product life-cycle management [30], as well as it has the potential to reduce water demand in manufacturing by systematically changing operational control strategies. – VAR 25: Since CPS is the digital representation of a real and physical object, it allows, through the virtual prototyping, the identification of critical points in the products life cycle and the enhancement of a sustainable product design [31]. – VAR 27: CPS may increase the efficiency of various activities, such as the materials selection [32], material reuse and recycling [33]. For Autonomous Robots (VAR 9) the following interactions have been identified: – VAR 11: Robots can collaborate to respond in real time and ensure that production runs smoothly [34]. – VAR 12: Robots empower the supply of materials to workstations and supermarket managed through the Kanban system [35]. – VAR 13: Robots allow to achieve standardization and improved processes through detecting and correcting production errors, to adjust production according to unfinished products [34, 36]. – VAR 21: Robots assist employees in their training and work and benefit from a certain level of autonomy to react to the employee’s actions [37]. – VAR 22: Human-robot collaboration increases production performance [38], since they allow for smart material planning and allocation systems, giving a contribution to material efficiency and saving significantly. They can also support worker’s ergonomically and prevent posture problems [38] and can detect the proximity of humans and reduce the safety risk to them. – VAR 23: Robots can be equipped with different types of sensors and smart meters to monitor operations and measure energy consumption [39].

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For Kaizen (VAR 19) the following interactions have been identified:VAR 9 impacts on: – VAR 2: Kaizen needs for Cloud ability of sharing and communicating information in real time with internal and external audiences for continuously improve processes [28]. – VAR 4: Continuous improvements efforts impact on the capability of BDA to process even more data with the aim of achieving higher performance, quality and customer satisfaction, as well as lower waste produced and costs [15]. – VAR 9: Kaizen constantly shapes the processes on which autonomous robots have to adapt to better serve their work [15]. – VAR 22: Kaizen provides a problem-solving structure, reducing materials wastes and pollution while improving manufacturing process by optimizing the performance of the supporting flow [40]. – VAR 23: Kaizen is considered as an important mediator to achieve environmental performances, as well as the continuous improvement way of thinking improves the sustainable management of performances [41]. – VAR 25: Kaizen acts on the design phase with continuous improvements in order to develop a product. – VAR 26: For the green supply chain, Kaizen way of thinking is extended to all the actors in the value chain. – VAR 27: Continuous improvement techniques impacts also the way in which a company manage the reusage of a product. For Value Stream Map (VAR 15) the following interactions have been identified: – VAR 2: The VSM requires the Cloud to constantly update the processes map since it allows to share and communicate information in real time with internal and external audiences [28]. – VAR 4: The impact of VSM on BDA relies on the need of processing data to constantly map the processes [28]. – VAR 22: By mapping the value stream, the actors could be aware about the environmental impact of processes, thus avoiding excess resource consumption and environmental wastes [40]. – VAR 23: Having a better identification and visualization of non-value-adding activities, the measurement and management of performances, in terms of environmental wastes as waters, energy, air emissions, are improved [42]. – VAR 25: The VSM lets to analyse each step of the product lifecycle, so it allows to focus on the design enhancing the sustainable side [43]. – VAR 27: The VSM affects the circular strategies because it allows to have a clear overview of the materials states, where they are positioned and where the reuse processes start [42]. For space constraints, all variables impacts are displayed in the Initial Reachability Matrix (IRM) and illustrated in Fig. 2. It translates the previously identified interactions for all variables into a matrix that has on the axes the variables ordered numerically from top to bottom and from left to right. The matrix obtained consists only of binary numbers (0 or 1): “1” is inserted in the intersection cells between two variables when

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the variable on the row influences the readiness of the variable in the column or “0” is inserted in the intersection cells between two variables when the variable on the row does not influence the readiness of the variable in the column [25]. Indeed, if variablei impacts on variablej , “1” is entered in the cell (i,j). Whether variable i does not influence variable j, “0” is entered in the intersection cell (i,j). By convention, the “1” have always been positioned on the diagonal.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

2 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0

3 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

4 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0

5 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

6 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

7 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

8 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 0 0 0 1 0 0 1 0 0 1 0 0 1 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 0 0 0 0 1 1 1 1 1 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 0 1 1 1 0 0 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 0 1 0 0 0 1 0 0 0 1 0 0 1 0 1 0 0 1 1 0 0 1 0 0 1 1 1 0 1 1 1 0 1 1 0 0 1 1 1 0 1 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 1 0 1 0 1 1 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

0 = no impact was identified between the variables 1= impact was identified between the variables *Rows from VAR22, which represent the GP variables, are blank because the impact of GP practices on I4.0 and LM was not analyzed in this study.

Fig. 2. Initial Reachability Matrix (IRM).

IRM has been improved considering the transitivity check. It allows to analyse the interrelationship between variables by applying the transitivity logic: if variable “a” impacts on variable “b” and variable “b” impacts on variable “c”, then variable “a” impacts on variable “c” [44]. The transitivity analysis is carried out on two levels. Indeed, the analysis of the influences of variable “a” is not limited to variable “c”, but the option also analysed is when variable “a” impacts on variable “b”, variable “b” impacts on variable “c” and variable “c” impacts on variable “d”, therefore it is stated that variable “a” impacts on variable “d” with a second level transitivity. The Final Reachability Matrix (FRM) is derived from the IRM and incorporates the first and second level of transitivity. The FRM is illustrated in Fig. 3. Every cell that contains “0” sections in the IRM is qualified for transitivity check. If any interrelation between variables occurs, the “0” is replaced by 1* or 1**, otherwise no changes happen. The first level transitivity is translated into 1*, while the second level transitivity with 1** [45].

Towards a Green Transition: Preliminary Steps 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

1 2 1 1* 0 1 0 1* 0 1* 0 1* 0 1* 0 1* 0 0 0 1* 1 1** 0 1 0 1** 0 1** 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

3 4 5 0 1* 1* 0 0 1* 1 1* 1* 0 1 1* 0 0 1 0 1* 1* 0 1* 1* 0 0 0 0 1** 1* 1 1 1** 0 0 1** 0 0 1 0 1** 1** 0 0 0 0 1 1** 0 0 0 0 0 0 0 0 0 0 1 1** 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

6 7 0 1** 0 0 0 1** 0 1* 0 0 1 1** 0 1 0 0 0 1* 0 1** 0 0 0 0 0 1 0 0 0 1** 0 0 0 0 0 0 0 1** 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

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8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 0 1** 0 1** 1 1** 1** 1 1** 1** 1 0 1** 1 1 1* 1** 1 1* 1 0 0 0 1 1 0 1 0 1 1 1** 0 0 0 1* 1 1 1 1 1 0 1** 0 1** 1 1** 1** 1 1** 1** 1** 0 1** 1** 1 1 1** 1 1 1 0 1* 0 1 1 1 1 1** 1 1 1 0 1 1 1 1 1 1 1 1 0 0 0 1 1 0 1 0 1** 1** 1 0 0 0 1 1* 1** 1 1* 1 0 1* 0 1 1 1** 1** 1 1** 1** 1 1 1 1** 1 1 1 1* 1 1 0 1* 0 1 1 1 1** 1 1** 1** 1** 0 1** 1 1* 1 1** 1* 1* 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 1* 1 1* 0 1 0 1 1 1 1** 1** 1** 1** 1** 0 0 1 1 1 1** 1** 1* 1* 0 1 1 1* 1* 1* 1* 1* 1* 1* 1* 0 1* 1* 1 1 1* 1* 1 1 0 0 0 1 1* 0 1* 0 1* 1* 1** 0 0 0 1 1 1* 1* 1 1* 0 0 0 1* 1 0 1* 0 1** 1** 1* 0 0 0 1 1 1** 1* 1 1* 0 1 0 1* 1* 1 1** 1* 1** 1** 1** 0 1** 1* 1 1 1** 1** 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1** 0 1* 1* 1* 1* 1 1* 1* 1* 0 1* 1* 1 1 1* 1 1* 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 1 0 1 0 1* 1* 1* 1* 1** 1* 1* 1* 1 1* 1* 1 1 1* 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

1*= first level transitivity 1**= second level transitivity *Rows from VAR22, which represent the GP variables, are blank because the impact of GP practices on I4.0 and LM was not analyzed in this study.

Fig. 3. Final Reachability Matrix (FRM).

It can be observed that certain variables have a significant impact on others. For instance, VAR3 (Internet of Things) has been identified to have a direct or indirect impact on almost all practices. The Internet of Things (IoT) supports LM and GP practices by enabling real-time data collection, which allows for the identification of waste and inefficiencies. Additionally, IoT facilitates machine-to-machine and system collaboration, enhancing production efficiency. In terms of sustainability, IoT enables precise resource monitoring and the implementation of recycling and reuse practices, thereby reducing unnecessary consumption and environmental impact. On the other hand, some variables have a lesser influence on others. For example, VAR16 (Supplier Collaboration) only impacts VAR23 (Sustainable Performance measurement and management), VAR25 (Sustainable Design), VAR26 (Green Supply Chain) and VAR27 (Circular Strategies). In other words, it does not have an impact on any I4.0 practices. The lack of impact of the LM practice of Supplier Collaboration on variables related to I4.0 may be due to several reasons. The practice itself may not be directly aligned with the specific aspects of I4.0, or there may be a disconnect between supplier collaboration approaches and I4.0 practices. It is important to note that the analysis of the impact of GP practices on I4.0and LM practices was not performed, which is why the lines from VAR22 are with zero.

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5 Conclusions This study aimed to address the need for a comprehensive model that integrates I4.0 and LM practices to enhance companies’ ability to achieve a green transition and attain a superior level of environmental sustainability. By developing a preliminary model, we have taken the initial steps towards understanding the potential of this integration and its impact on sustainability. Through a systematic literature review, we identified 27 key variables associated with I4.0, LM, and GP. We then examined the relationships between these key variables, considering both direct impacts and transitivity relationships. This research presents the preliminary steps of a qualitative and quantitative model that can support companies in transitioning towards the Green Paradigm. However, it is important to acknowledge the limitations of this study. Firstly, the research focused on the identification and analysis of key variables, but further empirical validation and testing of the proposed model is needed. Another limitation is that a Structural Self-Interaction Matrix was not performed and could improve the results. Future studies should aim to collect empirical data to validate the relationships between these variables and assess their impact on environmental sustainability. Additionally, the scope of this study was limited to the integration of I4.0, LM, and GP practices. Future research could explore the potential integration of other relevant paradigms Resilience Paradigm. Finally, it is important to acknowledge that the preliminary nature of the model presented in this study represents a limitation. The subsequent stages of the model, which would provide a more comprehensive framework, are left for future research.

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Rapid Sorting of Post-consumer Scrap Aluminium Alloys Based on Laser-Induced Breakdown Spectroscopy (LIBS) Md Ali Akram(B) , Ragnar Holthe, and Geir Ringen Department of Manufacturing and Civil Engineering, Norwegian University of Science and Technology (NTNU), 2815 Gjøvik, Norway [email protected], {ragnar.holthe,geir.ringen}@ntnu.no Abstract. This article provides a six-year state-of-the-art evaluation of the published research into opportunities and challenges of implementing LIBS technology for sorting post-consumer scrap aluminium alloys and a case study in aluminium automotive parts manufacturing industry. Findings from the literature review indicate that, with the suitable machine learning model, optimization of the laser pulse and spectrometer detector, and efficient alloy separation technology, it is possible to achieve accuracy near to 100 percent within a lab pilot LIBS machine sorting of post-consumer scrap aluminium alloys. The results also suggest that automated LIBS sorting technology may have great potential to solve the difficult task to sort Aluminium PCS on an alloy family level and on a specific alloy type. The reviewed articles show successful applications on lab scales but also point out the challenges to be solved before LIBS can successfully be implemented on a larger industrial scale. The review has been supplemented with a case study covering Metallco Aluminium AS, Metallco Fredrikstad, and Benteler Automotive Norway AS. The study covers the complete aluminium value chain from cradle to cradle and more specifically manufacturing of aluminium automotive products, collecting end-of-life (EOL) cars, shredding them and sorting PCS, and finally reintroducing the PCS into casting new alloys to be used for new automotive products. The case study showed the great potential of LIBS sorting technology to establish larger-scale use of PCS aluminium within the automotive circular value chain. Keywords: Post-consumer scrap (PCS) · Aluminium alloys Laser-induced breakdown spectroscopy (LIBS)

1

·

Introduction

Aluminium primary production and its value chain from mining until finished primary aluminium are polluting and highly energy-consuming. Primary aluminium production accounts for 3.5% of the world’s power consumtion and produces 3% of the world’s carbon dioxide [1,2]. Global demand for aluminium is c IFIP International Federation for Information Processing 2023  Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 241–255, 2023. https://doi.org/10.1007/978-3-031-43688-8_18

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expected to increase by 80% by 2050 [3] and today the main energy source for primary production is coal [4]. Substitution of primary aluminium with recycled aluminium based on post-consumer scrap (PCS) reduces CO2 emission by more than 95%, when compared to primary production based on coal. Approximately 75% of the total aluminium already processed is still being utilized today, which shows the significance of aluminium scrap recycling [5]. Recycling one ton of aluminium prevents the release of 6 tons of bauxite into aluminium oxide during processing and avoids the emission of 9 tons of carbon dioxide [6]. Every year, the planet reduces over 100 million tones of carbon dioxide emission due to aluminium recycling. The European Aluminium Association has set a target of increasing the share of recycled aluminium in finished European goods from 26% in 2000 to 49% in 2050 [7]. A major part of recycled aluminium originates from the shredding of endof-life (EOL) cars. Aluminium alloys must first be sorted before they can be recycled. Typically, over-belt magnets or drum magnets remove magnetizable ferrous scrap as the first step in a scrap sorting line [8]. Eddy current separators are used to further purge polymers from aluminium feedstock following magnetic separation. Second, the aluminium scrap is processed to remove copper, brass, and zinc. Since conventional practice does not allow for the alloys to be separated, they are all stored together. Remelters use sorted wrought alloy scrap to create extrusion billets and rolling ingots [9]. Today practice nearly all recycled aluminium from shredding end up as casting alloys which have much wider acceptance levels for alloying elements and impurities. Using conventional established sorting technologies based on differences in specific weight, casting alloys can be separated from wrought alloys. For cast houses to be able to use PCS wrought alloys for extrusion billet, and rolled slab casting, they need to be sorted at least into their alloy families 5xxx, 6xxx, and 7xxx series alloys. With the LIBS technology implemented on an industrial scale, successful recycling of wrought alloys can be established and opened up for using PCS in extrusion, forging, and rolled products. Fast multi-elemental and in-air analysis are two of LIBS’s main “selling point” for usage in a wide range industrial applications from metal classification to real-time process monitoring or quality control [10,11]. Using a scanning LIBS system with a twenty-channel Paschen-Runge spectrometer, aluminium alloys can be identified for use in large-scale metal sorting operations [12]. With a demonstrated mean identification accuracy of greater than 95%, eight distinct families of aluminium wrought alloys were successfully categorized. A multiCCD Paschen-Runge spectro-meter could analyze and sort singularized objects at speeds of up to 100 per second, according to recent industrial-scale LIBS sorting research [13]. The primary goal is to provide a clear image of the present potential implementation advantages and the problems still experienced in the process of implementing LIBS technology for post-consumer scrap aluminium sorting, which help its development. Based on the facts mentioned above, two research questions (RQ) have been formulated for this literature review:

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Fig. 1. Flowchart of Methodological Procedure adopted from [14] Table 1. Accessed articles and journals developed by author Article

Journal

Year Publisher

(D´ıaz-Romero et al.) [15]

Spectrochimica Acta Part B

2022 Elsevier

(den Eynde et al.) [16]

Procedia CIRP

2022 Elsevier

(Engelen et al.) [17]

Procedia CIRP

2022 Elsevier

(Kim et al.) [18]

Spectrochimica Acta Part B

2021 Elsevier

(Brooks and Gaustad,) [19]

Journal of Sustainable Metallurgy 2021 Springer

(Fugane et al.,) [20]

Analytical Sciences

2020 JSAC

(Rombach and Bauerschlag) [8] Light Metals 2019

2019 Springer

(Piorek) [21]

Materials Today

2019 Elsevier

(SHIN et al.) [22]

Plasma Science & Technology

2018 IOP

(Campanella et al.) [23]

Spectrochimica Acta Part B

2017 Elsevier

RQ 1: What particular opportunities and challenges of implementing LIBS have been identified in the last six years of literature for post-consumer scrap aluminium sorting? RQ 2: According to existing research and a case study at Metallco Fredrikstad, Metallco Aluminium, and Benteler Automotive, how can LIBS technology be used in the sorting of post-consumer scrap aluminum in the near and long term?

2

Method

To perform this research, the strategy that had been used is laid out in Fig. 1 (Denyer and Tranfield, 2009) [14], which entails conducting a comprehensive

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literature review. Google Scholar and Scopus, two of the most popular international databases, were utilized to look for relevant research publications. Finding academic articles stored in the NTNU library’s ORIA collection was also a priority. We found the articles by searching for phrases like “LIBS,” “post-consumer scrap,” and “aluminium alloy.” Based on these criteria, we chose 18 papers that focused on or provided conceptual support for or case studies of sorting postconsumer aluminium alloys using LIBS technology. A time frame of 6 years was considered, featuring 2022 as the starting point. From the 18 papers initially considered, only 10 articles met the criteria and were included in the final analysis shown in Table 1. Articles from reputable international periodicals that have been peerreviewed and included in searchable databases were kept. Authors believe that large-scale industrial LIBS machines and portable handheld LIBS might complete the study. The main findings from the research are presented in Sects. 3.1 through 3.8 and supplemented by a case study in Sect. 3.9. Researchers [18,19,22] discuss LIBS for sorting all types of metal alloy scrap in a few of the papers chosen for this review. The research scope is limited to aluminium and its alloys. Also, process scrap from manufacturing is not included in this review because manufacturing process waste is not “post-consumer” scrap. The literature review supports the research activities within the Greenplatform project Alu-Green. Within this project, a pilot industrial machine has been installed at Metallco Fredrikstad. As a part of this work as an Alu-Green project partner we studied in detail the value chains at the three companies Metallco Aluminium, Metallco Fredrikstad and Benteler Automotive. The study comprises mapping of the manufacturing process at each company during interviews and shop floor studies. The purpose was to understand their current practices and processes and analyze how each of them can benefit from using the LIBS sorting technology to get access aluminium PCS sorted into specific alloys and alloy families.

3 3.1

Results Complexity of Operations

The operational difficulty of portable libs and industrial-size libs are discussed here by the authors. LIBS portable analyzer classification was studied by researchers [19,21]. Short pulses of light at a wavelength of 1064 nm nm are generated by Q-switched, diode-pumped Nd/YAG lasers in portable LIBS devices. By ablating the sample, which emits light with a wavelength between 200 and 700 nm, these pulses produce plasma. A tiny spectrometer with a CCD detector is used to collect and examine the light that is being emitted. An alloy’s composition can be determined by comparing its elemental signature, which was obtained from the LIBS spectrum to a database for grading and identification purposes.

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Researchers have explored a greater degree of complexity for LIBS sorting machines used on an industrial basis [15,16,18,22]. The PCS alloys are transported in a feeder conveyor at a consistent speed in a dynamic industrial LIBS system, where a laser pulse is placed on the surface of a moving alloy, a spectrometer analyzes the wavelength produced from the alloy, and 3D cameras record the data. Gate delay (the period between both the laser pulse as well as the spectrometer detection switching on) and gate width (the duration the detector is on) must be optimized for the best possible outcome. Using a gate delay duration of 500 nanoseconds and a gate width of 50 microseconds, optimization was performed in research [20] while the spectrometer had set values for its gate width (1.1 milliseconds) and gate delay (1.28 microsecond) in the study [18]. The operational procedure of both portable and industrial dynamic LIBS are shown in Fig. 2

Fig. 2. Operational complexity in Portable handheld LIBS machine (left) and industrial scale LIBS machine (adopted from [15, 21])

3.2

Machine Learning Models and Data Analytics

Shin et al. [22] proposed a method based on Principal component analysis (PCA) methods by deciding on alternative input parameters (spectral lines) based on knowledge about the chemical elements of comparable metals. Full-spectrum LIBS data was first subjected to PCA, and afterward, using the loading plots, the most relevant input variables were selected and assigned weights accordingly. The classification accuracy of the proposed technique was almost identical to that of full-spectrum PCA in tests with aluminium alloy, while a factor of 20 or more decreased the calculation time. The results revealed that integrating information about constituent elements can greatly increase classification speed without compromising precision. [23] is part of the SHREDDERSORT initiative to develop a LIBS-based approach for sorting light alloys. LIBS was used to analyze stationary aluminium shredder samples. Using artificial neural networks, LIBS spectra were processed. Separation might be based on basic emission line ratios, but neural networks offer more repeatable results due to the low intrinsic reproducibility of LIBS devices. Neural network group samples and determines elemental amounts. In the literature [15], a three-way sorting of aluminium alloys using LIBS, machine learning (ML), and deep learning (DL) has been proposed. The data-set

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contains 943 aluminium alloy scraps. The first 733 pieces of Al trash are used for XRF pre-training and validation, while the second 210 pieces are used to evaluate samples of unknown compositions. A combination of denoising and the extraction of 145 features from the original LIBS spectra is the proposed strategy. Specifically, this study compares three machine learning (ML) algorithms for sorting unidentified aluminium post-consumer scrap into three distinct but potentially valuable categories, categorized by weight, melting, and spark analysis. The best ML system is enhanced with three pre-trained, state-of-the-art denoising and feature extraction networks used to enhance the quality of the baseline correction and features extracted. Transfer To identify Al in real-time from 200 spectra, 24 final DL models are developed and tested using six pretrained networks. By incorporating the skills and expertise of traditional spectral analysis and the extraction of features within DL, where the model learns from the complete spectrum, end-to-end DL emphasizes the benefits of understanding and denoising spectra. Another literature [18] proposes a front-end signal processing technique for identifying metal scrap spectra via LIBS, especially for systems capable of rapid sorting of samples in motion. Using machine learning (ML) methods, LIBS can accurately and quickly classify unknown samples without complicated sample preparation steps. This shows that it has a lot of promise for real-time industrial applications. Research is being done on LIBS systems to make high-speed, moving conveyor systems for recycling metal scrap. By pre-processing, LIBScaptured spectra before using ML, stable operation is ensured in dynamic situations caused by the movement of samples with different surface contaminants and shapes. Experiments demonstrated that the proposed initial screening method can find outlier spectra with 99.0% accuracy, which is better than the 95.2% accuracy of the traditional PLS-DA scheme. The proposed two-stage BR-RMSN can effectively reduce the spacing with both stationary and relocating samples at different times, leading to a classification accuracy of more than 95.5% across all experimented metals. The paper [16] says that classification algorithms based on LIBS spectra are showing promise for post-consumer scrap. This study looks at how useful a three-way sortation system based on the LIBS system would be. Each of the three classes, Premium, Desox, and Secondary, makes up 28.3% of the whole sample. This study is based on the “ground truth” classification of the XRF. When putting pieces into ground truth classes, only copper, zinc, silicon, and manganese are taken into account. Three ways to classify data have been looked at: Logistic Regression, Random Forest, and SVM. Table 2 represents the overall visualization of the literature mentioned in the Machine Learning Models and Data Analytics section. Table 3 represents the details of the seven methods we mentioned in Table 2. The preprocessing steps and full form of the techniques are categorized here for reference mentioned in the literature [18].

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3.3

247

Sorting Efficiency (Speed and Accuracy)

The researcher [16] found that for premium, deoxidized, and secondary aluminium alloys in both clean and unclean surface conditions, Logistic Regression, Support Vector Machine, and Random Forest machine learning models attained an accuracy of 55.10% to 65.82%. The Random Forest approach outperforms the SVM classifier (65.19%) and the Logistic Regression classifier (61.39%) in terms of accuracy in classification for the clean surface condition. The research [18] employed a LIBS-based automated sorting system that was constructed in-house to collect the necessary test data. It is made up of a moving belt that travels at a speed of one m/s and has an accuracy of more than 95.5%. Study [23] experiments mimic real-world industrial settings by testing on samples transported by a conveyor belt at a rate of several meters a second; these results show that Table 2. Overview of the Machine Learning and data analytics models Article Dataset

Methods

Accuracy

[16]

834 pieces Logistic Regression

[18]

Test set 1 SVM PLS-DA ANN DNN Test set 2 SVM PLS-DA ANN DNN Test set 3 SVM PLS-DA ANN DNN

Method-2 (∼98%) Method 1,2,4,7 (∼97.25%) Method 1,5,7 (∼98%) Method 7 (∼98%) Method-1, 7(>98%) Method-1, 4,5,7(>96%) Method-1(∼98%) Method-5,7(>98%) Method-1,5,6,7(>99%) Method-5(>99%) Method-2,4(>99%) Method-7(>99%)

564 pieces LIBS/ANN

More than 75%

773 pieces UNET RESNET GHOSTNET

76% 78% 77.50%

[23] [15]

Clean-61.39% Unclean -55.10% Both-62.54% Support Vector Machine Clean-65.19%, Unclean-60.76%, Both-62.86% Random Forest Clean -65.82% Unclean-59.49% Both-62.54%

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M. A. Akram et al. Table 3. Evaluation combinations of preprocessing steps

Method Preprocessing steps

Full Form

1

BRF - L - BRS - RMSNS

F- Full Spectrum

2

BRF - RMSNF - L - BRS

L- Line Selection Mechanism

3

BRF - L - BRS

RMSN- root mean square normalization

4

RMSNF - L - BRS - RMSNS

BRF-Baseline removal of full spectrum

5

BRF - RMSNF - L - RMSNS

BRS- BR of selected spectral lines

6

RMSNF - L - RMSNS

RMSNS- RMSN (spectral lines)

7

BRF - RMSNF - L - BRS - RMSNS RMSNF- RMSN (full spectrum)

effective categorization of aluminium automobile waste more than 75% of the time is possible. Misclassification rates ranged from 9% to 15% in research [15]; this was achieved using a feeder that accelerated the pieces to 2 m per second. In study [22], the researchers compared the computational time in the spectroscopy device for five- PC (1.602 s) and ten- PC (1.773 s) full-spectrum Principle component analysis to their proposed method (0.078 s) of using selected spectral lines of significant elements as the input parameters for linear discriminant analysis (LDA), where they found 98.4%, 99.4%, and 100% accuracy, respectively. Study [21] has shown that the portable LIBS device is an effective alloy sorting tool, with a 95–100% success rate in identifying alloy classes. According to the results of the study [8], the optimal particle size range for sorting scrap using a LIBS machine is between 40 and 110 mm. Sorting accuracy for aluminium 5XXX as well as 6XXX alloy grade ranges from 83% to 98%, with a throughput of 3– 5 metric tons per hour possible depending on scrap quality and, in particular, scrap material thickness. 3.4

Opportunities Handheld LIBS

Brooks and Gaustad, 2021 evaluated the performance of the SciAps Z 200, Rigaku’s KT-100, and TSI Chemlit micro-LIBS portable analyzers in the analysis and sorting of aluminium alloys [19]. The design characteristics and performance of Rigaku’s KT-100 micro-LIBS handheld analyzer for testing and classifying of aluminium alloys are discussed in Piorek’s 2019 [21] report. This lightweight and portable LIBS analyzer can provide results for the alloy composition of a given sample in under two seconds. The instrument is a great tool for sorting alloys thanks to its high level of analytical accuracy and its advanced identification algorithm, which together yield a 95–100% success rate in identifying alloy grades. 3.5

Comparison Between XRF and LIBS (Handheld Device)

Broos and Gaustad 2021, [19] experimented with specimens each for XRF and LIBS in terms of coefficients of Variation (COV), found COVs more than 9% in

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wrought obsolete aluminium waste (WOA), with the other samples differing by between 0.01% and 4.07%; nevertheless, neither instrument average proved to be superior for lower or higher COVs.

Fig. 3. Coefficient of variance comparison between XRF and LIBS handheld device on sorting wrought obsolete alloy (WOA1, WOA2, WOA3) and cast alloy (CA1,CA2,CA3) samples (adopted from [19])

Exceptional results were found for cast aluminium samples. Samples CA1 and CA3 show the most difficult for XRF, with COVs of more than 20% across the board, the highest of all of the aluminium samples tested (including wrought). When comparing COV, Al% age by mass, and standard deviation between XRF and LIBS, sample CA6 shows the highest degree of agreement. The LIBS instruments always resulted in higher average Al% by weight compositions. The summary has been shown in Fig. 3. It may be concluded from the data that LIBS is a superior technique for classifying both wrought and cast alloys scrap. 3.6

Opportunities in Industrial Scale LIBS Sorting

Using a LIBS sensor for alloy classification, as stated by [8,15–23], would allow for the further subdividing of the waste stream into aluminium-based fractions, which might lead to the generation of further profit. Although no classifier achieves perfect accuracy [8,15–21,23] the resulting output fraction compositions are promising, especially for new surface case. In order to train and validate the machine learning model, two crucial steps in the process, which necessitate a large data set, a LIBS experiment may gather hundreds of spectra [15,16,23]. Researchers [15,23] have demonstrated the practicality of LIBS in an industrial context, notably for sorting Premium class alloys with the use of more accurate ground truth data during system training. The study [8] proves that LIBS may be successfully implemented in a manufacturing facility, particularly for the separation of Al 5xxx and Al 6xxx, both of which belong to the Superior class of alloys. Fast sorting performances using a LIBS industrial scale machine were stated in studies [8,15,23]. Studies [15] and [23] discussed throughput in regards to a feeder velocity range of 2 m/s to several m/s, while study [8] discussed throughput in terms of metric tons of scrap ranging from 3 to 5 sorted per hour. Although the alloy’s surface may be filthy and covered, the portable

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LIBS’s ability to fire “cleaning shots” enables reliable alloy detection [19,21]. A quick laser “pre-burn” of the spot can be used to prepare the surface for LIBS investigation. To get a sample ready for LIBS analysis, a pre-burn is performed by directing a laser beam at it and letting it ablate and burn away any surface impurities. When working with aluminium alloys, a pre-burn that is twice or three times as long as the analytical burn length is usually sufficient. [22] asserts that by first categorizing the whole LIBS spectrum using PCA, and then applying LDA with only the spectral lines of the most relevant components as input variables, it is possible to achieve 100% accuracy with LIBS. Table 4 provides an outline of the advantages that can be gained from utilizing LIBS technology for the sorting of aluminium alloys, which have been cited by multiple researchers. 3.7

Challenges in Industrial Implementation

Numerous research [15,16,18,23] have shown that LIBS technology has trouble making a precise alloy identification when the surface is filthy. Composition measurements may be affected by lingering surface pollution, even if the surface has been cleansed or pre-burned, as shown in studies [16,19,21]. The author [20,23] claims that because of their identical chemical compositions, several types of aluminium alloys were difficult to distinguish using LIBS. The findings of study [8] indicate that the LIBS sorting process cannot be used without first subjecting scrap to pre-treatment. The study’s results also indicated that overlapping fragments were the main cause of the misclassified materials. The study’s author [21] argues that another factor affecting the generalizability of the results is the intrinsic non-homogeneity of the specimen alloy. It is difficult to apply the study’s methods [22,23] to samples whose composition is completely unknown since they need prior knowledge of the component elements. One should expect a drop in efficiency and an increase in the amount of time it takes to identify the alloy using LIBS if a large input dataset is needed to train the LIBS machine or as increased reference data for component analysis [15,22,23]. The difficulties of implementing LIBS into practice are outlined in Table 5. Table 4. Opportunities of LIBS technology in Industrial Implementation Opportunities

Reference Articles

Separating scrap into aluminium alloys

[8, 15–23]

Sorting alloys with promising accuracy

[8, 15–21, 23]

Training and validating machine learning model

[15, 16, 23]

Sorting premium class alloy

[8, 15, 23]

High Sorting Speed

[8, 15, 23]

Alloy identification in uncleaned and coated surface [19, 21] Sorting Alloy with 100% accuracy

[22]

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3.8

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Alloy Separation Techniques After Sorting

Separating alloys after sorting is a crucial part of LIBS’s use in industry. In the paper [8], researchers spoke about using an air blow approach to separate alloys after LIBS had sorted them, by blowing out the alloys through air valves. Typically, the goal of sorting is to isolate the minority constituent. This is done to save on sorting-related resources like compressed air. The research report [17] details the design and testing of a robotic sorting system that includes a vision system, a conveyor, a SCARA robot, and a pneumatic gripper. The conveyor’s contents are mapped out in detail by the vision system, which then relays that information to a novel sequence planning algorithm. Both of the alloy separation processes have been depicted in Fig. 4 Table 5. Challenges of LIBS technology in Industrial Implementation Challenges

Reference Articles

Identification difficulty in uncleaned or filthy surface

[15, 16, 18, 23]

Identification difficulty in contaminated surface

[16, 19, 21]

Difficult to distinguish identical chemical composition in alloys [22, 23] Large input dataset increases operational time

[15, 22, 23]

Non-homogeneity of the specimen alloy

[21]

Impossible to identify completely unknown alloy

[22, 23]

Pre-treatment requirement for surface cleaning

[8]

Overlapping of scrap fragments cause misclassification

[8]

Fig. 4. Alloy separation processes after sorting with LIBS; robotic separation (left) and air blow (right) (adopted from [8, 17])

3.9

Case Study

As explained above we analyzed the value chain and practices at two Metallco companies and Benteler Automotive Raufoss. In this paper, we are restricted to presenting only a summary of the case study performed. The current business model and operations at Metallco (Aluminium material part) are state-of-the-art metal scrap sorting technologies using eddy current, XRT, and XRF to separate out the aluminum fractions that they receive from their shredders or sourced

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externally. These fractions are then partially further processed at Metallco Aluminium into casting alloys. However, the main part of the sorted aluminium is exported as Zorba quality. Zorba is classified into different qualities depending on size of the fractions and the aluminium content, typically 70–95% Aluminium. The rest fractions are other nonferrous metals. The exported Zorba qualities are mainly manually sorted within low cost countries like China and India. Using the LIBS sorting technology combined with its new sorting line capable of separating cast alloys and wrought alloys, Metallco would be able to use the wrought alloy fraction and sort it into aluminium alloy families (i.e. 5xxx, 6xxx and 7xxx series alloys) which can be sold to a significantly higher price than today’s Zorba qualities. Companies like Benteler, Hydro, Speira, and others will need a steadily higher amount of this quality of sorted PCS for their cast houses thereby introducing PCS into their final products. The automotive OEMs (Volvo, BMW, Mercedes, and others) require within the next few years a minimum of from 30 to 40% recirculated aluminium into the aluminium products they source from their suppliers. Some OEM’s state that it shall specifically originate from PCS, which will presumably be the standard within the next years. Thereby first and second-tier aluminium product suppliers to the automotive industry need to be able to source PCS of a certain quality. Benteler Automotive uses 6xxx and 7xxx series alloys for their formed aluminium extrusion (crash management systems and car body parts) [24]. Benteler manufactures their own alloys into their cast house. When introducing PCS in their cast house and alloys, Benteler needs to source PCS that is sorted at least on an alloy family level. Introducing PCS in their cast house and alloys Benteler needs to source PCS that is sorted at least on an alloy family level. A larger scale use of PCS in Benteler cast house would be possible with access to material that can be sorted with a LIBS industrial scale implementation. The Pilot LIBS machine installed at Metallco will be tested for this purpose within the project Alu Green.

4

Discussion

According to the reviewed literature (RQ1), and case study (RQ2) there is a large potential for aluminium recycling companies to develop new profitable business by developing and implement LIBS sorting technology to separate valuable wrought alloys from their shredded scrap. At the same time, LIBS are the most promising technology for aluminium component suppliers like Benteler Automotive Raufoss to get access to high-quality aluminium PCS needed for their cast houses to be able to meet future requirements on PCS content in their products. LIBS technology shows great promise due to its high degree of accuracy (up to 100%). Section 3.6 includes a discussion of several additional possibilities. Sections 3.4 and 3.5 discuss the advantages of portable, handheld LIBS over XRF devices in the scrap sorting industry. While handheld LIBS devices aren’t suitable for real-time alloy sorting, they are useful for taking samples of alloys that have already been analyzed with a larger-scale LIBS machine. In Sect. 3.7, the

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difficulties anticipated by the researchers have been outlined. The findings suggest that, despite LIBS’s promising future, some obstacles must first be overcome before the technology can stabilize. Sections 3.1, 3.2, 3.3, 3.8 and 3.9 have RQ 2 question about the potential future applications of LIBS technology in the sorting of post-consumer aluminium scrap and case study. Machine learning and data analytics model plays an important role in the LIBS machine’s ability to properly categorize metals. Artificial neural networks (ANNs), deep learning (DL), and principal component analysis (PCA) optimized with linear discriminant analysis (LDA) are the most effective models in terms of accuracy. For Industrial Scale applications, sorting rate is also an important consideration, with rates of up to 5 metric tons per hour achievable depending on scrap quality. After LIBS has sorted the alloys, they can be separated using the air blow technique or a robotic system. The case study at Benteler and Metallco combined with the review of the literature points out a significant potential of LIBS in the automated sensing and sorting technology, helping to establish a true circular aluminium value chain. The LIBS machine needs to be trained to solve the specific tasks it is targeted to solve, if only 6xxx series aluminium PCS need to be sorted it should be trained to do that using proper machine learning algorithms. As we have experienced from the leading supplier in Europe existing machines built to sort PCS on an industrial scale, are not designed to be easily programmed and trained using ML algorithms. This is due to that the sensing spectrometer unit is delivered by a third-party company. Separation techniques after sensing unit advanced blow techniques, SCARA robots as have been reported will certainly need to be further developed and optimized dependent on input materials and specific tasks to be solved. Furthermore, the cleanliness of the material input (paint and dust of the PCS) will always be a challenge given the nature of shredding and PCS operations.

5

Conclousion and Future Work

Aluminium post-consumer scrap sorting and circularity of especially wrought alloys could be greatly boosted by successfully implementing LIBS technology, but doing so is no easy task. Alloy identification in real time using LIBS spectra, conveyor speed for feeding scrap alloys, and alloy separation after successful sorting are just a few of the factors that must be optimized for large-scale LIBS machines. Based on this literature and case study we are confident that ongoing research and development will move LIBS technology forward and a more widely used technology especially to sort and reuse directly at cast houses for making wrought alloys. This will be steadily more important as we see in the automotive shift to EVs. The need for casting alloys will necessarily decline, and the need for wrought alloys will increase. Thereby the need for more powerful sorting technologies like LIBS will be a crucible in the future as the route with casting alloy needs to be shifted to the wrought alloy to be manufactured at cast houses for billets and slabs.

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Future Work: Follow up testing and research, on the industrial-scale LIBS machine installed at the company Metallco in Fredrikstad, will start during this summer. The machine is partly financed by SIVA within the Green Platform project Alugreen.

References 1. Cullen, J.M., Allwood, J.M.: Mapping the global flow of aluminum: from liquid aluminum to end-use goods. Environ. Sci. Technol. 47(7), 3057–3064 (2013) 2. Van den Eynde, S., et al.: Forecasting global aluminium flows to demonstrate the need for improved sorting and recycling methods. Waste Manag. 137, 231–240 (2022) 3. IAI Material Flow Model - 2021 update. https://international-aluminium.org/ resource/iai-material-flow-model-2021-update/. International Aluminium Institute, June 2021. Accessed 12 Nov 2022 4. Life cycle inventory data and environmental metrics for the primary aluminium industry. https://fluoridealert.org/wp-content/uploads/aluminum.life-cycle.2015. pdf. Accessed 12 Nov 2022 5. Recycle Facts Aluminum. https://www.lehighcounty.org/Departments/SolidWaste-Management/Recycling-Facts/Aluminum. Lehigh County. Accessed 22 Oct 2022 6. Aluminium recycling. https://www.hydro.com/en-NO/aluminium/aboutaluminium/aluminium-recycling. Hydro.com. Accessed 12 Nov 2022 7. Vision 2050 - Full Report, European Aluminium. https://european-aluminium. eu/. Accessed 22 Oct 2022 8. Rombach, G., Bauerschlag, N.: LIBS based sorting—a solution for automotive scrap. In: Chesonis, C. (ed.) Light Metals 2019. TMMMS, pp. 1351–1357. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05864-7 167 9. Capuzzi, S., Timelli, G.: Preparation and melting of scrap in aluminum recycling: a review. Metals 8(4), 249 (2018) 10. Kashiwakura, S., Wagatsuma, K.: Rapid sorting of stainless steels by open-air laserinduced breakdown spectroscopy with detecting chromium, nickel, and molybdenum. ISIJ Int. 55(11), 2391–2396 (2015) 11. Noll, R., et al.: Laser-induced breakdown spectroscopy expands into industrial applications. Spectrochim. Acta, Part B 93, 41–51 (2014) 12. Werheit, P., Fricke-Begemann, C., Gesing, M., Noll, R.: Fast single piece identification with a 3D scanning LIBS for aluminium cast and wrought alloys recycling. J. Anal. At. Spectrom. 26(11), 2166–2174 (2011) 13. Noll, R., Fricke-Begemann, C., Connemann, S., Meinhardt, C., Sturm, V.: LIBS analyses for industrial applications-an overview of developments from 2014 to 2018. J. Anal. At. Spectrom. 33(6), 945–956 (2018) 14. Denyer, D., Tranfield, D.: Producing a systematic review (2009) 15. D´ıaz-Romero, D.J., et al.: Real-time classification of aluminum metal scrap with laser-induced breakdown spectroscopy using deep and other machine learning approaches. Spectrochimica Acta Part B: At. Spectrosc. 196, 106519 (2022) 16. Van den Eynde, S., et al.: Assessing the efficiency of laser-induced breakdown spectroscopy (LIBS) based sorting of post-consumer aluminium scrap. Procedia CIRP 105, 278–283 (2022)

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17. Engelen, B., et al.: Techno-economic assessment of robotic sorting of aluminium scrap. Procedia CIRP 105, 152–157 (2022) 18. Kim, H., Lee, J., Srivastava, E., Shin, S., Jeong, S., Hwang, E.: Front-end signal processing for metal scrap classification using online measurements based on laserinduced breakdown spectroscopy. Spectrochim. Acta, Part B 184, 106282 (2021) 19. Brooks, L., Gaustad, G.: The potential for XRF & LIBS handheld analyzers to perform material characterization in scrap yards. J. Sustain. Metall. 7(2), 732–754 (2021) 20. Fugane, Y., Kashiwakura, S., Wagatsuma, K.: Uncertainty of the analytical values in laser-induced plasma optical emission spectrometry for element-based sorting of commercial aluminum alloys. Anal. Sci. 36(11), 1415–1421 (2020) 21. Piorek, S.: Rapid sorting of aluminum alloys with handheld µLIBS analyzer. Mater. Today: Proc. 10, 348–354 (2019) 22. Sungho, S.H.I.N., Youngmin, M.O.O.N., Jaepil, L.E.E., Hyemin, J.A.N.G., Hwang, E., Jeong, S.: Signal processing for real-time identification of similar metals by laser-induced breakdown spectroscopy. Plasma Sci. Techno. 21(3), 034011 (2018) 23. Campanella, B., et al.: Classification of wrought aluminum alloys by artificial neural networks evaluation of laser induced breakdown spectroscopy spectra from aluminum scrap samples. Spectrochim. Acta, Part B 134, 52–57 (2017) 24. Welo, T., Holthe, R.: Crash management using advanced aluminium extrusionbased solutions. AutoTechnology 6(2), 60–63 (2006)

Understanding Sustainability: Cases from the Norwegian Maritime Industry Olena Klymenko

and Lise Lillebrygfjeld Halse(B)

Molde University College, Molde, Norway {olena.klymenko,lise.l.halse}@himolde.no

Abstract. In recent years, increased attention has been paid to sustainability in manufacturing. The sustainability concept is fuzzy, however, which, according to previous studies, may hamper the changes needed to address the urgent challenges of global warming and reduced biodiversity. In this study, we aimed at investigating how companies in the maritime industry in North West Norway interpret and operationalize the sustainability concept. The study suggests that companies’ understandings and operationalizations of sustainability are linked to formal regulations, customer demand, and voluntary initiatives intended to gain legitimacy from stakeholders. Companies’ efforts to disclose information on sustainability performance have increased and supply chain sustainability initiatives are attracting attention, while the cluster organization developing a common vision of becoming a zero-emission industry. Among approaches to addressing sustainability issues, the focus on technology is dominant, which can be related to the industry-specific context. Other initiatives are largely in line with economic considerations. This study argues that more comprehensive changes are needed to address the two other dimensions of sustainability, to accelerate the transition towards sustainable manufacturing operations. Keywords: Sustainability · Manufacturing · Maritime cluster

1 Introduction Since the “Brundtland report” Our Common Future [1], the concept of sustainability has attracted increasing attention among organizations and researchers. In the last decade, the sustainability issue risen on the agenda, being greatly boosted by climate and natural scientists’ increasingly serious findings about the planet’s evolution. Organizations now face expectations that their operations will operate in line with the United Nations (UN) Sustainable Development Goals. In some areas, this manifests itself as specific requirements in standards, laws, and regulations, and it is also reflected in changed customer expectations and requirements [2]. Manufacturing operations are directly related to material and resource use, the energy used in various production processes, and the waste created. Researchers take a particular interest in investigating sustainability by focusing on reducing environmental impact and © IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 256–270, 2023. https://doi.org/10.1007/978-3-031-43688-8_19

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societal harm and inequalities related to manufacturing companies [3], for example, by considering circular economy practices and the transition from a linear to a circular system [4, 5]. In parallel with this, technology and digital solutions have becoming increasingly important means for industrial companies to automate processes, improve efficiency, and reduce cost. The concept of sustainability is a powerfully present and complex concept. Diverse interpretations and ideas are associated with this term [6], and researchers and practitioners lack clarity concerning the essence of corporate sustainability [7]. This may create difficulties in implementing sustainable operations and practices. The variety of interpretations and translations of the concept may represent a barrier to a more sustainable future. Consequently, in this paper we aim at exploring how the concept of sustainability is understood and operationalized in manufacturing organizations, which play an important role in the sustainability transformation. To shed light on this issue, we have conducted a case study of companies that are part of a Norwegian maritime cluster. We analyze interview findings, observations, and a sample of sustainability reports and official statements to evaluate how the companies and cluster organizations implement sustainability practices in operations. The rest of the paper is organized as follows. The paper opens with an overview of the theoretical concept of sustainability and of its implications for the manufacturing context, followed by a discussion of the role of regional clusters in the sustainability transition. Next we provide an overview of the research methodology and the rationale for deploying the case study research method. The case is then briefly presented, followed by an analysis of the findings. After a discussion, the conclusions and implications for the research environment and industry practitioners are summarized at the end of the paper.

2 Theory 2.1 The Sustainability Concept Johnston et al. [8] claimed that there are around 300 definitions of the term “sustainability.” According to the Brundtland report, sustainable development is defined as development that “meets the needs of the present without compromising the ability of future generations to meet their own needs” [1]. Many companies have chosen to select some of the 17 sustainability areas identified by the UN and to measure and report on related parameters. The division of the sustainability concept into three pillars, namely, the economic, social, and environmental pillars, has become widespread [9]. These pillars represent a “triple bottom line” that is explicitly embedded in the UN Sustainable Development Goals (SDGs). The underlying idea is that companies should operate within the three dimensions of sustainability, i.e., the economic, social, and environmental dimensions. The triple bottom line assumes that it is possible to uphold all three dimensions simultaneously. However, it has been shown that environmental and social sustainability can conflict with achieving economic profit [10]. There are many interpretations of the “sustainability” concept [3], “seeing sustainability as an environmental initiative; as a goal or a process; as an integration of different aspects; or as a compromise between pillars, etc.” (p. 745). According to Ihlen and Roper [11], the world’s largest corporations

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“make no attempt to explicitly define the sustainability concept” (p. 48), supporting the claim that they are pursuing sustainability with unclear strategies [12]. 2.2 Sustainability in Manufacturing Manufacturing plays an important role in achieving a more sustainable society. Sustainability in manufacturing has been an issue for the last 25 years [13]. Despite this longstanding interest, there seems to be no common definition of sustainable manufacturing among scholars [3], and no unified understanding of the concept [14]. Moldavska and Welo [3] have analyzed the different definitions of sustainable manufacturing (SM) and found inconsistency in the understanding of issues associated with the SM concept. They found that 189 articles included an explicit definition of “sustainable manufacturing,” and identified 89 original definitions of the concept. Sixty-three percent of the articles cited the definition proposed by the U.S. Department of Commerce in 2008, i.e., “the creation of manufactured products that use processes that minimize negative environmental impacts, conserve energy and natural resources, are safe for employees, communities, and consumers and are economically sound” [15]. More recently, Machado et al. [16] defined “sustainable manufacturing” as “the integration of processes and systems capable to produce high quality products and services using less and more sustainable resources (energy and materials), being safer to employees, customers and communities surrounding and being able to mitigate environmental and social impacts throughout its whole lifecycle” (p. 1464). According to Sartal et al. [6], sustainability in the manufacturing context involves “the transformation of resources into economically valuable goods by operating socially and environmentally responsible processes” (p. 2). By reducing the use of material resources and focusing on waste management, manufacturers have been more proactive in improving the environmental performance of their processes [17]. The diffusion of the “lean manufacturing” philosophy has contributed to this development, since “lean management” aims to “use less of everything” [18]. In addition, consumers’ concern about the social and environmental impacts of manufacturing has put pressure on manufacturers to change the current industrial growth model. Several literature reviews have shed light on how sustainability in manufacturing is understood and operationalized. As expected, the three social, economic, and environmental pillars of sustainability are the most common aspects of the analysis [19]. Of these, the environmental dimension has been targeted the most. Of the papers studied by Eslami et al. [19], 94% referred to this dimension alone or alongside the other two. In the environmental dimension, the topics examined were emissions, pollution, resource consumption, and biodiversity, with the subdimensions of water, materials, carbon footprint, emissions, waste, landfill, transport, resources, and energy [19]. Bhatt et al. [13] conducted a bibliometric and content analysis of the sustainable manufacturing literature, and found that most empirical work focuses on the relationship of lean and green practices with organizational and environmental performance. Interestingly, they also found that the role and criticality of sustainability are underrepresented in this literature. They called for the integration of sustainability principles, such as the SDGs, the circular economy, lifecycle engineering, and corporate sustainability assessment, with sustainable manufacturing research.

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2.3 Regional Clusters and Sustainability In many countries, cluster policy has been considered an important tool for economic development [20] and recently also for the transformation of industries towards sustainability [21]. Clusters are defined as “geographic concentrations of interconnected companies and institutions in a particular field” [22]. Clusters encompass arrays of linked industries such as manufacturers, specialized suppliers and customers, and governmental and other institutions such as universities and cluster organizations. Cluster organizations are established intentionally and strategically, and funded by public cluster programs [23]. The main advantage of being in a cluster, according to Porter [22], is increased competitiveness compared with companies outside clusters. This competitiveness has its origins in the sharing of information and knowledge, leading to increased innovation capacity. Geographical proximity facilitates the development of trusting longterm relations [24], exchange of knowledge, mutual learning and cooperation [25], joint problem-solving, and the co-creation of value [26]. Moreover, organizational, cognitive, social, and institutional proximities reduce uncertainty and enhance interactive learning and innovation [27]. Clusters are seen as playing an important role in development towards sustainability, as this transformation requires innovative processes and products that are facilitated by the cluster environment [28]. Research in this field, however, is still in an early phase, as there has been little focus on how cluster policy can guide development in a more sustainable direction [29]. It is vital to have a shared understanding of the concept of sustainability to act on the urgent need to address the challenges associated with global warming and biodiversity loss. Consequently, in this study we will explore how cluster companies understand the sustainability concept and how they are meeting the increased expectations and requirements for more sustainable operations.

3 Methodology As mentioned above, there is limited theoretical understanding of how manufacturing companies understand sustainability. Considering the nature of the research question, we chose a qualitative case study approach [30, 31] including multiple cases [32] that are part of a regional cluster. We opted for an approach in which the sustainability aspects would emerge from the analysis, and through interpretation of the respondents’ statements [33]. The approach was abductive, with the data facilitating dialogue between the theoretical framework and the empirical domain [34]. Data were collected through semi-structured interviews with managers in the cluster organization and in four case companies operating in the maritime cluster in North West Norway (Table 1). In total, nine interviews were conducted with managers responsible for supply chain operations, i.e., managing, administrative, and technical directors. Each interview was based on an interview guide with a predetermined set of questions and the opportunity to ask additional questions during the interview process. The interview questions focused on topics regarding the essence of the sustainability concept for each company, the underlying motivations for sustainability actions, the implementation of sustainability practices in operations, the role of cluster membership, and the cluster

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environment during the sustainability transition. The interview questions for the nonprofit cluster organization concerned sustainability activities at the cluster level. The interviews lasted approximately 1–2 h. After obtaining the interviewees’ permission, all interviews were audio recorded and later transcribed, and field notes were made during and immediately after the interviews were conducted. Both authors were present during the interviews. Table 1. Data collected for each case company/organization. Company

Specialization

Source of data

Company A

Shipyard, ship design

- two interviews - sustainability reporting in annual report - company website

Company B

Shipyard

- two interviews - company website, including brief information on sustainability work presented there - other secondary data sources

Company C

Shipyard, ship design

- one interview - environmental report - sustainability report - supplier code of ethics - company website

Company D

Equipment manufacturer

- two interviews - sustainability report - supplier code of conduct - workshops, presentations - company website

Organization E

Cluster organization, maritime

- two interviews - annual maritime cluster conferences - maritime cluster analyses

Additional data were collected in workshops and meetings with regional companies, such as annual maritime cluster conferences, webinars, and courses organized for local companies. Observations performed during the conference visits provided insight into the status of the companies and the issues they experienced. During the conferences we made field notes. Part of the information presented during annual maritime conferences is published in the cluster analysis reports. During the conferences we obtained additional information, details, and responses from the companies in Q&A sessions. In addition to the interviews, we screened the websites of the companies and investigated their sustainability reports and statements. Content analysis of sustainability and annual reports has been a dominant research approach used by prior studies in this field [35]. We read and analyzed three sustainability reports as well as other available and relevant

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reporting documents, such as ethics guidelines, supplier codes of conduct, one strategic document presenting the sustainability strategy for the maritime cluster, and one company’s brief description of its sustainability commitment published on its website. The reports represent additional sources of data supplementing the interviews, allowing for comparison between managerial perceptions of sustainability and the actual operationalization of sustainability, and between how companies define sustainability in their reports and in public statements. In addition, we supplemented the above data with other materials, including archival data, presentations, internal reports, press releases, annual reports, and articles from the Norwegian business press. The data collection aimed at revealing how the companies regarded sustainability and related concepts, their strategy for meeting the sustainability challenge and opportunity, and their thoughts about how far they had come in implementing sustainability-related concepts internally in their organizations. Table 1 shows the types of data collected for each case company/organization belonging to the maritime cluster in North West Norway. Two researchers were involved in interpreting, analyzing, and summarizing the findings based on the data collected, supported by the interview questionnaire, transcribed interviews, and field notes. We used NVivo software in organizing transcripts into categories for the careful and comprehensive interpretation of the findings. Transcribed interviews were transferred to NVivo and then restructured into subtopics related to definitions of sustainability, motivations, sustainability practices, and the role of the cluster and the cluster environment, all supported by relevant quotations from the interviews. In addition, we imported the most relevant data found in secondary sources, involving various reports, media coverage, and public information, into NVivo. Afterwards, the categorized subtopics were screened and revised.

4 Findings 4.1 The Maritime Cluster The maritime cluster is based in Møre and Romsdal County in North West Norway. The cluster has had a long history, with fisheries having been important for the earlier development of the cluster [36]. Since the discovery of oil and gas on the Norwegian continental shelf in the late 1970s, the cluster has been part of the Norwegian offshore service sector, with companies constructing custom-made advanced vessels for this sector. The maritime cluster, named the GCE Blue Maritime Cluster, has gained a leading position in the international market. The cluster has achieved the status of a global center of expertise in the Norwegian cluster program and includes 213 firms. The cluster’s activities are coordinated by a nonprofit actor, the cluster organization GCE Blue Maritime, which performs the important function of coordinating various activities and promoting joint targets in the cluster. The organization facilitates collaboration and innovation and organizes activities to boost local competence and increase the industry’s competitiveness in the international arena. Since the oil price decline in 2014, the cluster has become more fragmented, as its companies have been delivering vessels and equipment to several market segments, such as offshore supply, offshore wind, aquaculture, oil and gas exploration, and the cruise ship, ferry, yacht, and fishing vessel segments [37]. Since 2014, the cluster’s activity in oil and gas has more than halved [37].

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In 2021, the cluster administration took a strategic step in accordance with the Paris Agreement’s goal and with ambitions to be part of the solution to the climate challenges articulated in the New Blue Deal. Their ambition is to be the world’s first zero-emission maritime cluster, with the goal of designing, constructing, equipping, and operating zeroemission vessels [38]. Moreover, this development will be based on segments in which Norway already has competence. This encompasses sea-based energy (e.g., offshore wind and petroleum), fish farming and fisheries, and marine-based tourism (e.g., cruise ships, ferries, and speed boats). In a recent statement, the cluster organization emphasized that the New Blue Deal is not just “nice words” but will result in concrete projects. Despite these ambitions, there are still few zero emission vessels in the Norwegian-owned fleet. However, there has been an increase in deliveries of low-emission vessels, particularly to the fish farming segment [39]. The green transition of global shipping is seen as a market opportunity, and a means for increased economic growth and employment. The projects organized by the cluster organizations indicate how this organization is operationalizing the concept of sustainability. According to the cluster organization’s website, the cluster is running seven projects, three of which address issues related to energy consumption and emissions. One of these entailed mapping the energy consumption in vessels, which was conducted in 2022. The project faced challenges due to participants’ reluctance to share data with one another. The project detected the lack of a comprehensive factual basis for the relationship between energy for propulsion, operations, and hotel operations. Notably, the project was also intended to include vessels for the offshore sector. Another project related to the energy issue was the pre-study, “Nuclear Energy in Ships,” while another pre-study was initiated addressing an autonomous containerized refueling barge for cruise vessels. The cluster’s strategy still covers vessels for the petroleum sector, particularly vessels having global potential. One representative of the cluster organization highlights the need to achieve profitability from sustainability initiatives: Sustainability is a very big topic, there can be so many ways to be sustainable. For companies it is natural to be sustainable in what they do in economic terms and then work towards achieving climate and other sustainability goals. However, that does not happen in a business without being sustainable financially. The cluster organization has stated that one of its goals is to find customers that will buy emission-free vessels, considering the collaborative help of Norwegian and EU policy instruments. However, it is unclear how this goal is to be operationalized. The findings from the cluster organization indicate that the social and economic dimensions of sustainability have received the most attention. The social dimension of sustainability has been addressed through customer requirements. In general, Norway is considered to have decent work conditions and social norms, and low corruption compared with some developing countries. However, there has recently been media coverage of low wages for foreign workers, and long working days without days off were reported in a local shipyard [40]. Moreover, several cluster companies have foreign suppliers and have outsourced parts of their manufacturing to low-cost locations [41]. The working conditions at these manufacturing sites are regulated through formal requirements (i.e., “codes of conduct”).

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4.2 The Case Companies The study includes four companies in the maritime cluster (Table 1): three shipbuilding companies (companies A, B, and C) and one equipment supplier (Company D). Shipyards A and C specialize in both shipbuilding and vessel design. Company A consists of several subsidiaries active in ship design and solutions, shipbuilding, global sales, and shipping. The company is represented in four countries and has its headquarters in Norway. We screened the sustainability section of Company A’s website, but found no statements about how the company defines sustainability in its sustainability report, which is integral to the annual financial report. The company describes the key sustainability reporting areas, i.e., human rights, labor, the environment, and anti-corruption efforts. Furthermore, the company prioritizes commitments in accordance with regulations and requirements, and then highlights measures associated with environmental performance, such as the development of environmentally friendly products, ensuring energy efficiency, and the appropriate recycling of materials. The respondent from this company emphasized that they prioritize economic performance and profitability as factors that can ensure ongoing business operations, and then commit to sustainability: “Sustainability for us is maintaining an industrial company that manages to make money.” In the maritime industry there has been an increased focus on alternative energy sources and carriers that do not cause environmental damage, while being used by a wide range of actors. Company A has been particularly active in this respect and, according to the respondent, has been looking at five different fuel alternatives, namely, ammonia, methanol, batteries rechargable with land-based electricity, nuclear power, and hydrogen. Similarly, the company’s vision of sustainability centers on cleaner energy sources and technological solutions, as stressed by the statement of the interviewee: Sustainability is how we make operations smarter, meaning how we conduct the operations using fewer resources, polluting less and at a lower cost. Company B is a multipurpose shipyard whose main specialization relates to the repair, maintenance, and modification of ships. Some years ago, the yard’s main activity was shipbuilding. According to the respondent from this company, the company embraces sustainability values and provides reporting in terms of the 17 UN SDGs, focusing on environmental and social commitments. During the interview, the respondent mentioned the extended focus on the UN SDGs as follows: Sustainability is a very broad term. It is linked to the UN’s sustainability goals, there are 17 SDGs. We focus on both the environmental and social aspects. The company is reportedly the first Norwegian shipyard to, in 1999, prepare and publish an environmental report, and it has been performing environmental reporting in accordance with the 17 UN SDGs since 2019. However, the company does not publish sustainability or environmental reports on its website. The website offers only a brief statement related to sustainability work, such as the company’s apprenticeship program, health security and environment (HSE) measures, environmental policy, and its achievement of reducing environmental emissions through offering a shore supply of

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electric power for customers. The company stated that it conducts environmental performance measurements and has an internal control system and established procedures for registration and improvement measures. Company C is a designer and builder of high-quality specialized vessels. Its latest sustainability reports (for 2021 and 2022) are integrated into the sustainability report of the parent company, which owns several subsidiaries in different countries. The goal stated in the report is ambitious and concerns multiple stakeholders, such as shareholders, partners, suppliers, subcontractors, and local communities in the countries where the company is represented, that are not explicitly mentioned in the report. In the report, the company dedicates considerable attention to describing practices for ensuring employee safety. The report emphasizes the environmental impacts of its shipyards located in Norway, Romania, and Vietnam. This is also emphasized by the report’s title, Vision zero, environment report. Ethical guidelines are available on the website as a separate document that consists of detailed description of priorities and standards in accordance with the International Labor Organization and fundamental human rights. The company provides a suppliers’ code of ethics that sets the aim of developing responsible and sustainable supply chains and specifies expectations and criteria for suppliers with respect to ethical standards, human rights, and environmental compliance. According to the report, the Norwegian subsidiary works with approved suppliers that undergo a qualification system. A performance evaluation system is used for monitoring suppliers. The 2022 sustainability report is largely dedicated to describing the corporate group’s sustainability strategy and the main objectives of the sustainability plan, including a emphasis on environmental issues, followed by social issues and the contribution to industrial development. According to the interview findings, we can see that the term “sustainability” is associated with different aspects: economic sustainability is mentioned several times, and sustainability is related to the “smart” use of resources. It seems that managers recognize the importance and urgency of sustainability; at the same time, economic thinking prevails over climate and environmental action, as managers argue for the importance of economic profitability. Sustainability is often linked to goals selected from the 17 UN SDGs by some respondents, while for others it is associated with HSE measures. Recent reporting in accordance with the Norwegian Transparency Act resulted in preliminary operationalization attempts by each of the four case companies, as the Act must be adhered to starting from June 2023. Company D designs, manufactures, and supports systems for the propulsion, positioning, and maneuvering of vessels, and delivers products and services to customers inside and outside the maritime cluster, mainly in Europe. The company’s manufacturing facilities are situated in the cluster. In the company’s 2022 report, there is no explicit definition of sustainability. However, the company published a comprehensive sustainability report that includes information on both environmental performance and contributions to social and ethical dimensions. In addition, the company provides a Supplier Code of Conduct and Ethical Guidelines document. The company’s sustainability strategy and road map are presented in the sustainability report, indicating the company’s goals for achieving net-zero emissions by 2050. From the sustainability report, it seems that the company is emphasizing a “greener economy” when addressing sustainability reporting. Furthermore, the economic component of the sustainability strategy was

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highlighted by the interviewees, who claimed that “an important part of sustainability is being economically sustainable.” The Company D manager expressed the importance of economic considerations when it comes to being a sustainable manufacturer. Another interviewee from the same company emphasized two main factors that facilitate the current strategy. The first is the strategy of producing in Norway, which has been part of the competitive advantage of the company and its trustworthiness built over many years of experience. The second factor is the green transition, which shapes strategy and operations. Hence, the company aims at utilizing in-house competence and technology to retain its position in delivering green products to global customers. The manufacturing operations of Company D involve high material and energy inputs and considerable waste production. The company states that it applies a lifecycle approach in order to improve resource efficiency, reduce emissions and waste, and improve the logistics of the product for customers. Producing in house in a high-cost Norwegian environment depends on the use of modern production technology and innovation, and the company is continuously working on improving productivity. Although the topic of recycling and reuse has been discussed for a while by the company as a potential strategic focus, it has not yet been operationalized and commercialized. The company summarizes its current state of sustainability performance by stating that it recognizes that both positive and negative impacts can be transformed into opportunities to achieve a net zero economy. The company explicitly refers to the regulations that require businesses to quantify and report on material environment, social, and governance (ESG) topics, starting in 2021. In its report, the company presents the results of Global Reporting Initiative (GRI) reporting for 2022. In addition to the environmental measures, the company focuses on its contributions to the local community and employees. 4.3 Discussion The findings show that the cluster companies have raised the issue of sustainability high on their strategic agendas. Recently, the companies have started to incorporate sustainability into strategy and operations, as illustrated by their latest reporting on sustainability and the way they communicate sustainability on their websites. We found that the case companies address sustainability using two main approaches: they develop products that will be more sustainable during operation, and they focus on resource use and sustainability in their manufacturing operations. Regarding the first, the maritime cluster companies are known for being oriented towards individual customers and developing ships according to customer requirements. This is supported by the present findings, which indicate that some of the cluster companies are preparing to respond to increased sustainability requirements from customers. This is an issue regarding the development of new vessel designs, energy sources, and propulsion systems. Furthermore, the companies have developed advanced technology used for monitoring operations and controlling emissions, and for storing data that can be shared with suppliers on demand. The success of these projects is dependent on market developments in this industry. In the second approach, companies develop internal sustainability practices, follow certification programs for environment and management systems, and set up sustainability accounting systems and reporting. In-house practices can be stimulated by national

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and industry-specific sustainability regulations. Our findings show that the case companies are at different levels regarding sustainability reporting. The sustainability report of Company D clearly connects the sustainability strategy to specific indicators and goals. Furthermore, companies C and D emphasize the supply chain level to establish common sustainability-oriented values in all supply chain actors. Most companies provide sustainability statements and list the contact information for the person responsible for sustainability, according to the requirements of the Norwegian Transparency Act. The SDG framework is encouraged by the UN but is voluntary. All the case companies specified that they engaged in work targeting selected SDGs, although they do not report on SDGs. This is in line with the study of Heras-Saizarbitoria, Urbieta [42] showing that SDGs are addressed at a general level without representing actual indicators. Due to the absence of standard reporting criteria, companies provide individually developed sustainability reports. Our findings show that regulations can simplify companies’ sustainability efforts by providing specific requirements and stating how they should be reported. Lack of reporting experience and rigor can be resolved by strengthening the standardization of reporting practices [35]. The findings show that the cluster organization aims at establishing a common sustainability-oriented vision and strategy in the cluster, facilitating the diffusion of sustainability knowledge and holding networking events at the cluster, national, and international levels. In this way, the organization helps create links among local actors with the necessary competence. The cluster organization has no mandate to interfere with the companies’ strategies and operations but helps push the companies in a more sustainable direction. In 2022, Hessevik [23] showed that cluster membership does not have a major effect in terms of strengthening environmental strategy. However, disseminating information about trends and new technologies can lead to incremental changes in energy efficiency. The present authors argue that the information transferred by the cluster organization was rather general in character and not adapted to the individual members’ needs. Our findings may indicate an underlying tension between the public sustainability reports/statements and the interview findings. Several respondents emphasized that profit was the highest priority. This means that economic sustainability is a prerequisite for pursuing an environmentally sustainable path. Ideally, these two dimensions of sustainability—economic and environmental—should overlap [43], which in this industry requires a market that demands sustainable products. Consequently, managers and shareholders concerned with business-case thinking might focus on selected environmental goals that do not harm financial performance [4, 44]. As the concept of sustainability is inherently fuzzy, it is to some degree up to the companies to develop content and operationalize the concept. Furthermore, there is a risk that the general level and multiple interpretations of the sustainability concept may hinder the ongoing implementation of sustainability practices, as pointed out in previous studies [3, 45]. With no clear definition of sustainability, companies can link previous achievements and practices to sustainability because doing so is ethically convenient Ihlen and Roper [11]. Moreover, companies need to gain legitimacy in the eyes of other stakeholders. The present findings indicate that cluster companies have increasingly become aware of the importance of communicating their positive intentions regarding

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sustainability to business partners and the public, mainly through public statements and sustainability reports. Our findings show that when operationalizing sustainability-oriented goals, the companies and the cluster organization gave a privileged role to technological solutions and digitalization. Similar findings were reported by Sjøtun [46], who described engineers as technology optimists who primarily believe in novel technology that can enable the “green” maritime transformation of the Southwestern Norwegian maritime industry. This is also illustrated by the maritime cluster organization’s dominant focus on innovative solutions to reduce environmental impact and ensure profitability, with less emphasis on the social dimension of sustainability.

5 Conclusion In this study, we have investigated what the sustainability concept means for manufacturing companies in the maritime cluster in North West Norway, and how they have operationalized the concept [50]. In contrast to a few years ago, this industry has strengthened its sustainability strategy. The cluster organization has launched a sustainability strategy, called the New Blue Deal, aiming at becoming the first zero-emission maritime cluster in the world. Similarly, some cluster companies have started to prepare and publish sustainability reports, and there is an increasing tendency to disseminate sustainability-oriented values at the supply chain level. Our findings suggest that limited attempts have been made to interpret and operationalize the whole sustainability concept, resulting in a relatively narrow focus on single dimensions of sustainability. Sustainability practices applied in in-house manufacturing operations are largely motivated by mandatory regulations and followed by voluntary actions in the form of implementing environmental management systems. In this way, the companies may be gaining legitimacy from stakeholders. Product sustainability often seems to be driven by individual customer requests and market demand. The prioritization of economic considerations may lead to minor and incremental improvements in environmental impacts, while more fundamental changes are necessary for the transformation towards sustainability. The study contributes to theory and practice by extending our understanding of manufacturing companies’ interpretation of the sustainability concept and its operationalization. Furthermore, the study illustrates that the incorporation of the environmental and social dimensions of sustainability into firms’ practices is a demanding process in terms of resources and competence, requiring the involvement of various organizational units with a long-term focus. Participation in the events and projects organized by the cluster organization is an opportunity for companies to gain new knowledge and competence in different relevant topics and adjust their sustainability strategy. Furthermore, the study identifies a lack of reporting standards, meaning that policy measures should focus on standardization practices to make sustainability reports more reliable and useful for practitioners. Findings are focused on one context of Norwegian companies that belong to the maritime cluster. It would require future research to investigate multiple case studies of other industries and clusters. Future research should explore how companies’ interpretations

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and operationalizations of sustainability practices change over time through conducting longitudinal studies. Quantitative studies using larger samples and datasets regarding perceptions of sustainability and sustainability reporting practices would extend the research.

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Smart Manufacturing to Support Circular Economy

Assessing the Interplay Between Circular Economy, Industry 4.0, and Lean Production: A Bibliometric Review Violetta Giada Cannas, Riccardo Fabris, Rossella Pozzi(B) , Matteo Ridella, Nicolò Saporiti, and Andrea Urbinati School of Industrial Engineering, Università Carlo Cattaneo – LIUC, Castellanza, Italy [email protected]

Abstract. The combination of the industrial paradigms of Circular Economy (CE), Industry 4.0 (I40), and Lean Production (LP) has been debated by academics and practitioners in the last years, demonstrating that I40 technologies and LP enable CE, and that I40 and LP mutually support each other. The analyses conclude that several economic and environmental benefits can be achieved from these synergies. However, given most of the studies in literature focused on the dual combination between these paradigms, there is a need for understanding how all three are related to each other simultaneously. Accordingly, the proposed research defines a model that shows how the circular transition of manufacturing companies can be enhanced through the exploitation of LP practices and the key enabling technologies of I40. To achieve this result, the proposed research conducts a bibliometric review of the literature extracted from Scopus, exploiting a systematic literature network analysis methodology to detect and then analyze clusters of themes. The study observed that employing LP practices and I40 technologies support manufacturing companies towards a more effective circular transition and proposes future research avenues to be addressed by future studies at the intersection between the topics of I40, LP, and CE. Keywords: Circular Economy · Lean Production · Industry 4.0 · literature review · bibliometric analyses · co-occurrence network

1 Introduction The three industrial paradigms of Circular Economy (CE), Industry 4.0 (I40), and Lean Production (LP) have been largely debated by academics and practitioners in the last years. CE focuses on minimizing the consumption of finite resources and decoupling their economic growth, by leveraging the intelligent design of materials, products, and systems, new ways of value creation, revenue generation, cost reduction, resiliency, and legitimacy [1]. I40 involves the digitalization of manufacturing processes and the digital transformation of the industrial value chains, by implementing digital technologies such as the Internet of Things (IoT), big data, cloud computing, and digital twin, © IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 273–287, 2023. https://doi.org/10.1007/978-3-031-43688-8_20

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and enabling the fusion between the physical and virtual world [2, 3]. LP represents a knowledge-driven and customer-focused industrial paradigm, pioneered by the automotive manufacturing company Toyota, that aims to eliminate unnecessary activities and optimize activities that add value to companies’ operations, aiming to create value [4, 5]. Even if CE, I40, and LP have been historically considered as three independent fields, they have more recently been explored in dual combination by several literature studies. The combination of I40 and CE has been particularly debated by recent studies. I40 technologies have been demonstrated to have a positive relationship with CE capabilities and enable the transition of companies from linear to circular, by reducing resource consumption and carbon emission in manufacturing processes and improving reverse logistics [6–8]. For example, literature has shown the positive role of additive manufacturing in a circular design, blockchain in collaborations, and IoT sensors in product lifecycle management and reverse flows [9]. The nexus between CE and I40 is also the base of the current fifth industrial revolution, i.e., Industry 5.0 (I50), which leverages I40 technologies to facilitate intelligent manufacturing and achieve sustainability via CE [10, 11]. Additionally, the dual relationship between I40 e LP represents another largely debated topic in literature. Recent studies discussed how these two paradigms, which share the objectives of productivity and flexibility, mutually support each other [12, 13]. LP generates opportunities for I40 development, whereas I40 leads to the achievement of self-efficacy and the best version of LP [14]. Finally, scarce literature addressed the combination of LP and CE, but the narrow set of recent studies that focused at the intersections between these two research topics underlined the importance of combining the two approaches to achieve economic and environmental benefits since LP could increase the efficiency of all circular flows [15]. As far as the synergy between the triad CE, I40, and LP is concerned, [16] conducted a conceptual study discussing how this synergy can help companies to reduce waste, increase cost efficiency, productivity and flexibility, reduce lead times and achieve high customer satisfaction. They concluded that both scientific and industrial sectors must focus on finding optimal strategies to enhance the integration of I40, LP, and CE and should analyze the consequent beneficial environmental and social effects [16]. However, to the best of the authors’ knowledge, only one empirical study is available on this topic, which was conducted by [17]. In their study, the authors performed a questionnaire-based survey to investigate the integrated impact of the triad CE, I40, and LP on the sustainability performance of organizations in Saudi Arabia and acknowledged the important role simultaneously played by CE principles, I40 technologies, and LP tools to achieve sustainability [17]. Additionally, a limited set of studies addressed the synergy of CE and I40 with Lean Six Sigma (LSS), which combines the LP approach with the Six Sigma approach. [18] conducted a literature review and proposed a framework for this integration in the manufacturing context, whereas [19] focused on Indian manufacturing companies and conducted a DEMATEL study on the topic. They concluded that some LSS enablers prevent wasteful activities and ensure better usage of materials, making it possible to also achieve the objectives of CE, and these objectives can be further improved through strengthening I40 [19]. Notwithstanding LSS and LP share a continuous improvement approach and the goal of achieving quality by reducing variation, they

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differ because LSS follows a structured problem-solving method known as ‘define, measure, analyze, improve, control’ (DMAIC) and addresses quality management through statistical methods [20]. In conclusion, even if the literature acknowledged a positive effect generated by the joint implementation of lean practices, digital technologies, and circular models, so far, few studies addressed their interplay and there is an evident need for further effort in management research. The proposed study aims to further investigate the relationship between these topics and to answer the following research questions (RQs): RQ1: How does the existing literature interpret the interplay between Industry 4.0, Lean Production, and Circular Economy?; RQ2: What are the potential future research directions concerning the interplay between Industry 4.0, Lean Production, and Circular Economy? To achieve this result, the proposed research conducts a bibliometric review of the literature to define a theoretical framework that explains the relationship between the three investigated topics, building a basis for future empirical research. The findings of this study can have positive implications for theory and practice. They make researchers reflect on a relevant research gap, stimulate future research directions, and provide practitioners with guidance on the interplay between CE, I40, and LP. Managers can especially benefit from our study, as they can be aware of how they can exploit I40 technologies and LP practices to advance to a more CE in manufacturing companies.

2 Materials and Method 2.1 Materials The papers analyzed in this study are collected from Scopus, the database with the largest scientific journal coverage [21]. A filter is used to find the documents having the search terms in the title, abstract, and keyword to ensure that a study related to the proposed subject, i.e., the interplay of CE, I40 and LP, is not excluded. Regarding the keywords used to represent the three areas, many sets of inclusion criteria for the search were considered, in line with the scope of the research. Regarding CE, this study considers the keyword ‘circular economy’ and all the commonly agreed definitions of this paradigm, as proposed by Ellen MacArthur Foundation (2015) [22], complemented by complementary studies addressing the topic from a sustainable manufacturing perspective [23], such as those dealing with the 6R (reduce, reuse, recycle, recover, redesign, remanufacturing) framework. Given that the CE not only concerns the redesign of products and the redesign of processes, but also their impact on the environment and in terms of waste production, additional keywords have been considered to comprise works devoted to this research area [23]. Therefore, keywords such as ‘design for the environment’ and ‘design out of waste’ were considered to investigate contributions to the topic involving the particular focus of environment and waste reduction. Finally, given that the transition towards a CE requires extended producer responsibility, to prolong the value of products and extend their lifetime, therefore the transition from product ownership to its use (servitization), additional keywords related with CE practices supporting this objective have been considered, as ‘maintenance’ ‘end of waste’, ‘waste management’, ‘take back’, ‘product service systems’.

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Regarding I40, this study considers the keyword ‘industry 4.0’ and all the synonymous used by recent studies on the topic. These comprise ‘digital transformation’, ‘digital factory’, ‘smart manufacturing’, and ‘smart factory’ [2, 3, 24]. Also, the keywords include all the technological pillars considered by recent studies as building blocks and enablers of the I40 paradigm and their synonymous and/or acronyms [2, 3, 24–31], i.e., ‘big data’ and ‘data analytics’, ‘cybersecurity’, ‘cyber-physical system’, ‘cloud computing’ and ‘cloud manufacturing’, ‘Internet of Things’ and ‘Internet of Humans’, ‘additive manufacturing’, ‘augmented reality’ and ‘virtual reality’, ‘autonomous robots’, ‘simulation’, ‘vertical integration’ and ‘horizontal integration’, ‘manufacturing execution system’, ‘artificial intelligence’ and ‘machine learning’, ‘digital twin’ and ‘block chain’. Finally, the search process includes the keyword ‘industry 5.0’, a recent emerging concept in literature, which complements I40, aiming to solve the human-machine frictions that emerged from I40 and shifting the focus on a sustainable, human-centric, and resilient smart industry [32, 33]. Regarding LP, this study considers the keywords used as synonymous of ‘lean production’ by [34]. These comprise the origin of the paradigm, i.e., the ‘Toyota Production System’, its pillars, i.e., ‘Just-in-Time’ and ‘Total Quality Management’, and the related concept of ‘Six Sigma’. Moreover, as ‘lean’ is a broad term, used in many contexts different from production, the authors provided a list of areas of research that are close to LP, but are typically identified with the synonymous ‘lean thinking’, ‘lean manufacturing’, ‘lean management’, ‘lean supply chain’, ‘lean product development’ and ‘lean enterprise’. As the topics are broad and the focus of this study is on the management of manufacturing systems, the research has been refined by limiting the subject area to “Business, Management, and Accounting”. An additional filter on the publication year was applied, as the search outcome before 2011 cannot be related to the concept of I40, which traces back to this year [35]. Hence, the search was restricted to papers published from 2011. Considering the sources of the documents, due to the novelty of I40, no limitation is applied, and all sources are considered. Figure 1 reports the search query.

Fig. 1. Scopus query for the collection of papers.

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2.2 Method To provide a complete overview of scientific literature on the interplay between CE, I40, and LP, the method used in this work is the bibliographic network analysis, which exploits the Systematic Literature Network Analysis methodology, developed by [36], which makes it possible to examine a high number of documents and study the relationships between them through keywords analysis. The keywords’ co-occurrence network analysis supports the study of the topics, tools, and main implementation areas at the intersection between CE, I40, and LP. The analysis conducted by this study is based on the approach developed by [37] and consists in the mapping of a bibliometric network and in the development of clusters, obtained through of the use of VOSviewer, an open software for the implementation of this approach. In this study, the network is composed by the keywords that Authors attribute to the document to describe its content, i.e. author keywords, the map is the representation of the network, in which the nodes, i.e., the keywords, have various dimensions according to their occurrence weight, links are between keywords that have been used at least twice together, and clusters represent group of keywords used most frequently together. The nodes in the network are marked by different colors reflecting how they belong to the different clusters. The analysis implies the investigation of the relationships between keywords that are linked together within clusters. By means of the review of the articles extracted from Scopus, the content of the clusters describes the relevant works and research trajectories on the subjects.

3 Results 3.1 Documents Analysis In March 2023, the described search yielded 108 results (Scopus file available here), from 2011, increasing over time, confirming researchers’ interest in the interplay between CE, I40, and LP. Considering the sources of the documents, the Proceedings of the International Conference on Industrial Engineering and Operations Management represent the main contributor. Considering the articles published in journal sources, the majority are from the International Journal of Production Research (IJPR) and the Journal of Cleaner Production (JCP). These results confirm that, on the one hand, the studies on these three topics are recent and not yet established as the topics they ground on and, on the other hand, that the journals that are traditionally devoted to the research areas of LP, i.e. the IJPR [34], and of both CE and I40, i.e., the JCP and IJPR [38], the journals are now considering the combination of these areas. Regarding the countries that are conducting research at the intersection between CE, I40, and LP, United States is leader, followed by Italy. 3.2 Keywords Clusters Analysis Setting 2 as the minimum number of keyword occurrences and 6 as minimum cluster size, the network depicted in Fig. 2 is the VOSviewer outcome. The network is composed of 40 nodes, i.e., keywords, corresponding to 5 clusters. Table 1 depicts the list of keywords that constitute the clusters, ordered by total link strength. The following paragraphs comment on the content of the clusters through relevant articles and define the main research areas within the literature concerning CE, I40, and LP.

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Fig. 2. Co-occurrence network of keywords from publications considering CE, I40, and LP together.

Table 1. List of keywords constituting the network clusters. Cluster 1

Cluster 2

Cluster 3

Cluster 4

Cluster 5

simulation

lean production

six sigma

industry 4.0

lean management

healthcare

digital transformation

sustainable development

circular economy

lean

lean thinking

internet of things

production

socio-technical systems

operational performance

resilience

lean startup

sustainable production

challenges

continuous improvement

role-playing

process improvement

manufacturing

just-in-time

knowledge management

supply chain disruption

sustainability

organizations

construction supply chain

logistics

supply chain dynamics

business model

quality management

supply chain management

case study

total quality management

dmaic

additive manufacturing

waste

improvement

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3.2.1 Cluster 1 Simulation has been traditionally applied as a methodology for solving many real-world problems, describing and analyzing the behavior of a system and asking “what if” questions and managing disruptions. Examples provided by literature are the use of simulation for allocating scarce resources to improve patient flow in healthcare management [42], analyzing the benefit of applying lean thinking tools [43], or predicting the impacts of epidemic outbreaks on global supply chains, i.e., supply chain disruptions (see among the others [44]). Considering healthcare systems, simulations are run also as role-playing activity, an educational and training tool, to apply lean thinking principles [39]. More recently, the study by [40] applied simulation to a case study on the interplay of CE and LP, with the aim to quantify the reduction in energy consumption led by LP tools, still responding a “what if” question. Simulation is also one of the key technologies of I40 and the base for the digital twin, rising renovated interest in researchers [41]. I40 technologies are meant to facilitate supply chain dynamics with more robust operations, such as remanufacturing, just-in-time production, and automated workflows. Recent supply chain disruptions are motivating enterprises to develop emerging IT capabilities, which will enhance supply chain resilience as well as sustainability [42]. As well, also additive manufacturing represents a tool to achieve green and lean supply chain, enabling the manufacturing of products near the customer’s geographical location, reducing the response time and achieving the maximum customer satisfaction [43]. 3.2.2 Cluster 2 In the last few years, LP applied in manufacturing companies aims at sustainability, enabling the transformation towards circular business models. Indeed, Lean thinking offers great flexibility in production processes and systems, challenging mass production practices and resulting in ‘leaner’ products with less waste [44]. In aid of lean, industry 4.0 digital technologies such as advanced analytics, autonomous vehicles or the Internet of Things are often touted as means to substantially improve operations and support process improvement and lean practices [45]. A pivotal example is the automotive industry where Total Quality Management is used to achieve zero defects [46]. Another LP tool is the whiteboard in the workshop, that, due to digital transformation, becomes a digital whiteboard, and in line with the goal of continuous improvement towards sustainability makes it possible to limit or even eliminate certain unnecessary processes [47]. Another example of the combination of digital transformation and LP to achieve CE objectives are start-up companies aiming for IoT product innovation. The combination of lean start-ups and design thinking makes it possible to develop an innovative solution for waste management in metropolitan areas, after understanding the processes of waste collection systems to support waste management in urban areas [48]. Agile business models and new digital services to follow the ongoing digital transformation, such as the BizDevOps approach, makes it possible to analyze business processes and transactions, enabling their automation through the composition of microservices, capable of rapidly adapting to the needs of rapidly changing businesses, i.e., adopting a Just In Time approach [49].

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3.2.3 Cluster 3 An organization with an intent to implement lean six sigma can reap benefits of achieving goals of CE and sustainability which can further be expedited with Industry 4.0. These goals can be met through the management of quality and productivity by applying the methods of lean six sigma [50]. Specifically, the Define, Measure, Analyze, Improve, and Control (DMAIC) methodology combined with simulation techniques has proved to help in managing process times, eliminating non-value-added activities, and balancing workstations [51]. Considering the management of process waste, LP and environmental waste are linked [52]. In the context of construction operations, a Discrete-Event Simulation model and an Input/Output framework for environmental loads was developed to simultaneously evaluate production and environmental performance and to reduce the time and cost of a project by improving resource utilization and reducing the emission of pollutants [52]. Other examples of I40 technologies supporting lean manufacturing for waste reduction are reported by literature. Manufacturing Execution Systems help to support process improvement through standardization of the way of working and satisfactory results can be achieved in terms of machine efficiency and time loss reduction [53]. Moreover, organizations can implement LP, I40, and CE early in the product design process. Lean Design and Sustainable Design combined with I40 technologies, such as digital simulation and augmented reality, provide effective solutions for developing products based on sustainable production strategies. The interaction of these areas makes it possible to include sustainability in the entire product life cycle [44]. 3.2.4 Cluster 4 Nowadays the adoption of sustainability has become essential for industries to support competition in the global market, but challenges in achieving sustainable supply chain management are varied and range from the high upfront cost of green packaging to the complexity of supply chain configuration. In this context, I40 and CE are said to contribute to the realization of sustainable products [54]. The adoption of the 6 Rs (i.e., repurpose, reuse, reject, repair, reduce, and recycle), environmental product design and life cycle analysis, and the digitization of supply chain activities are solution measures that can help overcome the challenges [54]. Specific challenges threaten the implementation of I40, and a socio-technical systems perspective could help overcome them [55]. Socio-technical systems theory includes socio-factors (people and culture) and technical factors (technology and infrastructure), with operational factors (processes and performance) and is suitable to understand the complexities derived by merging, for example, LP and I40 [56]. In construction supply chain management, two recent paths can be identified. On the one hand, lean construction is adopted mainly through the just-in-time approach, with the aim of reducing waste [57]. Another promising direction is to implement CE strategies, that, together with I40 technologies has been proven efficient in construction supply chain management [58]. Issues typical of the construction supply chain bring additional difficulties to the successful implementation of CE strategies, such as lack of legal warranties on recycled or reused materials and scarcity of demand from the market [58].

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3.2.5 Cluster 5 Lean management and the continuous improvement of processes have helped manufacturing organizations to improve their operational performance, i.e., cost, speed, dependability, quality, and flexibility [59]. According to [60], by applying lean and green concepts with special reference to I40 technologies, the enhancement of operational performance of logistics operations can be achieved. In the implementation of LP systems, the biggest change must be made in people’s knowledge. As multitude of different knowledge flows can occur, the management of knowledge is crucial to the success of implementation [61]. Also, organizations that are in a digital transformation program can benefit of the combination of lean management and knowledge management. Indeed, this combination allows the identification of wastes and their elimination, helping standardization and process automation required by the digital transformation [62].

4 Discussion and Future Research Avenues According to the results obtained from the cluster analysis above, first, I40 technologies and LP practices are confirmed to play the role of enablers for supporting the transition of companies towards a more CE, such as circular business models and circular operations, while both these paradigms support each other, as also shown in Fig. 3.

Fig. 3. A relationship framework between I40, LP, and CE.

Second, these paradigms can be especially exploited in the CE transition to maximize the performance of companies in terms of product and process redesign, waste production, as well as redesign of the customer interface. As far as the product and process redesign is concerned, the results show that some I40 technologies can effectively enable circular production. For example, the additive manufacturing, consisting of a suite of technologies that allows the production of a growing range of products through the layering or 3D printing of materials, is a very useful tool for the optimization of reuse, remanufacturing, and recycling processes, then for the redesign and reuse of products and their components. In this case, the implementation of LP together with this technology can offer the possibility to develop ‘leaner’ products with less waste. In addition, the IoT technology, which allows the interaction and cooperation between devices, things, or objects, through modern wireless telecommunications, such

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as IoT, but also sensors, tags, actuators and cell phones, can enable circular practices that are aimed to extend product life cycle and reduce waste management, such as predictive monitoring and maintenance. If combined with lean design systems, such as manufacturing execution systems, this technology may help supporting product and process improvement through standardization of the operations, which result in better efficiency and reduction of production time and waste production. Finally, simulation may be exploited together with just-in-time production to achieve objectives related to both the circular production and I40, such as the modeling of material flows in recycling processes or the regeneration of products. As far as the redesign of the customer interface is concerned, the implementation of I40 technologies and LP is mostly aimed to overcome the challenges associated with the achievement of operational performance, while engaging the stakeholders in the circular transition. For example, given that the CE paradigm requires producers to extend their responsibility on products, and the clients/customers to act as products’ users instead of buyers, there is a strong need to create a culture of circular economy in producer companies and disseminate it to their clients/customers. Notwithstanding the interesting results above, future research avenues remain to be addressed by future studies dealing with the topics of I40, LP, and CE, and especially at the intersection between each of these topics. These research avenues can be especially conceptualized considering the several perspectives through which the CE topic can be analyzed, and that are at the core of our theoretical background, i.e., redesign of products and processes, waste production, and servitization. Accordingly, future research questions may arise in each of these CE perspectives, considering the role of the I40 technologies and the LP practices as enablers of CE: Redesign of products and processes • “How do companies develop the I40 technologies and LP practices for circular products and processes?” • “How do companies leverage I40 technologies and LP practices to scout and select partners for developing circular business models?” • “What are the best I40 technologies and LP practices that can be exploited by companies for developing circular business models?” Waste production • “How do companies exploit the I40 technologies and LP practices to minimize the production of waste in their manufacturing activity?” • “How do companies exploit the I40 technologies and LP practices to support the reuse and recycling of their waste?” • “How do companies exploit the I40 technologies and LP practices to facilitate industrial symbiosis through waste management?” Servitization • “How can I40 technologies and LP practices be exploited for facilitating a more extended producer responsibility?” • “How do companies can exploit I40 technologies and LP practices for the design of take-back systems?”

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• “How do companies can exploit I40 technologies and LP practices to increase operations performance, thus saving costs and generating more revenues?”

5 Conclusions This study analyzed the interplay between I40, LP, and CE through a bibliometric analysis of the literature. The results show how the interplay between these three paradigms has gained prominence in existing research, with also several implications for scholars and practitioners. In particular, the research determines that the exploitation of LP practices and the I40 technologies support the circular transition of manufacturing companies, and that their simultaneous adoption amplifies the achievement of a more effective transition for these companies. These results contribute to theory and practice in various significant ways. Theoretically, it reveals the scarcity of studies on the interplay between I40, LP, and CE, and opens important future research questions on this field, which have been highlighted in the previous section. From a practical viewpoint, it provides guidance on how LP practices and I40 technologies can be used to support an effective implementation of CE, in terms of circular business model and circular operations, and recommends concrete actions for practitioners. Notwithstanding these interesting, although preliminary results, this study presents some limitations. The model provided in Fig. 3 should be further deepened and contextualized in different companies and sectors of activities, as well as eventually refined, given that major research gaps and questions remain to be explored and answered while investigating the relationship between I40, LP, and CE. Also, given that the rationale of the methodology behind conducting the current review relies on the analysis the cooccurrence of keywords that may be not completely representative of the importance of references and topics within the body of knowledge, our results must be confirmed through the usage of a wider sample of empirical techniques. Finally, given our analysis mostly highlights how the exploitation of I40 technologies and LP practices allow manufacturing companies to achieve environmental goals, such as the reduction of waste or a better (energy and/or material) efficiency, future studies are also required to investigate the economic feasibility related with the implementation of I40 and LP for a CE.

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Adopting Circular Economy Paradigm to Waste Prevention: Investigating Waste Drivers in Vegetable Supply Chains Madushan Madhava Jayalath1,2 , R. M. Chandima Ratnayake1(B) H. Niles Perera2 , and Amila Thibbotuwawa2

,

1 Department of Mechanical and Structural Engineering and Materials Science, University of

Stavanger, Stavanger, Norway [email protected] 2 Center for Supply Chain, Operations and Logistics Optimization, University of Moratuwa, Katubedda 10400, Sri Lanka

Abstract. Waste is a main issue in vegetable supply chains. Many studies focus on food loss in the context of waste, however, neglecting the losses due to farm inputs, handling & inter-relational inefficiencies, and suboptimal resource (labour, time, etc.) & energy consumption. This study aims to address the issue of waste in vegetable supply chains by focusing on the logistics aspect of the supply chain. The study employs the Delphi Technique and Interpretive Structural Modeling to identify and rank the main waste drivers and sub-drivers. The results reveal that the Collaboration Gap, Coordination Issues, Communication Gap, Unavailability of Cold Storages, and Unavailability of Handling Equipment are the main waste sub-drivers. Interestingly, three out of these five sub-drivers fall under the waste driver of connectivity-related drivers, emphasising the importance of connectivity in reducing waste. The study suggests circular economy-driven strategies and techniques such as circular business models, direct marketplaces, traceability systems, collaborative transportation and partnerships, equipment sharing systems or rental programs, and biogas-based cold storage systems to mitigate the impact of these identified waste sub-drivers. The findings of this study can guide stakeholders and policymakers in developing economies to reduce waste in vegetable supply chains. By addressing the identified waste sub-drivers, the supply chain can become more efficient, cost-effective, and sustainable, leading to significant economic and environmental benefits. Keywords: Waste Drivers · Vegetable Supply Chain · Circular Economy

1 Introduction The global vegetable supply chains (VSC) are troubled by significant levels of waste, resulting in a loss of valuable resources [1]. It is estimated that up to one-third of all food produced for human consumption is lost or wasted, with much of this occurring due to © IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 288–302, 2023. https://doi.org/10.1007/978-3-031-43688-8_21

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inefficiencies in the global supply chain, including post-harvest loss of vegetables [2]. The post-harvest loss of vegetables can occur at any stage of the supply chain, from the growing of crops to the final sale of produce in markets [3, 4]. This post-harvest waste occurred in the supply chain represents a significant loss of resources, including water, energy, labour, land, and other operational activities that used in the production process and logistics process [2]. In developing countries post-harvest waste mainly occur in the earlier stages of the supply chain including production, handling, storing, processing, packaging and transportation while in developed countries it is in the consumption stage [5]. Due to loss of vegetables, the resources used in the production, marketing and distribution process has a negative impact on the three pillars of sustainability that is economy, society, and environment [6]. This increases the cost per unit in the consumer market, while it increases the farmer expenses and decrease their income [5]. In the context of supply chains, logistics and transportation plays a vital role enabling delivery of right product at right quantity at right time to the right consumer [7]. Nevertheless, a vegetable supply chain has significant challenges in relation to perishability, temperature restrictions, uncertainty in supply and demand. Further, difficulties may arise in packaging and transportation, which increase the complexity of the supply chain in the context of waste minimisation [2, 8, 9]. The previous studies in the literature tried to identify and reveal the causal factors which affect towards the food loss and post-harvest waste [6, 10–14]. However, most of these studies only considered the post-harvest waste instead of overall waste (i.e., loss of resources, including water, energy, labour, land, and time). Regardless, the purpose of this study is to identify and rank the waste drivers in the VSC based on their impact and influence on the overall amount of loss. Waste reduction is one of the major challenges in a food related supply chain. Similarly, this is notable issue in a VSC, which associated majorly with the quantity and quality loss during the production, post-harvest and processing stage [15]. As opposed to the linearity practices in existing supply chains, the Circular Economy (CE) paradigm enables offering more efficient solutions to minimise the waste in the vegetable and other related food supply chains [15–17]. Specifically, Food Waste Hierarchy (FWH) frameworks provide various options to address the concerns related to food waste, by identifying priority actions and ranking them based on their environmental impact [18, 19]. This paradigm shift has the potential to increase the supply chain’s resource efficiency, minimise waste and implement recycling methods, extend the shelf life of vegetables, and propose business models and networks that foster collaboration throughout the supply chain [15]. The remainder of the paper is structured as follows. Section 2 undertakes an examination of the extant literature. Section 3 explains the methodology section as well as the technique employed in implementation of Interpretive Structural Modeling (ISM). The data analysis and discussion are covered in the fourth and fifth section. As the conclusion of the study section six contains a summary with insights for industry practitioners and decision makers, as well as the final remarks.

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2 Background Study The VSC meets with different challenges due to waste drivers, especially in terms of delays due to transportation problems in supply chains and logistics handling [20]. These issues exist in both developing and developed countries, and overcoming them requires coordination among stakeholders, including producers, retailers, consumers, and governments. Poor infrastructure, particularly inadequate transportation [21, 22] and storage facilities [10, 23] is a major source of waste in the VSC. This leads to severe food loss and waste along the supply chain, which is a common issue in developing countries due to limited resources and a lack of infrastructural investment [24]. However, this may be caused by a lack of coordination and inefficient supply chains in developed countries. Overproduction is another major source of waste that can be seen in the global context of agriculture [25, 26]. Farmers may overestimate the quantity of vegetables required in order to meet demand, resulting in surplus vegetables that are left unsold and eventually go to waste [11]. This is an issue in many countries, as producers may place a higher priority on supplying demand than on eliminating waste. Another waste driver in developing countries is the lack of education and awareness about food safety and handling procedures [15, 27]. This may increase the possibility of spoilage and contamination during transportation, storage, and handling due to the perishable nature of the vegetables. Changes in consumer preferences and the need for the “perfect” product can also lead to waste in developed countries [28]. Vegetables that do not satisfy particular quality standards and requirements may be rejected by supermarkets and retailers as well as consumers, resulting in significant waste during the distribution stage of the VSC. Table 1 summarises a comparison of key issues in developing and developed countries in the context of VSC. The waste occurred in VSC has the effect over several sustainability issues, many of which are identified by the United Nations’ Sustainable Development Goals (SDG) [27, 29]. The first one is to eliminate poverty by assuring fair prices for farmers and supply chain employees, which is important for attaining No Poverty (SDG 1). The second concern is to eliminate food waste and loss throughout the supply chain, which helps to achieve Zero Hunger (SDG 2) as well as Responsible Consumption and Production (SDG 12). The third one is to promote Good Health and Well-being (SDG 3) by limiting the use of toxic pesticides and chemicals in vegetable production, which can impair consumers and employees’ health. Achieving these sustainable development goals by enhancing the performance of the VSC can minimise the negative impact on economy, society and the environment while achieving higher food security. To achieve these SDGs through enhancing the performance of the VSC, CE is considered as a well-suited framework by several scholars [15, 16, 28, 30–32] Specifically, the concept of 3 R’s (i.e., reduce, reuse and recycle) has been used in several studies to confirm the objectives of the CE. Further, there are different waste hierarchies explained in the literature and Ciccullo et al. [15] summarised these frameworks mainly focusing on food waste. Waste Hierarchy (WH) has been used widely in the context of food supply chains to bring circularity to the context. WH was introduced by the European Union it was used by different scholars in the previous literature to reduce the waste in the food supply chains [33, 34].

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However, there is a grey area in the literature on how to associate actions and strategies in the early stages of the VSC in developing countries to reduce the waste in the VSC by incorporating CE. Adopting circular-driven practices allows for the reduction of waste, reduction of environmental impacts, optimisation of resource use, and the creation of new economic opportunities, all of which contribute to a more sustainable and regenerative food system. Hence, this study will contribute to the literature by identifying the supply chain and logistics related waste drivers in the supply chain in the first phase. In the second phase of the study, we propose recommendations on how to mitigate the negative effect of the identified waste drivers by incorporating a CE paradigm. Therefore, the objectives of the study will be to identify and prioritise the waste drivers in VSC and identify CE driven strategies to integrate with the VSC to mitigate the negative impact of the identified waste drivers. Table 1. Comparison of key issues in developing and developed countries Issues

Developing Countries

Developed Countries

Food Loss and Waste

Severe food loss and waste due Consumer demand for to poor infrastructure and limited high-quality and aesthetically resources along the supply chain pleasing products can result in significant waste during distribution

Education and Awareness

Limited knowledge about food safety and handling procedures

Higher level of knowledge, but there may still be issues with food safety and handling

Coordination Among Stakeholders

Lack of coordination among stakeholders due to poor infrastructure and limited resources, Less developed governance frameworks

Robust regulatory frameworks. However, still there may be Inefficiencies in supply chains and coordination issues among stakeholders

Infrastructure

Inadequate storage facilities, Inadequate transportation systems, lack of refrigeration facilities, and limited access to reliable energy sources,

Advanced and maintained infrastructure, enabling more efficient transportation and storage of vegetables

Sustainability

Challenges in adopting sustainable practices, lack of awareness,

Efforts to reduce waste, and improve environmental and social outcomes

3 Methodology This study identified the waste drivers through a background study along with a Delphi study in the first stage. In the second stage, the waste drivers were examined further, using Interpretive Structural Modelling (ISM), to prioritise the waste drivers while identifying the causal relationship between the waste drivers (Fig. 1).

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Fig. 1. Methodology

3.1 Delphi Study The study was conducted in Sri Lanka, which is a developing economy in the south Asian region. In the present, there are young entrepreneurs that are entering into this market, and they are trying to uplift the fresh Agri-supply chain through state-of-the-art concepts and technologies. Hence, there is potential to implement a CE driven VSC with these fresh business models. A background study was conducted using the previous literature in the domain. Consequently, field visits were carried out to different markets involved in the VSC, including distribution centers, wholesale markets and retail markets in Sri Lanka to connect with experts in the local VSC. The Delphi technique made the way to collect insights about waste drivers and waste sub-drivers in VSC from the experts who engaged in the VSC. This technique is more reasonable over other methods due to its characteristics such as anonymity, iterative process, and consensus-building capabilities and expert inputs [35]. This study employed 10 experts from the industry, including experts in the field of agriculture businesses, and 10 experts from the academic sector of the Sri Lankan university system with expert knowledge in sustainable supply chain management and CE. After selecting the expert panel, there were five rounds to collect their input. The first round was to generate initial responses; the second round was to collect feedback and compile the responses; and the third, fourth, and fifth rounds were to refine the initial responses and finalise the responses until there was a consensus of responses. To identify the waste drivers and sub-drivers in the vegetable supply chain, open-ended questions with a “what” question series were asked of the experts to generate initial responses and their opinions. After the fifth round, the Delphi study was concluded. Deploying the Delphi study as explained, waste-drivers and waste sub-drivers were identified as per Table 2.

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Table 2. Identified waste drivers and sub-drivers through Delphi Technique Waste Drivers

Waste Sub-drivers

ID

Transportation-related drivers

Poor Vehicle Conditions

T1

High Transportation Cost

T2

Delivery Delays

T3

Unavailability of Cold Vehicles

T4

Poor Transportation Handling

T5

Unavailability of Cold Storages

W1

Improper Storage Techniques

W2

Poor Storage Handling

W3

Unavailability of Handling Equipment

W4

Unavailability of Inventory Policy

I1

Demand Uncertainty

I2

Supply Fluctuations

I3

Packaging Damage

P1

Improper Packaging Practices

P2

Communication Gap

C1

Coordination Issues

C2

Collaboration Gap

C3

Warehousing-related drivers

Inventory management-related drivers

Packaging-related drivers Connectivity-related drivers

3.1.1 Interpretive Structural Modelling Interpretive Structural Modelling is used in many fields and especially in the fields of management science to understand relationships among different factors and elements of a system. This technique enables the researchers to identify the key elements, interrelationship between the elements and the hierarchy of the elements considering their mutual dependence [36]. This method is a powerful tool considering its collaborative approach which enables the stakeholders to be involved in the analysis process, comprehensive analysis and less subjectivity since it is based on expert judgement and consensus among the stakeholders of the study. Therefore, ISM was employed for this study to identify the relationship between identified waste sub-drivers and rank them according to the inputs collected through the experts involved in the Delphi Study. As Warfield [37] suggested below are the five steps to use the ISM tool respective to this study. I. Identify the waste drivers in VSC. II. Subsequently, undertake the following subtasks. a. Develop an interpretative logic table for each pair of identified waste sub-drivers b. Establish a pairwise contextual relationship between waste sub-drivers • V – Sub-driver i leads to sub-driver j

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• A – Sub-driver j leads to sub-driver i • X – Sub-driver i or j leads to sub-driver i or j • O – No relationship between the sub-drivers c. Develop a Structural Self-interaction Matrix (SSIM). Converting the SSIM matrix to the Initial Reachability Matrix (IRM) by substituting V, A, X, and O as follows: • V for sub-drivers (i, j), then the binary value in IRM for (i, j) becomes 1, and (j, i) becomes 0. • A for sub-drivers (i, j), then the binary value in IRM for (i, j) becomes 0, and (j, i) becomes 1. • X for sub-drivers (i, j), then the binary value in IRM for both (i, j) and (j, i) becomes 1. • O for sub-drivers (i, j), then the binary value in IRM for both (i, j) and (j, i) becomes 0. III. Develop the Final Reachability Matrix (FRM). IV. The MICMAC analysis should be conducted calculating the dependence and driver power from FRM. This MICMAC analysis reveals four clusters as below. • The first cluster is the “autonomous cluster” with low driving power and low dependency. • The second cluster is the “dependent cluster” with low driving power but high dependency. • The third cluster is the “linkage cluster” with high driving power and high dependency. • The fourth cluster is the “independent cluster” with high driving power but low dependency. V. Partitioning the waste sub-drivers into different levels. a. The level partition can be identified through the reachability set (RS) and antecedent set (AS). Finding the value “1” for factor i in the row identifies the reachability set of sub-drivers, whereas finding the value “1” for factor i in the column identifies the antecedent set. Finding the intersection between the reachability set and the antecedent set provides the intersection set (IS). The RS and IS elements are the same. The sub-driver is subsequently identified as Level 1 and removed for the next iteration. The level of the sub-drivers can be identified through the iteration process. VI. Develop the ISM digraph with interpretation relationships to represent the waste sub-drivers. As the first step of the methodology adopted for this study, main waste drivers and 17 waste sub-drivers were explored with the panel of experts and taken their inputs as pair-wise comparisons. This helps to identify the contextual relationship between the waste sub-drivers. After taking the inputs from the experts, the SSIM was developed considering the highest number of input value given by the experts for each cell as per Table 3.

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Table 3. Structural Self-interaction Matrix Waste Drivers

Sub-Drivers (T1) Poor Vehicle Conditions (T2) High Transportation Cost Transportation-related (T3) Delivery Delays drivers (T4) Unavailability of Cold Vehicles (T5) Poor Transportation Handling (W1) Unavailability of Cold Storages Warehousing-related (W2) Improper Storage Techniques drivers (W3) Poor Storage Handling (W4) Unavailability of Handling Equipment (I1) Unavailability of Inventory Policy Inventory management(I2) Demand Uncertainty related drivers (I3) Supply Fluctuations Packaging-related (P1) Packaging Damage drivers (P2) Improper Packaging Practices (C1) Communication Gap Connectivity-related (C2) Coordination Issues drivers (C3) Collaboration Gap

T1

T2 V

T3 V V

T4 O V O

T5 V A A V

W1 O O A X A

W2 O O O V X V

W3 O O A V X V X

W4 O O A A A A A A

I1

I2 O A A A A X X X X

I3 O O O O O V O O O A

V V X V V V V V V V X

P1 V O V V V V V V V V V V

P2 O V A V X X X X X X O V A

C1 O A A O A A A A O X A A A A

C2 O A A O A A A A O X A A A A X

C3 A A A A A A A A A X A A A A X X

4 Data Analysis The FRM was developed through eliminating relationships using the transitivity concept. Table 4 illustrates the FRM. Among the identified sub-drivers, Unavailability of Inventory Policy has the highest driving power and Packaging Damage has the highest dependence power, according to the FRM. As per the level partitions, there are 12 levels for the identified waste sub-drivers. The ISM model is constructed employing the levels as the final stage in the analysis. The connection between the sub-drivers is depicted by one-way and two-way connected arrows. Figure 3 depicts the ISM digraph with levels and connections between waste sub-drivers. ISM digraph also known as ISM model visualises the relationship between the identified waste sub-drivers. However, it does not provide a clear understanding about the dependent and independent waste sub-drivers. Therefore, the MICMAC analysis was performed. In here all the identified waste sub-drivers were categorised into four clusters according to their dependence and driver power as per Fig. 2. Table 4. Final Reachability Matrix Sub-drivers (T1) Poor Vehicle Conditions (T2) High Transportation Cost (T3) Delivery Delays (T4) Unavailability of Cold Vehicles (T5) Poor Transportation Handling (W1) Unavailability of Cold Storage (W2) Improper StorageTechniques (W3) Poor Storage Handling (W4) Unavailability of Handling Equipment (I1) Unavailability of Inventory Policy (I2) Demand Uncertainty (I3) Supply Fluctuations (P1) Packaging Damage (P2) Improper Packaging Practices (C1) Communication Gap (C2) Coordination Issues (C3) Collaboration Gap Dependence Power

T1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2

T2 1 1 0 0 1 0 0 0 0 1 0 0 0 0 1 1 1 7

T3 T4 1 0 1 1 1 0 0 1 1 0 1 1 0 0 1 0 1 1 1 1 0 0 1 0 0 0 1 0 1 0 1 0 1 1 13 6

T5 W1 1 0 0 0 0 0 1 1 1 0 1 1 1 0 1 0 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 12 8

W2 W3 W4 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 1 1 0 1 1 1 11 11 4

I1 0 0 0 0 0 1 1 1 1 1 1 0 0 1 1 1 1 10

I2

I3 0 0 0 0 0 1 0 0 0 0 1 1 0 0 1 1 1 6

1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 15

P1 P2 C1 1 0 0 0 1 0 1 0 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 1 1 0 0 1 1 0 1 0 0 1 1 0 1 1 1 1 1 1 1 1 1 16 13 4

C2 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 4

C3 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 4

Driving Power 6 5 3 8 8 11 7 8 11 15 4 5 1 9 14 14 17

As per Fig. 2, autonomous waste sub-drivers are Poor Vehicle Conditions (T1), High Transportation Cost (T2), Unavailability of Cold Vehicles (T4) and Demand uncertainty (I2). These sub-drivers have significantly less impact on the overall supply chain and can

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easily mitigate through relevant policies and actions. Improper Storage Techniques (W2), Poor Storage Handling (W3), Delivery Delays (T3), Poor Transportation Handling (T5), Supply Fluctuations (I3), Packaging Damage (P1) comes under the dependent cluster since they are dependent on the other drivers, and they are effortlessly vulnerable to the changes occurred in the supply chain. Unavailability of Inventory Policy (I1) and Improper Packaging Practices (P2), almost lies on the linkage cluster since both of these drivers have a higher dependence power and driving power. Communication Gap (C1), Coordination Issues (C2), Collaboration Gap (C3), Unavailability of Handling Equipment (W4) and Unavailability of Cold Storages (W1) are the independent subdrivers since it can influence other drivers of the supply chain. These five sub-drivers can be identified as major sub-drivers for waste generation in the VSC as per this analysis. More interestingly, when we consider the waste drivers aspect all Connectivity related drivers lie in this category. Since, we can understand that information flow is disrupted in most VSC in developing countries. This fact further verifies through the ISM model, since all connectivity related waste drivers are in the bottom level driving the other waste drivers. Hence, there should be certain strategies to improve communication among the supply chain actors starting from the sourcing point of input materials for the farming point to the end market of the consumer point.

Fig. 2. Driving and dependence power diagram

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Fig. 3. ISM Model

5 Discussion From the identified 17 waste sub-drivers through this study, Collaboration Gap, Coordination Issues, Communication Gap, Unavailability of Cold Storages and the Unavailability of Handling Equipment are found as the major sub-drivers of waste in the supply chain. These drivers highlight the issue of less integration and connectivity among the supply chain actors. This finding will add value to the current literature since this will enable fellow researchers to identify root causes which make waste in the VSC. These concerns arise with linear supply chain practices that involves with linear flow of materials from raw materials to product and disposal. To address these concerns shifting to a closed loop supply chain aiming to minimise waste is very important. Circular practices prioritise practices such as reusing, repurpose waste materials into valuable resources

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such as biogas or other bio-based products. Hence, this study enables to prevent the root causes that enable linear behaviours in the supply chains and enables ‘Circular Supply Chains” by proposing the CE driven strategies to the VSC. The following managerial strategies and implications are proposed through this study to bring circularity to the VSC while minimising the impact of the waste sub-drivers identified through the study. The lack of collaboration between the actors, including public-private partnerships or government institutions, is a major contributor to the wastage in the VSC. The study emphasises that the lack of infrastructure, including vegetable handling accessories such as crates, is a major factor contributing to the waste in the supply chain. The results of the study suggest that improving collaboration, coordination, and communication among the actors in the supply chain and promoting public-private partnerships and government support for infrastructure development could help to reduce waste in the VSC. As Kumar et al. [38] suggested, there should be development strategies and standards to enhance the market share through CE strategies to reduce waste in VSC. Hence, we suggest adopting CE driven strategies to reduce the waste occurred due to the identified waste drivers. These strategies were suggested to align to the levels of Waste Hierarchy (i.e., prevention, re-use, recycle, recovery, disposal) suggested by the European Union. Implementing circular business models and direct marketplaces is one solution to address the above main waste sub-drivers and promote sustainability in the agriculture supply chain. It will reduce waste and promote sustainability by enabling supply chain stakeholders to recognise consumer demand. Further this business model can utilise waste goods or byproducts from the supply chain as inputs for other products while satisfying the consumption demand with less production. This can also lead to more resilient farming practices that can tolerate demand unpredictability. In order to tackle waste that occurred due to coordination problems businesses can implement traceability systems that can trace products from farm to fork. This can assist in reducing the risk of waste due to miscommunication or misinformation, as well as identifying and addressing potential supply chain concerns such as delays or product quality issues. This will mitigate waste generation along the supply chain by reducing the intermediaries and the common issue that transferring the burden of waste to farmers and producers. Partnerships and collaborative transportation can also assist in minimising waste and increase sustainability in the agricultural supply chain. Farmers and producers can cut transportation costs by sharing transportation resources with other communities. Furthermore, by collaborating, they can share information, identify potential issues, and find waste-reduction solutions. Another solution is adopting sustainable packaging methods. A few organisations can collaborate, and they can utilise biodegradable or compostable materials as packaging methods or minimise the quantity of packaging used, or using packaging that can be reused or recycled. The lack of handling equipment can also lead to waste and inefficiency in the agricultural supply chain. The farming community and the stakeholders in the supply chain can address this by using equipment sharing systems or equipment rental programs. This enables farmers and other stakeholders to obtain equipment such as crates for handling vegetables when they need it. It will lower the total cost of the supply chain and boost circularity and sustainability by maximising equipment utilisation.

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An innovative solution to address the unavailability of cold storage is to develop a biogas-based cold storage system. This system reuses post-harvest waste that occurred in the VSC, recycling it into energy that can be used to power the cold storage system. By creating a self-sufficient business ecosystem that utilises waste as a valuable resource, businesses can reduce waste, promote sustainability, and reduce costs. These systems can be placed in major distribution centers and wholesale centers that can be seen in many developing countries.

6 Conclusion This study focuses on the key waste drivers and sub-drivers in the VSC and concluded that Collaboration Gap, Coordination Issues, Communication Gap, Unavailability of Cold Storages, and Handling Equipment Unavailability as major waste sub-drivers. Further, three out of these five sub-drivers fall under the waste driver of connectivity-related main driver, emphasising the importance of connectivity in supply chain stakeholders. This study suggests removing these root causes by implementing circular practices to the conventional linear consumption pattern with the aim of improving circular consumption pattern. To mitigate the impact of these identified waste sub-drivers, this study suggests circular economy-driven strategies such as circular business models, direct marketplaces, traceability systems, collaborative transportation and partnerships, equipment sharing systems or rental programs, and biogas-based cold storage systems. The VSC can become more efficient, cost-effective, and sustainable by tackling these sub-drivers, resulting in significant economic and environmental benefits. This will pave the way forward to removing the root causes of uncircular consumption patterns and diminishing the linearity in supply chains increasing food security. Future research should be conducted to identify the dynamic behaviour of these waste-drivers and simulate the benefits that can gained by establishing CE practices to the VSC than the conventional linear VSC. Acknowledgement. The authors would like to acknowledge the financial support given by the Norwegian Program for Capacity Development in Higher Education and Research for Development (NORHED II – Project number 68085), the “Politics and Economic Governance” sub-theme, the project “Enhancing Lean Practices in Supply Chains: Digitalization”, which is a collaboration between the University of Stavanger (Norway), ITB (Indonesia), and the University of Moratuwa (Sri Lanka).

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Product Information Management and Extended Producer Responsibility

Opportunities and Challenges of Applying Internet of Things for Improving Supply Chain Visibility of Incoming Goods: Results from a Pilot Study Ravi Kalaiarasan1,2(B) , Malin Ducloux2 , Tarun Kumar Agrawal3 Jannicke Baalsrud Hauge1 , and Magnus Wiktorsson1

,

1 KTH Royal Institute of Technology, Södertälje, Sweden 2 Scania CV AB, Södertälje, Sweden

[email protected] 3 Chalmers University of Technology, Göteborg, Sweden

Abstract. Supply chain visibility has become a key priority for manufacturing companies to handle disruptions and improve supply chain performance. IoT technologies have been recognised to enable higher levels of real-time supply chain visibility. However, there is a need for empirical research focusing on testing IoT technologies to improve supply chain visibility of incoming goods from suppliers. This study presents the findings from a pilot study conducted at a global manufacturer of transport solutions testing IoT technologies in real operations to improve supply chain visibility of incoming goods in their inbound logistics flow. In particular, the opportunities and challenges of applying IoT technologies to increase supply chain visibility in real operations are identified. The study provides guidance for manufacturing companies aiming to improve supply chain visibility and performance in their inbound flow and potentially other areas of their supply chain using IoT technologies. Keywords: IoT · pilot study · real-time data · supply chain visibility · inbound logistics

1 Introduction Improving visibility in supply chains has become crucial for manufacturing companies to both maintain and further enhance their supply chain performance [1–3]. Key elements of supply chain visibility (SCV) are information accessibility, accuracy, timeliness, completeness, and usage to enhance operational and strategic activities [4]. Several manufacturers attributed delayed deliveries and material shortages in their supply chains to the lack of implementation of SCV practices [4, 5]. Moreover, the covid-19 pandemic revealed how lower levels of SCV can significantly impact availability of materials and components [6]. It is reported that the supply disruptions cost in 2020 alone was © IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 305–318, 2023. https://doi.org/10.1007/978-3-031-43688-8_22

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approximately 4 trillion dollars [7]. SCV is stated to be key for operating in volatile and competitive markets [8]. Various supply chain regulations and increasing demand from customers will also require a focus on SCV [9]. Examples of such demands include responsible sourcing and ensuring human rights as well as employees working condition throughout supply chains [8, 10]. Recent studies emphasised the importance of SCV in the transition towards circular supply chains [11, 12]. Given this background, Internet of Things (IoT) technologies have been stated to support companies to attain SCV, and increasing planning, analytical and predictive capabilities [2, 13]. Overall, IoT technologies support to gather, manage and use data. Examples of IoT technologies include IT-infrastructure [14, 15], sensor-based technologies [16–18], and collaborative platforms [18, 19]. Several studies have addressed the potential of applying IoT technologies to improve SCV [20, 21, 25]. However, there are few studies that which investigated real-word examples where IoT technologies have been applied to enhance SCV in external flows [21]. The recent study by Wycislak [5] focused on the challenges of operationalisation of real-time visibility from supply chain professional’s viewpoint. Still, there is a need for testing IoT technologies for realising the value of SCV involving stakeholders across supply chains which include suppliers and 3rd party transport solution providers [5, 22, 23]. Such studies will provide insights into the possibilities and challenges of improving SCV of material and components in external flows [24]. Ignoring understanding the practical aspects of SCV in external flows is likely to result in both financial and reputational loss [25]. Motivated by this need, the objective of this study is to identify opportunities and challenges of applying IoT technologies to improve SCV of incoming goods. This study presents the findings from a pilot conducted at a global manufacturing company testing IoT technologies in road transports to attain real-time SCV of incoming goods. The study contributes to the understanding the implications of applying IoT technologies for improving SCV in flows involving multiple stakeholders and processes related to incoming goods. The results serve as a guidance for manufacturing companies aiming to improve deviations handling and decision-making capabilities in their inbound flow and potentially other areas of their supply chain.

2 IoT Technologies – Existing Literature A brief literature review was conducted to understand the potential of IoT technologies for improving SCV and supply chain performance. Prior to IoT, radio-frequency identification (RFID) served as a key technology for wireless information tracking and transmission. Subsequently, the interconnected networks consisting of GPS devices, smartphones, cloud-based systems, and sensors emerged. Collectively these are referred to as IoT [20]. Based on a systematic literature review focusing on IoT for supply chain management, Ben-Daya et al. [20, p.4721] defined IoT to be:”a network of physical objects that are digitally connected to sense, monitor and interact within a company and between the company and its supply chain enabling agility, visibility, tracking and information sharing to facilitate timely planning, control, and coordination of the supply chain processes.” This definition implies that using IoT technologies for improving SCV must focus on data gathering, data management, and data usage [26]. In terms of

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data gathering, IT infrastructure has been stated to support data collection [14]. Dubey et al. [15, 27] argued that connectivity and quality of information are important for SCV. Furthermore, sensor-based technologies have been recognised to play significant role in IoT applications for gathering real-time data [10]. One of the most used sensor types for SCV is RFID [28–30]. Previous studies focusing on implementing RFID to improve SCV in practice focused mainly on internal flows [28, 31]. However, improving SCV in external flows is likely to require active sensors-based tags apt to send real-time data of parts moving across multiple locations and environments. Data management includes storing, handling, and the protection of the acquired data. For large and complex supply chains, Big Data (BD) is required to be acquired and managed. For instance, Dubey et al. [32], based on a survey study from 205 organisations, stated that handling BD is linked to improving SCV. The framework by Govindan et al. [33] includes BD as a component for supply chain performance. Attaran [34] stated that storing and visualising collected data is important. In addition, collaborative planning systems can further support data handling and connect supply chain actors [35]. Based on their case study in the retail industry, Barratt and Barratt [18] informed that collaborative platforms support supply chain linkage. In recent years, blockchain has emerged as a promising technology to protect data and increase SCV [3]. Wang et al.[36], based on a pilot study conducted in UK, stated that blockchain will support the protection of data and increase trust. Finally, related to data usage, AI have been linked improve the usage of captured data. Calatayud et al. [2] based on a systematic literature study, argued that AI can aid stakeholders to improve decision-making. In addition, AI is stated to improve predictive capabilities and further enhance SCV [37]. In line with this, Dubey et al. [32], based on survey data from 205 organisations informed that the BD can be used to improve predictive capabilities which in turn can improve decision-making.

3 Research Approach 3.1 Company Selection This study is a continuation of the pilot described in Kalaiarasan et al. [38]. The pilot was conducted at one of the industrial partners – a global manufacturer of transport system solutions. They have a global manufacturing with approximately 50 000 employees across 100 countries. As presented and further described in Kalaiarasan et al. [38], the present study was initiated during the interactions with the case company where it was identified that there is a lack of SCV of incoming goods in their inbound flows. Especially, the covid-19 pandemic and the block at the Suez Canal caused several disruptions in material supply affecting the supply chain performance at the case company. To address the material shortage challenges and further improve supply chain performance, the case company wanted to focus on improving SCV of incoming goods in their inbound flow. As a first step, the pilot 1 described in Kalaiarasan et al. [38] was conducted. Based on the results and identified limitations of pilot 1, a second pilot (the focus of this study) to test IoT technologies in real operations in inbound flow was motivated. The case company was convinced that IoT technologies will play a key role in enabling SCV in their supply chain. Hence, it can be argued that the case company is an apt representation of a large

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manufacturing company on their journey towards extending SCV in its supply chains using IoT technologies. 3.2 Pilot Study As stated above, the objective of this study is to provide deeper insights of the opportunities and challenges of applying IoT technologies to improve SCV of incoming goods in inbound logistics flows. To support the objective, a pilot study approach was chosen. Conducting pilot studies have been informed to support approaching practical problems [39, 40]. Pilot studies further support to refine research approach before proceeding to a larger study and possible implementation of the solutions [41]. It is advocated to apply pilot studies to acquire in-depth knowledge in complex situations. In turn, this in-depth knowledge can generate more generic solutions [41]. In line with this, the pilot study at the case company involved multiple stakeholders (see Table 1), IoT technologies (see Sect. 4.1), and locations (see Fig. 1) related to the inbound flow. Data collection was carried out between April 2022 and June 2022. It included participation of the PhD student in six project meetings (concerning the planning, preparation and execution of pilot), six semi-structured interviews with the project manager of the pilot (from the case company), and analysis of project documentation. The data was discussed and reviewed the research team (consisting of one professor, one associate professor, one senior lecturer, and one PhD student). 3.3 Participant and Pilot Overview To support the objectives of the pilot study (detailed in Sect. 3.4), the case company wanted to test IoT technologies in a specific inbound flow in Sweden focusing on incoming goods from suppliers to one of their logistics centres. The pilot study required synchronisation between current stakeholders and processes related to the chosen flow. The case company also added stakeholders by collaborating with an IoT platform provider and supplier of sensor-based tags. Collaboratively they were both responsible for installing the IoT technologies and platform for the pilot (see Sect. 4.1). The installation required additions to the current processes at the case company, suppliers, and 3rd party transport solutions providers (see Sect. 4.2). Therefore, two suppliers and transport-solution providers who already delivered goods in the chosen inbound flow were approached and they agreed to be part of the pilot. The test was performed for 10 business days in Q2 2022. An overview of the participants and their responsibilities is summarised in Table 1. 3.4 Pilot Objectives Overall, the case company wanted to achieve tracking on pallet level for the deliveries from suppliers to one of their logistics centres. The decision to track on pallet level was based on primarily two reasons. First, case company decided that tracking on pallet-level was relevant to support the potential transition towards autonomous inbound flow. In such scenarios, tracking on pallet-level as considered important to enable autonomous docking

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Table 1. Overview of pilot participants. Participants

Responsibilities

Cross-functional team from the case company

- Leading the pilot - Ensuring that the objectives are tested

IoT platform supplier

- Responsible for the IoT based platform - Collecting and presenting the results

IoT technology supplier

- Responsible for supplying the sensor-based tags and related gateway - Installing equipment on the test vehicles

Two transport solution providers

- Responsible for the transport (from supplier to logistics centre - Responsible for vehicles used during transport

Two suppliers

- Responsible for tag deployment

Table 2. Overview of the objectives. Objectives

Description

Connectivity

- Evaluate connectivity between sensor-based tags and gateway - Evaluate connectivity between gateway and IoT platform - Evaluate connectivity during transport - Evaluate GPS position

Event registrations

- Arrival/departure at destinations - Geo-located zones entry - Completed delivery of goods

Notification of deviations - Notification via test and email when there is a deviation from ETA and scheduled route System support

- Provide visualisation of ongoing and historical transports - Visualise deviations -Provide real-time track & trace function

and departure of pallets containing goods. Second, it was not feasible and possible to track on item-level in during the timeline of the pilot. There were mainly four objectives which were developed in the project team. First, it focused on connectivity aspects including the connectivity between sensor-based tags gateways and IoT platform (described in 4.1). The second objective targeted registrations, which included collecting real-time data and registering the departure and arrival of goods. For instance, this involved registering departure and arrival of goods after loading and unloading operations at destinations. The second objective also aimed to register the movement of goods through checkpoints in geo-located zones created by the

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project team. The third objective targeted notification of deviations in events of deviations from estimated time of arrival (ETA) and scheduled route. This included automatic generations of messages and updated ETA based on real-time position data. The fourth objective was based on system support which included real-time track and trace function displaying historical and ongoing transports. The objectives are summarised in Table 2.

4 Pilot – Set up and Results 4.1 Overview of Technologies and Technical Set-up The preparation of technologies and equipment to support the test flow (detailed in Sect. 4.2) consisted of mainly installing gateways, deployment of sensor-based tags and connecting to IoT platform. The following describe the steps: 1. Preparing and installing gateways. The supplier responsible for IoT technology delivered sensor-based tags and corresponding gateway with complementing equipment. The gateway was a prototyped solution developed for test and was sealed and place in front truck cab driven by the 3rd-party transport solution providers (see Fig. 1). In addition, the gateway was connected to a power supply and an antenna. In essence, the function of the gateway was to receive positional data from sensor-based tags and connect to the IoT platform. 2. Preparing sensor-based tags. The sensor-based tags used in the pilot were based on ultra-narrowband technology. The tag had a dimension of 4 x 9 cm and were equipped with unique QR-code. The sensor-based tags were placed in red plastic bags to enable easy identification and prevent damage. 3. Enabling real-time tracking of goods. Smartphones, containing application handled by the IoT platform provider, were used to scan order and shipment ID by the supplier (see operation 2 in Fig. 2). The scanning created a unique ID for the goods which made it possible to monitor goods in transport in real-time. 4.2 Test Flow and Operations The test flow was from Jönköping to Södertälje in Sweden. Figure 1 presents an overview of the selected inbound flow, illustrating the implementation of IoT technologies for real-time tracking of pallets across various locations, stakeholders, and processes. The implementation of sensor-based tags into daily operations started at the supplier site, followed by transportation to cross-docks via carrier 1. This was followed by delivery to the logistics centre by carrier 2. As further illustrated in Fig. 1, there were three control points between the cross-dock and the logistics centre which were established to collect real-time data of the pallets during longer transports. Once the pallets were delivered to the logistics centre, the sensor-based tags were returned to the supplier. Figure 2 provides details on how the sensor-based tags were included in daily operations. There were six main operations. First, it started with registration of an order which involved the case company sending an order to the supplier. The second area concerned the preparation of order and pick-up at the supplier site. In the pilot, this was the first part of daily operation where the sensor-based tags were introduced and connected

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Fig. 1. Test flow for delivery of goods from supplier to logistic centre.

to the IoT platform to acquire real-time tracking of pallets already at the supplier site. This operation involved the supplier collecting orders, placement of sensor-based tags in pallets, and finally printing, attaching, and scanning label with mobile application from IoT supplier. The third area involved the pick-up of order of the connected goods. The pick-up was performed by the carrier. The fourth step was the arrival at cross-dock. Upon arrival, the goods were unloaded and sorted for pick-up by trunk-load. The fifth operation was the arrival at company logistics centre where the goods were received and unloaded. Sixth, after completion, the sensor-based tags had to be separated from the goods. Once collected, the sensor-based tags were returned to the supplier by the project team to restart the test.

Fig. 2. Operations involved in test flow from suppliers to logistics centre.

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4.3 Overall Results Overall, all objectives presented in Sect. 3.4 were tested and evaluated. In terms of connectivity, the result showed that high level of connectivity during the entire test flow could be maintained. It was observed that the connectivity from gateways and the sensor-based tags in the trailer was satisfactory. However, although not measured, three factors were noted to possibly affect connectivity. First, it was related to the material of the tracked goods. It was noted that the density of the goods had an impact on the connectivity. Second, the IoT platform supplier informed that placement of pallet with sensor-based tags can possibly affect connectivity. Third, the IoT platform supplier further informed that connectivity may be affected if the pallet with sensor-based tags were surrounded by dense material/goods in the trailer. Table 3. Overview of the findings. Objectives

Findings

Connectivity

+ Connectivity established during entire test flow, both between sensor-based tags and gateway and gateways and IoT platform − Connectivity possibly affected by material of goods tracked, placement of goods in trailer and surrounding material

Event registrations

+ Arrival/departure could be timely captured + Registration of loading/unloading, geozone entry and deliver of goods − Information from sensor-based tags missed due to frequency of message setting

Notification of deviations

+ Generation of automatic messages with new ETA − Accuracy of ETA is affected by frequency setting for receiving real-time position

System support

+ Real-time data-based track & trace system could be displayed + Display of deviations, ongoing and historical transports in the pilot

In terms of event registrations, arrival/departure events could be captured in a timely manner and displayed in the IoT platform system. In addition, successful loading, unloading, entry into geozones, and delivery of goods could also be registered. However, there were few instances where information from the sensor-based tags were missed due to configuration of geozones and frequency of messages settings. Related to notification of deviations, automatic messages in forms of email and SMS messages could be generated and sent to the assigned stakeholders. The messages were generated when there was a deviation from scheduled route and/or delays of deliveries in regard to original ETA. In such instances, new ETA based on real-time data could be generated. On the other hand, it was noted that the accuracy of ETA is related to the frequency setting for receiving GPS position.

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Finally, related to system support, the IoT platform system could provide a real-time data-based track & trace system. The system could display an overview of both ongoing and historical transports. Furthermore, visualisation of deviation was made possible in the system. In general, it was estimated that the ability to detect deviations can potentially save 1–1,5 working days of deviation handling at the case company. An overview of the findings is presented in Table 3.

5 Discussion, Conclusions and Future Research 5.1 Discussion Opportunities to Apply Iot Technologies: The results revealed ways in which IoT technologies can improve SCV leading to improvements in inbound logistics flows [13]. The opportunities can be grouped into technological, people and process-related factors. In terms of IoT technologies, the findings imply that sensor-based technologies are important for manufacturing companies to realise real-time SCV of incoming goods in their inbound flow (as illustrated in Figs. 1 and 2). In particular, the findings reveal that sensor-based tags can enable real-time SCV on pallet level. Depending on the situation, it can be argued that this is an optimal solution to attain real-time SCV compared to a lastmile delivery approach in which drivers normally need to manually enter the positional data of the delivery. Furthermore, the function of ultra-narrowband-based tags used in the pilot confirms that sensor-based technologies are fundamental to collecting real-time data [31]. While previous studies have employed sensor technology based on RFID to improve SCV [28, 30, 31, 42], this study proposes sensors based on ultra-narrowband as a viable alternative to enhance SCV in scenarios involving transportation of goods across multiple location and diverse environments. The results further indicate that large manufacturing companies, with complex supply chain structures, need to adopt IoTbased platforms to achieve SCV and make the data available to stakeholders in a useful way [34]. Evaluating the impact of possible implementation of the IoT solution within case company, three implications were identified. First, the results were promising in addressing the challenges associated with low supply chain visibility (SCV) of incoming goods faced by companies [38]. This study provides practical example of how IoT technologies can planned and tested in real-world operations, enabling real-time SCV of incoming goods across multiple stakeholders, processes, and locations in the bound logistics flow. Second, the case company informed that this would enable them to move from a state of trust-based system with manual data monitoring to a fact-based system. Third, such a system will in addition to improving daily operations, including deviations handling and cost management, also support the transition towards a more autonomous inbound flow. Related to people, the study reveals that multiple stakeholders must be involved and aligned in order to apply IoT technologies [43, 44]. One key success factor for executing the pilot was the motivation and management support at the case company [44]. Another success factor for testing IoT technologies was the involvement and positive attitude of the IoT platform provider, supplier of sensor-based tags, material suppliers, and transport solutions providers involved in the pilot.

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Related to processes and adding to the findings of Wycislak [5], the implementation of IoT technologies is likely potentially demand changes or additions in current processes. For example, considering the pilot in this study, the suppliers had to add one operation in their process which was to create a unique shipment ID for goods using a smartphone before goods were picked up by 3rd party transport solution providers. Similarly, the gateways had to be installed in cabs to enable connectivity and thereby capture data in real-time. Challenges to Implement IoT Technologies: Similar to the opportunities, the challenges of implementing IoT technologies can be categorised as technology, people, and process-related factors. Concerning the technological challenges, it was observed that there were instances where connectivity was low and in which the sensors were not communicating with the gateways. This affects the reliability of the overall solution and can potentially cause issues in large-scale implementation. Furthermore, it was observed that the material and density inside the trailer affect connectivity. Another challenge was the inability to use the platform to find root causes, something a potential user will need. Another example was the limitations of the gateways used. It was noted that trucks nearby can register the wrong goods. Related to people and processes, it is worth mentioning that the SCV gained in the pilot was limited to first-tier suppliers. To fully benefit from IoT technologies, IoT technologies must be extended and integrated with other areas of the supply chain such as internal and outbound operations. In addition, technologies identified in literature such as BD [32, 33], AI [2] and blockchain [36], which are needed for analysing and further using real-time data, have not been part of the pilot scope. These technologies are likely to be needed for large-scale implementations of IoT technologies across extended supply chains involving several stakeholders and processes. In such scenarios, potential challenges such as unwillingness to share information [45], feasibility and budget related challenges [25, 29], data security and governance [43, 45], and conflict of interest among supply chain actors must be addressed to benefit from IoT technologies [46].

5.2 Conclusion and Future Research Directions This study provides insights on improving SCV of incoming goods in real operations by deploying IoT technologies. Specifically, the study supports understanding the possibilities, challenges, and implications of deploying IoT technologies for improving SCV of incoming goods in flows involving multiple supply chain stakeholders and processes [5, 22]. Besides supporting a possible implementation of SCV system, the IoT technologies also showed potential to support handling deviations handling incoming goods and improve related decision-making The results also provide guidance for manufacturing companies aiming to attain real-time SCV of incoming goods by deploying IoT technologies in their inbound flow and potentially other areas of their supply chain. First, the objectives of what SCV-related issues and/or goals to be achieved must be established, aligned and communicated with stakeholders. As detailed in Sect. 3.4, the objectives set by the case company enabled them to evaluate the result against the objectives. Second, knowledge regarding deploying IoT technologies has to be ensured in order to gather,

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manage, and use real-time data [1]. The results from this study show that manufacturers might potentially need to collaborate with external partners to test IoT technologies in real operations. Third, as revealed by this study, deploying IoT technologies in real operations will demand changes in related processes which in turn require collaboration with suppliers and 3rd party transport solution providers (as detailed in Sects. 4.1 and 4.2). It is essential to acknowledge the limitations of this study. Together with the results, these limitations call for research in mainly three directions. First, related to data gathering and connectivity, the results call for more research regarding factors affecting connectivity of sensor-based technologies for tracking goods in real-time. In line with past studies on sensor-based technologies [42], this study noted that tracking goods can be affected by factors such as placement of goods in trailers and surrounding material. However, the factors were not measured and/or quantified in a structured way, and therefore, calls more research in this direction. Second, data governance and security were not in the scope of the pilot. Supply chain stakeholders will likely need to ensure data governance to secure data throughout supply chains. Therefore, technologies such as blockchain [36], which has been advocated to increase trust and protect data should be considered in future research targeting real-time SCV across supply chains. Third, although not explored in this study, the enhanced SCV through IoT technologies is likely to have the potential for the transition towards circular supply chains. Given the results, it could be valuable to understand how identified possibilities and challenges for improving SCV can be applied for establishing circular supply chains [11, 12]. In addition, given the involvement of the project team at the company, research on understanding stakeholder perspective on the role of SCV for circular supply chains is warranted.

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A Review on Design for Repair Practices and Product Information Management Nataliia Roskladka1(B)

, Gianmarco Bressanelli2 and Nicola Saccani2

, Giovanni Miragliotta1

,

1 Politecnico Di Milano, 20156 Milan, Italy

[email protected] 2 Università Degli Studi Di Brescia, 25123 Brescia, Italy

Abstract. Repair is a product value recovery strategy that slows down the use of new resources, allowing more time for resource recreation. Although it is one of the cheapest and easier to adopt circular economy strategies, the repair is still poorly applied in practice and less investigated in the literature compared to other strategies, especially in terms of product information management. This paper aims to shed light on Design for Repair practices for circular economy and sustainability, as well as on their data needs, requirements and ownership, which are vital for establishing proper product information management systems across its circular supply chain. A systematic literature review is carried out to collect and classify Design for Repair practices and their data needs. Results show that seven classes of data are needed to enable the adoption of Design for Repair practices in the supply chain of durable products: materials specifications; manufacturing and engineering Bill of Materials; routing lines such as product assembly/disassembly/testing sequences; product specifications; network and service infrastructure data; users’ data reflecting personas; usage data such as use frequency, failures and alerts. The identified practices and their data needs may help practitioners redesign their products in line with current and future Right to Repair regulations. Keywords: Product Design · Repair · Sustainability · Circular Economy · Extended Producer Responsibility

1 Introduction The topic of responsible manufacturing and extended producer responsibility has been paramount for the last few decades [1], and it has become an urgent issue, considering recent challenges related to climate change and the geopolitical context. Indeed, scientific articles on sustainable production and consumption have been increasing over the last few years [2, 3]. As a response to these challenges, academic society develops theoretical and practical frameworks for circular economy business models and sustainable product design implementation [4, 5], as well as establishing sustainable behaviour culture [6]. Governmental institutions also react by issuing policies to regulate energy consumption, material efficiency, and green product development [7, 8]. © IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 319–334, 2023. https://doi.org/10.1007/978-3-031-43688-8_23

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However, most products are designed to be substituted quickly [3], ignoring the responsibility for the entire product lifecycle. For instance, the number of smartphones produced per year has grown almost exponentially over the last 15 years [9], but the treatment of smartphone disposal and their redesign to meet the requirements of environmental policies do not follow the same pace. On the other hand, Extended Producer Responsibility policies expect the entrepreneurs to be in charge of the product lifecycle treatment, not only to the production and disposal stage but to proper servicing throughout the lifecycle [1], guaranteeing its longevity. Product repair is one of the most frequent practices to extend a product lifecycle [10]. Repair contributes to the sustainability of our planet, recovering product value after a failure and making products last longer. It slows down the use of new resources needed for new product manufacturing by postponing a moment when a new item is bought. Attention to repair has been raised after emerging the Right to Repair in the USA and the recent new opening of repair cafes all around the world [11]. To follow the Extended Producer Responsibility policy, changes must be introduced in the design phase of product development, and a proper product information management system must be set. The literature highlights the significant role of digitalisation in facilitating product design and managing its lifecycle [12]. For example, virtual reality and additive manufacturing may enable the prototyping of easily repairable products [13]; simulation of product performances can be enhanced with digital twins and the application of Artificial Intelligence algorithms [14]; monitoring and improvements recommendations can be given thanks to IoT sensing systems and Industrial Analytics dashboards. Even if repair is one of the cheapest and easier to adopt strategies of the circular economy [15], paradoxically, it is less investigated in the literature compared to other circular economy strategies, especially in terms of product information management. Therefore, this paper aims to shed light on Design for Repair (DfR) practices for circular economy and sustainability, as well as on their data requirements and ownership, which are vital for establishing proper product information management systems across its circular supply chain. To achieve this objective, a systematic literature review is performed with a content-based analysis of the selected articles. Section 2 presents the methodology adopted for this research. In Sect. 3, DfR practices are identified from the literature and systematised according to different perspectives of interested stakeholders and data ownership. In addition, data requirements are provided for each DfR practice. Then, Sect. 4 discusses the findings of the research in terms of DfR practices, types of product obsolescence and product information management. Lastly, the conclusions of the research are reported in Sect. 5.

2 Research Methodology Scientific articles on design strategies contributing to an easy and quick repair were reviewed in the context of sustainability and circular economy. Thus, two groups of keywords were used: (i) related to repair and design strategies to slow the loop, such as “design for repair”, “design for long life products”, “design for longevity”, etc.; and (ii) related to sustainability, such as “sustainab*”, “circular”, “green”. The “AND” operator was used to compose different search fields with two keyword groups from different sets.

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Scopus was the selected database, as it is a renowned source for engineering studies. Articles, books, conference papers and editorials were included, and three subject areas were selected: “Engineering”, “Business Management and Accounting”, and “Econometrics and Finance”, as they appear to be the most related to the field of study. The language of contributions was set for English only. The combined use of keywords brought to the total number of 828 papers that were then filtered by relevance based on journals, titles and abstracts (see Fig. 1).

Fig. 1. Process chart for the systematic literature review (Moher et al., 2009)

There were two main exclusion criteria for practical screening. The first one is related to the research area: the papers on civil engineering, built environment, marine science, medicine, sociology, history, agriculture, materials and energy management, and design creativity, were excluded. The second one is related to the focus of the study: papers focused on recycling, materials selection, and the assessment of the environmental impact or the lifecycle of such activities were excluded. During the methodological screening phase, the following criteria were applied: exclusion of technical documents that contain a detailed description of repair services which are hardly generalisable; and exclusion of papers in which repair is just mentioned but is not a focus of study. After the keyword-based search, a backward approach was adopted to include the relevant studies cited in the found contributions: this led to additional 33 papers being included in the review. So, in total, this literature review considers 119 available papers. The papers have been scrutinized, looking for the practices of different design strategies that could facilitate product repairability. Thus, examining design for disassembly,

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design for modularity, design for durability and other design strategies, the comprehensive list of practices was collected to conceptualize Design for Repair. The representative examples found in the literature were provided for a better illustration of each DfR practice, and the relevant data required were stated to support the adoption of product DfR.

3 Literature Review 3.1 Descriptive Analysis In the current section, the descriptive findings are reported. Firstly, the distribution over time of the 119 articles is presented on the intersection between DfR and circular economy and sustainability (see Fig. 2). Single contributions were published before 2000, focusing on repair in general or some product characteristics that extend the product lifecycle. Since then, there has been an overall increasing trend, with around 50% of papers published in the last five years. This trend also confirms the general claim that the number of published papers about circular economy and sustainability has grown considerably due to the increasing interest of researchers, practitioners and governments in this topic and emerged Right to Repair regulation [16].

Distribution of publications per year 18 15

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Fig. 2. Distribution of publications per year

Secondly, the distribution of the 119 papers across the journals where they have been published was analysed (see Fig. 3). The majority of articles (44 papers out of 119) have been published in the Journal of Cleaner Production, Sustainability, Resource, Conservation and Recycling, Proceedings of the ASME Design Engineering Technical Conference, International Journal of Advanced Manufacturing Technology. The Journal of Cleaner Production is the dominant source of articles in this literature review, accounting for 27 of the 119 articles. It confirms that research focusing on DfR is strongly connected to sustainability aspects. The 119 contributions scrutinised in the present review appeared in 71 different journals. There are also 4 book chapters and one dissertation among them. Consequently, it can be stated that the topic applies to many different research areas besides those related to sustainability.

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Fig. 3. Distribution of publications per journal

3.2 Conceptualisation of Design for Repair Practices and Their Data Needs, Requirements and Ownership As a sustainable product design strategy, DfR requires practices not only to facilitate repair when a failure occurs but to extend the product lifecycle until repair may be needed. Design practices for easier and quicker repair are mostly related to product architecture and functioning [17]. Relative design choices of Original Equipment Manufacturers (OEMs) become visible to repairers in the moment of product servicing. Instead, design practices to prolong product longevity have to consider how consumers use the product. For instance, [12, 18, 19] highlight the importance of including the consumer perspective when designing a product for a specific lifetime because, in the end, a consumer is the one who decides whether repair or replace a product. Thus, it is fundamental that product design recalls consumer attachment to the product, so he is willing to take care of it as long as possible. Therefore, DfR is supposed to be shaped considering several perspectives: the one about product functioning that mostly depends on OEMs choices, the one about product servicing that considers repairing services infrastructure and the one about product longevity that mostly depends on consumer preferences. The following sections report a collection of DfR practices from the systematic literature review, providing the definition formulated by the authors for each practice, giving representative examples and some indications of data required to manage the product lifecycle. Following the French repairability index requirements, the DfR practices provided in the following section were considered for consumer electronics. Design for Repair Practices: OEM Perspective. When a failure occurs, OEM’s choices about product architecture during the product design phase will guide the repairreplace decision. The complexity of repairing activities is tied up with the physical characteristics of products: materials choices, product fixing, disassembly sequences, etc. These product characteristics depend indeed on OEMs, manufacturers and suppliers. Table 1 summarises DfR practices which depend entirely on the abovementioned actors. Design for Repair Practices: Servicing Perspective. For proper functioning of repairing services network and infrastructure, there are fundamental aspects, such as spare parts availability, standardisation and commonality of components, accessibility to repair

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# DfR practice and definition

Representative example

Data requirements and ownership

1 Modularity – product feature that ensures its construction using individually distinct functional units instead of a solid monolithic structure

Framework laptop that is deeply customisable, allowing disassembly, upgrades and replacement of almost all components [20]

For redesigning products architectures as a joint union of physically detachable modules, an engineering Bill of Materials (BOM) and material information is needed [21], where each module is responsible for separate function [22]. These data are pertinent to manufacturers and suppliers

2 Easy and quick disassembly and reassembly – possibility to perform a straight-forward intuitive disassembly process and uncomplicated reassembly process

Fairphone is a representative example of an easy-to-disassemble and -repair phone [10]

To allow disassembly without damage to (reusable) components [2, 23–26] in a way to ensure short disassembly time, Products assembly/disassembly routing and sequences should be designed considering the ease of servicing and be reported in repair documentation

3 Openability and Accessibility – the ability to open a product and access its architecture and components with standard tools and equipment [21]

iPad has an adhesive and glue-based design that requires special tools to open or disassemble it [17]

Manufacturers should avoid narrow slits and holes for easier cleaning [27], adhesives and soldering components [28], or proprietary screws. Manufacturing BOM and disassembly routing lines should be reported in repair documentation and shared with repair technicians (continued)

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Table 1. (continued) # DfR practice and definition

Representative example

Data requirements and ownership

4 Safety – product design allowing safe repair

In terms of health injuries during use and repair because of the required use of small sharp tools

Besides avoiding toxic materials or unprotected sharp elements [2, 29], product tests of electrical items like voltage, frequency, load, and brownout should be made in security conditions [30]. In that regard, technical manuals with product specifications and testing routing lines according to ISO standards should be made available to repair technicians [21]

5 Materials durability – use Being able to replace parts of robust materials according without breaking them to the product performance

To ensure that components are robust and durable in line with product lifespan [22], materials information and technical specifications should be provided to repair technicians

6 Upgradability – keeping product performance by improving product functioning to prevent technological obsolescence

Miele washing machine is equipped with upgradeable software, an intelligent system and dynamic washing program management, according to the new cleaning products availability [1]

To communicate available upgrades to consumers and provide them with the right to decide whether accept or decline new upgrades, product version and characteristics should be made available [31], as well as usage data in order to understand consumers’ needs

7 Updateability – keeping product performance as it was originally designed to adapt to a temporally deteriorated product value

Computer software updates to assist products in adapting to technological change or adaptable highchair for children [32]

Constant updates release to maintain the competition and ensure the product’s effectiveness in changing environment [31]. Product version and characteristics should be available [33, 34], as well as usage data, in order to understand consumers’ needs

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manuals, and others. No matter whom repair is performed: official repairing services guaranteed by the manufacturer, or independent repairers or do-it-yourself, the absence of these DfR practices will impede repair (see Table 2). Table 2. Design for Repair practices: servicing perspective. #

DfR practice and definition

Representative example

Data requirements and ownership

8

Standardisation or adaptability of components – use of non-custom components through product generations within the same product category

Apple’s repair software does not allow independent repairers to ‘replace a broken part with one taken from another Apple device’ [35]

Apply the standard parts design and interfaces to make replacements feasible and economically viable; product manufacturing BOM should be made available in this regard [17, 23], as well as usage data to standardise the most critical components

9

Commonality or compatibility of components – use of common parts across product lines

EU Parliament approves common charging cable from 2024: all smartphones and tablets will have to be adapted for USB-C charger

Use of components that are feasible to back up from one product line to another within the industry (sector agreements); manufacturing BOM should be made available in this regard [2], as well as usage data to identify the critical components

The highest percentage of fail-to-repair reasons for iPad is the absence of appropriate repair tools [17]. However, in 2020 Apple declared to provide their authorised service providers with parts, tools and training [36]

To allow easy access and identification of the spare parts [23, 37] as well as to ensure spare parts availability throughout the product use-cycle (after last production) and fair price, delivery condition, manufacturing BOM and network and infrastructure data should be made available, as well as usage data to identify critical components

10 Spare parts and tools availability and affordability – the existence of spare parts and repair tools on the market at a competitive price that does not exceed 30% of the product price

(continued)

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Table 2. (continued) #

DfR practice and definition

Representative example

Data requirements and ownership

11 After-sales servicing – establishing infrastructure for returns and services, warranties

Paying a fee for full To establish an authorised servicing needed along with network of after-sales use [38] services to enhance the experience of the product use [12], network and infrastructure data should be made available (location of after-sales technicians, their characteristics, etc.), as well as usage data to understand which services are needed

12 Documentation – providing manuals and documentation containing information on how to service product

Motorola and Lenovo supply a wide range of product manuals and guides, warranty information, DIY instructions, and multiple repair service options and solutions directly from their corporate websites [36]

Providing understandable repair instructions with guidelines for disassembly and assembly routing lines [17, 36], clear illustrations, diagrams [39], as well as product information in terms of technical specification and points of product return for servicing and repair

Design for Repair Practices: A Consumer Perspective. No matter how well the product may be serviced, if a consumer perceives it obsolete, it will become an excuse for replacement [18]. Thus, to prolong product longevity, it is fundamental to consider consumers’ way of product use and their preferences on its look, as they are key decision makers upon repairing or replacement. Table 3 summarises DfR practices that make consumers keep products longer. Table 3. Design for Repair practices: consumer perspective. #

DfR practice and definition

Representative example

Data requirements and ownership

13

Attachment for products that contain a particular value for a consumer Detachment: consumer neutral attitude toward products

Users are likely to take care of a product that has a special meaning for them (e.g. being gifted by someone special)

To communicate the potential value of a product to its users, underline the meaning it bears [37], customer data and preferences should be made available [7] (continued)

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#

DfR practice and definition

Representative example

Data requirements and ownership

14

Timeless design – applying classic and “never old” design techniques

Traditional white washing machine, which is less likely to annoy a consumer soon

To prevent “fashion obsolescence” in design, there is the need to consider the various time and ecological dimensions of the materials that exist within the product lifetime [29]; customer data and preferences should be taken into account in this regard

15

Personalisation/Customisation A product with – allowing a user to personalise personalised writing its products and enhance a feeling on it of uniqueness

To allow customisable product architecture so that the users may personalise their products in a way it matches their personality, being an additional reason to keep it longer [34], customer data and preferences should be considered in this regard

16

Ergonomics in use – product design to ensure suitable and intuitive functioning

Comfort in use [40] Intelligent assistant for the troubleshooting and testing processes [41]

To provide easily understandable and reliable information about how to inspect [22], use and service product, advice on product care, describe signals of product malfunctioning [21], usage data such as diagnostics, alert/error codes should be made available. In addition, customers’ data should be included in order to meet consumers’ preferences and expectations

17

Repairability look and feedback interface – including intuitive interaction signals about product functioning and failure

Electronic displays on the products that may indicate the error code, different colouring for blinking lights [21]

Embed alert, error codes in monitoring sensors and display to signal when it’s time to schedule service before a failure actually occurs [42]

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4 Discussion: Towards Product Information Management for Design for Repair Practices in Sustainable Supply Chains Sustainability-aware society starts forcing manufacturers to take on their extended producer responsibility, for example, by claiming their Right to Repair [31, 43]. To comply with the current Extended Producer Responsibility policy, changes must be introduced in the design phase of product development. However, paradoxically, repair is less investigated in the literature compared to other circular economy strategies. DfR practices, collected through the systematic literature review, reflect the perspectives of OEMs, suppliers and manufacturers, repairers, and consumers. Figure 4 shows the number of publications that addressed each specific DfR practice.

Number of pubblications discussing DfR features 80 70 60 50 40 30 20 10 0

OEM perspective Repairers perspective Consumer perspective

Fig. 4. Number of publications of DfR practices

Overall, this systematic literature review demonstrated that design practices discussed more often are those related to product physical functionality and servicing, such as disassembly, modularity, and spare parts availability. Instead, design practices crucial to making consumers keep products longer are less discussed. To meet consumer preferences and ensure product longevity, it is essential to analyse the product usage data. It is the responsibility of the producer to establish related product information management processes to enable product design accordingly. The analysed literature provides considerations on data requirements and ownership for establishing a sustainable and circular supply management system based on repair. In particular, Table 4 summarises the data needed for each DfR practice to be shared among the supply chain actors. Data have been grouped into seven classes: (i.) materials specifications such as their composition and characteristics, usually owned by suppliers; (ii.) Bill of Materials, usually owned by the manufacturer; (iii.) routing lines, such as

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product assembly/disassembly/testing sequences, usually owned by the manufacturer; (iv.) product specifications, such as its technical characteristics and versions, usually owned by the manufacturer; (v.) network and service infrastructure data and information such as actors of the circular supply chain, their characteristics and geographic location, usually owned by the manufacturer and distributors; (vi.) users’ data reflecting personas with their preferences and willingness to pay, owned by customers; (vii.) usage data such as use frequency, failures, alerts, owned by customers, or in some cases by manufacturers. Even though currently, tracking and tracing usage data by manufacturers is challenging due to privacy issues. Table 4. Design for Repair practices data and ownership across the supply chain. Supplier

Manufacturer

DfR practice

Materials specifications

Bill of Materials

1. Modularity

X

X

2. Dis-reassembly

Consumer Product specifications

Network data

Users’ data

Usage data

X

3. Openability, accessibility

X

4. Safety 5. Materials durability

Routing lines

X X

X

X X

6. Upgradability

X

X

7. Updateability

X

X

8. Standardisation of components

X

X

9. Commonality of components

X

X

10. Spare parts and tools

X

11. After sales servicing 12. Documentation

X

X

X

X

X

X

X

13. Attachment/Detachment

X

14. Timeless design

X

15. Personalisation/customisation

X

16. Ergonomics in use

X

17. Repairability look

X

X X

Table 4 confirms that DfR practices 1–7 related to product architecture require data sharing from suppliers, OEMs and manufacturers, such as materials composition, bill of materials, and product specifications. When it comes to product servicing (practices 8– 12), the ownership of data required shifts from suppliers to consumers, keeping involved manufacturers and distributors because to service the product effectively, the data on how the product has been used is crucial. It implies the identification of critical components with a higher probability of failure. Other essential data to service the product are product specifications to open a product safely, routing lines to disassemble and reassemble it quickly, and so forth. It is also essential to gain distributors’ willingness to collaborate and share network data to establish repair services infrastructure (e.g., drop-off and pick-up

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points, distribution channels for spare parts and tools). Indeed, the literature confirms that easily accessible and widely available repair infrastructure nudges consumers to repair their products instead of replacing them. Lastly, DfR practices 13–17 for prolonging product lifespan require data on user preferences and their usage of products. Digitalization can enable transparency and more accessible information sharing among circular supply chains through the interconnection of its processes. In this regard, the literature confirms the significant impact of IoT, Big Data and Analytics, Blockchain and other digital technologies on product lifecycle management and optimisation. Profound and structured data collection may ensure more efficient decision-making when integrating sustainability considerations, particularly, DfR practices, in product design. Overall, this is the first attempt to shed light on data requirements and ownership for DfR purposes. Following the extended producer responsibility policies, this information can be a first step in conceptualising a Digital Product Passport for long-lasting products that enable easier repair.

5 Conclusion Circularity and eco-efficiency are gaining momentum both in the state-of-practice and the state-of-art, nudging manufacturers to respect their extended producer responsibility by redesigning the product for longevity. Repair is a product value recovery strategy that extends the product lifecycle that helps follow environmental policies. Although crucial in the journey towards sustainability, product DfR is still both poorly investigated in the context of the circular economy literature and seldomly applied in practice. This paper provides the results of a systematic literature review on DfR practices for circular economy and sustainability, as well as on their data requirements and ownership. Based on the analysis of 119 articles, design practices to enable easier repair are systematised and discussed. A definition of each practice, a representative example and the data required to implement them have been highlighted. Through this literature review, we found that practices related to product architecture and product servicing are widely discussed, probably as they are more connected with traditional manufacturing excellence practices, while others are less investigated. Future research should then investigate DfR practices adopting the consumers’ perspective, which are crucial to meet consumers’ preferences and make them keep products longer. A first attempt to shed light on data needs, requirements, and ownership of relevant information needed for DfR is also carried out. These data and information are vital for establishing proper product information management systems across its circular supply chain. They can be seen as a first step in creating a Digital Product Passport for long-lasting products that enable easier repair, following the extended producer responsibility policies. Nevertheless, future research is needed on this topic, encompassing theoretical and empirical investigations on designing Digital Product Passport solutions based on repair for different long-lasting product categories. The DfR practices and their data needs may help practitioners implement sustainability and circular economy projects in the supply chain of durable products. Indeed, this paper could be taken as a starting point for practitioners who consider redesigning their product in line with current and future environmental policies and Right to Repair legislation, as well as a point of reference to draw the repairability evaluation

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criteria for further application and promotion of repairability index to enhance manufacturers responsibility for sustainable product development. Nevertheless, the results of this research are purely based on the scientific literature, thus lacking insights from the practice world. Therefore, the next research step would be to test the relevance of the identified DfR practices through several case studies taking different durable products.

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Approach on How to Handle Digital Thread Information in Manufacturing with a Human-Centric Perspective Taking into Account a Didactic Factory Kay Burow1(B)

, Patrick Klein1

, Karl Hribernik1

, and Klaus-Dieter Thoben2

1 BIBA – Bremer Institut für Produktion und Logistik GmbH, Hochschulring 20,

28359 Bremen, Germany {bow,klp,hri}@biba.uni-bremen.de 2 Institute for Integrated Product Development, University of Bremen, Badgasteiner Straße 1, 28359 Bremen, Germany [email protected]

Abstract. Industry 4.0 is well-known nowadays and widely integrated into many production processes, but there is also a dark side of it. Many technical systems have become highly complex and are difficult to understand and maintain in detail. Familiarisation requires a certain amount of experience to understand the problems properly. Reconfiguration, adaption, or repair may cause risks of downtime and malfunction, especially for inexperienced operators. With the background of manufacturing large-scale components and mapping these to a digital thread, we extended our Industry 4.0 approach by incorporating didactic factory elements. The advantage of this approach is to scale down the production environment to a lab level and mirror difficulties and challenges against small-scale systems, as well as being able, at least in part, to scale up identified problems and solutions. The paper shows this mirroring, starting with how data can be collected, extracted and processed using a digital thread on different levels. Further, the digital thread is adapted to a use case with its information, starting with redesign through to the integration with robot simulation software. The therefor-developed application updates and integrates the parameters into the simulation on physical robots. An important part is introducing inexperienced people, next to the interaction of different software; the reading, mapping and processing of information are typical challenges in an industrial environment. This paper intends to show how giving them the possibility to try, learn and/or develop (depending on their level of knowledge and their overall task) a “miniature production facility”. Keywords: Digital thread · AutomationML · Didactic Factory · Industry 5.0

© IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 335–349, 2023. https://doi.org/10.1007/978-3-031-43688-8_24

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1 Introduction Today, Industry 4.0 is widely used in large industrial environments. Especially in areas such as automation and mass production, it has become an important and everyday companion of logistics [1]. The application of the Internet of Things (IoT) and Industrial IoT (IIoT) to digitalization has revealed new opportunities. This is especially important in the field of complex systems and products. For example, these systems and products can be represented as digital twins. These are comprehensive virtual images of a physical system or product [2]. It is also possible to only partially represent them in relation to individual information using a digital thread (DTh), which are the focus of this work. A Digital thread is a data-driven architecture that links information from all stages of the product lifecycle. It can track individual information about their entire lifetime and thus detach himself from the complexity of the overall system [3]. For example, Fig. 1 shows a digital thread and its information progress, on whose structure we based our work.

Fig. 1. Product lifecycle over a digital thread [3]

The possibilities for handling real-time data for process optimisation and advanced maintenance and monitoring possibilities have become enormously important to manage complex systems, leading to improvements in automation and enhanced productivity [4]. However, with new opportunities come new challenges. Due to the increase in the complexity of systems the following challenges can be highlighted, which are typical for the Industry 4.0 [5]: • Technical Skills: New skills and experience are necessary for the user to be able to use the new technologies • Interoperability: Necessary to merge/interconnect different protocols, components, products, and systems • Handling Data Growth: With digitalisation and IoT, a vast amount of collected data must be handled • Data Sensitivity: Data and IP privacy, ownership, and management • Security: Physical and virtual connected systems and components must be secure against attacks and data theft The first thing that strikes us is the increase in complexity, especially when we compare the technical systems from Industry 3.0. Furthermore, it is also evident that

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it is becoming increasingly difficult for people/workers to integrate into the industrial environment. On the one hand, there is a need for concrete and highly qualified specialists who can work on even a small task in the overall system. Still, on the other hand, there is also an increased risk that the role of people will become outsiders and that they will hardly be needed anymore [6]. While the focus of Industry 4.0 lies heavily on automatisation and digitalisation due to new technologies and customer demands, the role of humans was neglected [7]. Awareness about environmental pollution and energy consumption has increased dramatically over the last few years. These topics were not in the focus of Industry 4.0 but have since become highly relevant. In addition, the impact of the COVID pandemic, international conflicts and other disruptions on industry have changed the game for industry worldwide. These developments have given rise to a new concept called Industry 5.0, which focusses on human centricity, the environment and resilience [7, 8]. 1.1 Industry 5.0 Industry 5.0 is based upon the concept of Industry 4.0 but will not mindlessly follow its steps in improving and optimising technology. It questions the current development of Industry 4.0 and wants to counteract negative trends and fears, for example a technocentricity leading to the loss of jobs. In the last years, the gap between new technologies and users/humans has become more prominent – a critical aspect which also can be found in the literature [7, 9]. New concepts and ideas must be developed and implemented on how humans can be a part of the industrial environment. Three pillars which should particularly shape Industry 5.0 were highlighted in two European Commission workshops [8, 10, 11] (Fig. 2):

Fig. 2. The three pillars of Industry 5.0: Human-centricity, resilience and sustainability [12]

• Human-Centricity: The human shall be back in the centre of the production process with his needs and interests using what new technology can do for us and how to adapt the technology so that it is easier to handle for humans. Using the “power” of humans will be part of human-robot-collaboration (HRC), our creativity and brainpower to pair humans and machines for future applications. Another interesting point is the cultural collaboration between humans, as many processes are globally connected and need improved interaction and understanding [7–9].

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• Resilience: The need to develop a higher degree of robustness in industrial production, arming it better against disruptions and ensuring it can provide and support critical infrastructure in times of crisis [8]. The corona outbreak, the US-China trade war, and Russia’s attack on Ukraine caused heavy disturbances in the global economy, logistic chains and production lines [13, 14]. • Sustainability: Develop circular processes that re-use, re-purpose and recycle natural resources, reduce waste and environmental impact. Especially concerning Earth’s natural resources, sustainability is reducing energy consumption and greenhouse emissions [8]. But also economic sustainability (e.g., economic development, industrial productivity) and socio-environmental sustainability (e.g., social innovation, safer working environment) are part of this pillar [15]. The first pillar, Human-Centric, plays a significant role for us as an institute in applied industrial research (application-oriented research) and teaching. As found in the literature, HRC and human-machine interaction (HMI) will be highly relevant for future industrial applications but require a better understanding, new concepts and ideas on how humans and machines can collaborate and work together [15]. We repeatedly observe problems in dealing with complex systems, and at this point, we try to work through the understanding of the problems, and the transfer of knowledge plays a significant role as well as opportunities for people. The point noted by the EC that new technologies must be adapted to people so that we can use them better and more easily is an observation that we regularly make in the context of our work and projects. 1.2 Our Idea: A Factory Down-Scaled to the Lab Level This gave rise to the idea of taking up industrial tasks and challenges from our research and connecting them with our work in teaching or for demonstration purposes on-site. As a research institute, we cannot map large and complex process flows, but we can take up important aspects such as data handling or the interaction between humans and robots and map them at the lab level (down-scaling). On the one hand, this enables us to address critical industrial problems in a targeted manner. Still, on the other hand, it also allows us to impart knowledge and understanding in dealing with technical systems. In this way, we can try out new ideas at the lab level, even make mistakes, and address problems in a targeted manner. In comparison, this is virtually impossible in ongoing industrial processes. Integrating and testing new modules in a continuous process is very delicate. Often this has to be done during maintenance work or during conversion, but running processes are not simply interrupted. The risk that problems arise during integration and an unwanted downtime occurs, or in the worst case, a fault leads to the entire system failing is simply too great – an economic loss for which no one wants to pay. We want to turn this disadvantage into an advantage. In addition to the research approaches to data handling and interoperability, we also want to convey industrial problems, broken down to a lab level, in a didactic factory. Based on an industrial use case for the automated handling of large-scale components, more specifically redesigning fitting pipes for shipbuilding, the approach will supplemented by a physical demonstrator. With the help of a small collaborative robot, the problems mentioned are addressed, and

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the demonstrator is supported within the framework of student projects. The following points are covered: • • • • • • •

Data handling Data Availability Turning data into information. Software, tool and application integration Developing generic and transferable models Approaching (technical) problems Human-Robot-Collaboration

As a second step, we look at what problems there are for humans and how we can better introduce them to such processes. People should once again be at the centre of industry and technology. Here more research has to be done. On the one side, we need a deeper look at the literature and compile and structure typical problems humans face interacting with machines and robots. On the other side, we collect our observations during the set-up of the demonstrator and, later, use the demonstrator in workshops and teaching projects. At the moment, our focus lies on the set-up of the demonstrator and data handling before we concentrate on the human-centric topic.

2 Approach and Methodology As described in Sect. 1, our project consists of several building blocks. We started with research on how we could set up such a system. In addition, there is experience and preparatory work from previous research included here [16–18]. Our approach can be roughly divided into two parts. The first part considers how digital thread information can be processed. Objectives included here are: • Identification of requirements • Identification of possible/usable software/applications/tools (commercial, opensource, in-house developments) • Identification and definition of interfaces The second part concerns the Didactic Factory approach, which ultimately takes up the problems of the first part (digital thread information) and is intended to bring the application closer to humans. Small tasks are worked on in workshops and projects – problems that arise are collected and analysed, and the approach to them is taught. Therefore, it is one of our goals to build up a Didactic Factory piece by piece and already use parts of it as well as approaches that contribute to further building our Didactic Factory. A significant advantage of a Didactic Factory is the opportunity to try things out and make mistakes without paralysing the entire production process. At the lab level, mistakes are justifiable, but in the industry, a standstill is costly. This also allows us to analyse, process and review mistakes made to provide a better understanding of technical systems. In addition, we take the opportunity to take up and try out new ideas that are practically not feasible in ongoing (industrial) processes.

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2.1 Concept This section will introduce the general concept (see Fig. 3) of handling data using the digital thread approach. Our approach is divided into three parts. The first part belongs to external data, where we get requirements and technical data like CAD files and 3D measure points. The second part includes mainly our developed application Telegon, described in the next chapter in more detail. Telegon serves as a data handling tool and uses the neutral XML format; it is connected with the other software using APIs, like the CAD software Inventor and the robot simulation software RoboDK. The third part is the didactic factory and exists of a software part (RoboDK) and a physical demonstrator (UR3e and a piping set-up). Behind this is the automated and digitised adaptation of fitting pipes, which we have presented in another work (see [19]), but will not be in focus in this paper – we are continuing the previous work. Fitting pipes are used to connect pipelines from two ship segments. Since each ship segment is manufactured separately, production-related deviations occur later when connected. One possibility of adaptation is the measurement by a 3D system, which measures the “real” positions of the flanges. Through an AS-IS comparison, the design parameters can be adjusted with the newly measured parameters, and a new fitting pipe can be created (auto-routing via Inventor). The parameters are written in a neutral XML format, allowing us to remain generic and only partially dependent on specific software. In addition to the Telegon application we developed, the data structure is based on AutomationML, which will be discussed in more detail in the next chapter. As a further step, we used an API for a robot simulation (RoboDK). We can now transfer the design parameters, i.e., the coordinates of the fitting pipes, as targets into RoboDK and use them in the robot simulation. As a next step, the new parameters will be transferred to RoboDK, and the robot simulation will be automatically adapted with just a few clicks, which will finally be shown on a physical demonstrator. By means of the developed application, it is ultimately possible for us to deliver automated parameter adjustments to the robot simulation. Here it is our goal that these parameters are used in the context of student projects and workshops to create and test our simulations. The approach of transferring parameter adjustments from the physical robot back to the robot simulation is planned but not developed yet. This means that the positions of the robot are adjusted, and these readjusted parameters are fed back to the robot simulation. The developed application -named Telegon – for data exchange is presented in the following section, which is the basis of the whole concept.

3 Use Case: Software Solution Telegon for Information Exchange The industrial implementation of Industry 4.0 and IIoT covers hardware and software systems. Following the approach described above, the authors decided to develop a dedicated software prototype that aims to reduce the complexity of IIoT through its user interface without limiting restrictions for used hardware. The specified and prototypically developed solution allows information exchange along the digital thread for the process chain based upon the neutral exchange format AutomationML (AML). AML

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Fig. 3. Concept of the combined approach of data handling and the didactic factory

files, standardised in IEC62713, consist of an XML-based format, allowing modelling topologies (plant topologies, resource topologies, and communication topologies) [20]. AML files can store all relevant data for a production system [21], the structure remains human-readable due to its XML roots. It can be displayed in a way that is easy for even inexperienced users to understand. The software prototype (named “Telegon”) provides capabilities to open AML files and add relevant parameters tuple (attribute name + value) to structural elements (socalled Internal Elements in AML). Telegon can exchange information with 3rd party applications and store this information in AML files or, more precisely, enhance existing AML files by appending existing AML elements to a certain point in the hierarchical structure of AML. To support intuitive usage, Telegon takes responsibility for semantics. For this purpose, each AML file (which is manipulated by Telegon) is enhanced with AML roles to be used for further post-processing. For instance, importing dedicated CAD points (e.g., extracted from auto-routed pipe segments) means adding each as an AML element with its role “3D Point” next to 3 attributes (“X”,” Y”,” Z”). Telegon does not fully replicate the features of an AML editor. The workflow allows setting up an AML file with an AML editor (e.g. [22]) and using this file with Telegon. Telegon offers to open an AML file template provided with the installation for users who want to avoid using an editor. For the digital thread, we validated the AML backbone in conjunction with our software prototype Telegon by transferring measure data into CAD to initiate autorouting of pipe layering and export piping routes back into AML for data exchange with subsequent steps. In addition, we can import the aggregated pipe routing information (for more details about the use case, see [19]) and process it to a robot simulation with information relevant to enable the handling and welding of pipe segments of a fitting

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pipe. The commercial robot simulation software RoboDK is linked to Telegon via its API. This link enables to generate so-called targets in RoboDK by manipulating 3D points stored in AML. Since RoboDK supports a broad range of industrial robots of different brands directly from the shelf and targets are modelled independently from chosen robot model, the scalability (as envisaged in our overall approach) is supported by default. AML is not suited for monitoring and tracking production statuses like the status of robots. This is mainly because it is not specified to handle data streams and events. The prototype has been enhanced with OPC UA (OPC Unified Architecture) client features to cover this. Telegon can connect to an OPC UA server, and information of interest can be selected and handed over to AML. The module implementation reflects the OPC UA specification [23]. The OPC UA coupling allows accessing status information of production lines and/or collecting relevant information, which is provided, e.g., about a production cell via OPC UA. While offering parallel handling of AML and OPC UA, certain emphasise was put on a simple but coherent user interface, as outlined in Fig. 4. Users can connect to an

Fig. 4. Top: AML node with attributes; below: OPC UA node tree with “collectable variables”.

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OPC UA server, and the tree view offers access to all structural elements (nodes in OPC UA) in the same manner as they can relate to AML. Parameter tuple (attribute name + value) are displayed in tables while selecting an element of the structure. A direct transfer from OPC to AML is supported on the attribute level; the users can select all attributes of interest and add them to a variable collection. The collection can be added below any hierarchical element. This way, simply by clicking and collecting, users can re-group OPC information in an AML structure (e.g. temperature parameters from OPC node x, OPC node y, etc., are added to a single AML internal element. Telegon also provides features to call OPC methods, and in case of results (provided by an OPC UA method), those results can be stored in an AML file as well. Again such information can be used to monitor the shop floor, e.g. to initiate specific actions on a production cell. Telegon itself takes responsibility for semantic mapping between AML and OPC UA. The implementation refers to OPC mapping recommendation as specified in OPC 30040: AutomationML [24]. The subscription to monitored items (e.g., continuously monitored temperature values) adds (near) real-time information to the collectable information sets in AML. Monitored items are collected in individual tables, constantly updating with incoming data. (Copying table sections is currently under development and will soon be added to the software prototype) (Fig. 5).

Fig. 5. All OPC variables of Node CC1001 are transferred to the collection list to be handed over to AML

Next to tables, charts are provided for a visual illustration of data streams. However, streamed OPC UA information can be of any datatype (e.g., continuously sending “words”). Consequently, only a limited subset can be visualised by charts without further post-processing. Telegon’s current version allows any data stream, which can be

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converted to datatype “double”, which appears to be sufficient in our given industrial scenario. The Telegon prototype allows, in principle, digitalising the entire manufacturing process. It will enable data and information to be easily integrated and exchanged between the stakeholders. In addition, the software has been specified and implemented to be used as an integral component of a didactic demonstrator without being restricted to this lab level. For an initial validation of the envisaged scalability, the authors focus on a scenario of pipe welding with robots. Usually, pipes consist of standardised segments (flanges, elbows, linear segments, etc.) which are standardised (to fit each other). While the welding may have some specifics (since it depends on material wall thickness etc.), the general arrangement can perfectly demonstrate scalability since the inherent complexity is not coupled to the dimensions. The following table illustrates the transferability (Table 1): Table 1. Overview of scalability of the components Components

Characteristics

Scalable

Design of piping

The CAD model is based on the skeleton yes model; by selecting a predefined standard, pipe segments and components can be adapted in diameter and “material”. Automated pipe-routing is possible, which allows creating any kind of pipe between points A and B

Core parameter transfer to digital thread Telegon supports the automated yes extraction of core parameters and storage in AML. The solution supports all sizes and dimensions Core parameters to robot simulation

In its current version, for each axis point, yes the Telegon calculates four equally distributed targets by points on the cross-sectional plane on the pipe surface, allowing a robot a circular motion around the pipe cross-section. Calculated targets can be directly transferred to the robot simulation software. (Generation of targets is not restricted to “small” or “huge” pipings)

Robot simulation

The robot simulation software supports yes different sizes and brands. Path planning is auto-generated between targets and thus fully scalable if targets are defined (continued)

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Table 1. (continued) Components

Characteristics

Scalable

Robot movements

Usually, six-axis industrial robots are yes very similar to each other. Various manufacturers (including the one we have chosen) offer families of robots that are identical in structure and differ only in size and payload. The control system is entirely identical

Welding Tool/ Gripping Tool

The robot simulation software supports no /partly the design and use of self-designed robot tools and grippers as well as predefined commercial grippers (or welding tips). The commands for tool control can be defined based on I/O commands However, special efforts are required to “replicate” a realistic dummy welding tip to be used by inexperienced but behave similarly to commercial welding tips

Monitoring of process parameters

Monitoring of process parameters is yes foreseen via OPC UA; if production inventory is OPC-ready, it can be accessed. In our scenario, robots (independent of size) are OPC-ready and provide data to Telegon

4 Didactic Demonstrator Setup Robot welding and its required equipment (welding tools etc.) are commercially available and are not be part of this research [25]. However, because such hardware is expensive and relatively dangerous to use, the authors proposed a substitute for lab level trials using functional mock-ups, which offer several advantages. The size of the welding tool replica can be adapted to the robot size. In case of programming malfunctions, a broken part can be replaced, and learners can experiment with the design and arrangement of the robot tools themselves, in particular if manufactured assemblies are based on 3D printed components. Figure 6 shows a 3D-printed welding tool for a small robot as designed and assembled by the authors. This physical prototype combines 3D printed components in addition to purchased standard parts (screws etc.) The connection plate has been suited for a small robot (UR3e from Universal Robot) but can be changed by other connectors if required for other industrial robots. Since CAD has been used to model the tool geometry, the spatial model can be imported into the robot simulation, used in the same way as the commercial welding tools, and simulated in its behaviour. The mock-up does not have functionality for simulating welding itself yet. This is planned for the next version. A laser light diode (used in laser pointers) will be integrated

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Fig. 6. 3D printed welding tool replica (for simulation and demonstrator)

and connected with the robot controls, enabling robot commands to switch the laser on (and off). The authors plan that such the laser will indicate for the learners when and where the robot would generate the weld seam and how the tool moves along its path. The functional enhancement helps to understand where surfaces are tackled and how precisely the tool can be positioned and enables one to acquire basic programming skills to control robot tool behaviours. Even though it is basically an on/off command, its integration in process simulation and robot programming can be trained. Furthermore, the didactic demonstrator will support status monitoring using Telegon’s OPC UA features. It will not only monitor the robot’s position, but also other parameters such as whether it is welding, changes in velocity, or whether tolerances are acceptable. In addition, typical IoT challenges, such as latencies caused by Industry 4.0 protocols, can be illustrated and the mitigation will be discussed. The initial evaluation with a student group already confirmed some expected advantages. For example, a 3D printed robot tool tip which collided and broke due to inappropriate movement along a pipe geometry was not as costly as it would have been with its commercial physical twin. Nevertheless, the Didactic Demonstrator set-up is at the beginning of its development. So far, our primary focus is the development of Telegon to handle the data. Now that Telegon is operational, the authors can now concentrate on the demonstrator itself and work on the above-mentioned missing and/or neglected topics towards a human-centric vision.

5 Discussion and Outlook The project is still ongoing; parts of the work and building blocks are finished but are being iteratively expanded and adapted to be able to implement the holistic model. Especially the part towards the implementation of the demonstrator still needs a lot of work and processing. Developing recurring problems and a better understanding of users

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to Industry 4.0, digitisation, HRC, and the junction of these topics remain central to our work. Finally, it should be noted that our project undermines a certain dynamism, and that new ideas and possibilities are certainly taken into account; for example, there is already the idea of extending the demonstrator with a linear axis in order to increase the limited working radius of the UR3e. The next big step for us will be focusing on the human-centric approach. By starting with our work, we used the way of Industry 4.0 and put the technology in the centre. An approach we now have to work on to achieve the didactic factory with a human-centric perspective, as stated in the beginning. Also, a comparison with literature about the human-centric view and HMI and its problems with the possibilities of the demonstrator is missing and will be part of our work in the near future. Next to the set-up of the physical demonstrator, we have to adjust the needs and requirements of humans in future industrial environments to implement them in our approach.

6 Conclusion With the development of Telegon, we are able to ease monotonous work for blue-collar workers by using the digital thread approach and doing the measurement and adaption for the fitting pipes in an automated process instead of manual. Even though the demonstrator is far from finished, essential aspects can already be used. We found and described a way to depict and transform manual processes in a digital thread and work on a specific implementation through a use case. This digital thread approach will help to improve the fabrication of the fitting pipes, in one way, as it is possible to automate and digitalise the process but also by reducing the effort of manual work. Further, digitalising the entire manufacturing process allows data and information to be easily integrated and exchanged between the stakeholders. So far, we can import, adapt, and provide the input data from CAD and AML and vice-versa. There is a lot of interest in specific information, and AML offers an excellent way to transfer it. We are currently working on implementing the data from flanges measured by a 3D-measurement system into Telegon. It is also clear that we are only developed the framework, and bringing the humancentric approach more in the centre requires continuous work on the development and setup of the demonstrator, but also to dive deep into the literature to sharpen the requirements and ideas of human-centric within Industry 5.0. The human-centric topic shall be the outcome of the didactic factory. Acknowledgements. This work was supported by the European Union’s Horizon 2020 research and innovation programme under the PENELOPE project under grant agreement No. 958303 and EIT Manufacturing under the KAVA activity 21168 ENHANCE.

References 1. Supply Chain 4.0 – the next-generation digital supply chain. https://www.mckinsey.com/cap abilities/operations/our-insights/supply-chain-40--the-next-generation-digital-supply-chain (2016). Zugegriffen: 19 Mai 2023

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2. Hribernik, K., Cabri, G., Mandreoli, F., Mentzas, G.: Autonomous, context-aware, adaptive Digital Twins—State of the art and roadmap. Comput. Ind. 133, 103508 (2021). https://doi. org/10.1016/j.compind.2021.103508 3. Singh, V., Willcox, K.E.: Engineering design with digital thread. AIAA J. 56(11), 4515–4528 (2018). https://doi.org/10.2514/1.J057255 4. Transforming advanced manufacturing through Industry 4.0. https://www.mckinsey.com/ capabilities/operations/our-insights/transforming-advanced-manufacturing-through-indust ry-4-0 (2022). Zugegriffen: 19 Mai 2023 5. The Fourth Industrial Revolution: Industry 4.0 Challenges And Opportunities For Your Business. https://stefanini.com/en/insights/news/the-fourth-industrial-revolution-industry-40-challenges-and-opp (2021). Zugegriffen: 19 Mai 2023 6. Renda, A., et al.: Industry 5.0, a transformative vision for Europe. Governing systemic transformations towards a sustainable industry. ESIR Policy Brief, No. 3. Publications Office of the European Union, Luxembourg (2021) 7. Nahavandi, S.: Industry 5.0—a human-centric solution. Sustainability 11(16), 4371 (2019). https://doi.org/10.3390/su11164371 8. Breque, M., de Nul, L., Petridis, A.: Industry 5.0. Towards a sustainable, human-centric and resilient European industry. R&I Paper Series, policy brief. Publications Office of the European Union, Luxembourg (2021) 9. Adel, A.: Future of industry 5.0 in society: human-centric solutions, challenges and prospective research areas. J. Cloud. Comput. (Heidelb) 11(1), 40 (2022). https://doi.org/10.1186/ s13677-022-00314-5 10. Kraaijenbrink, J.: What Is Industry 5.0 And How It Will Radically Change Your Business Strategy? https://www.forbes.com/sites/jeroenkraaijenbrink/2022/05/24/what-is-industry50-and-how-it-will-radically-change-your-business-strategy/?sh=525f75d120bd (2023). Zugegriffen: 19 Mai 2023 11. Industry 5.0: Adding the human edge to industry 4.0. https://www.sap.com/insights/industry5-0.html. Zugegriffen 19 Mai 2023 12. Aníbal Reñones Domínguez: Industry 5.0, seriously? https://blog.cartif.es/en/industry-5-0/ (2021). Zugegriffen: 19 Mai 2023 13. Sindhwani, R., Afridi, S., Kumar, A., Banaitis, A., Luthra, S., Singh, P.L.: Can industry 5.0 revolutionize the wave of resilience and social value creation? A multi-criteria framework to analyze enablers. Technol. Soc. 68, 101887 (2022). https://doi.org/10.1016/j.techsoc.2022. 101887 14. Fajgelbaum, P., Khandelwal, A.: The Economic Impacts of the US-China Trade War. National Bureau of Economic Research, Cambridge, MA (2021) 15. Ghobakhloo, M., Iranmanesh, M., Mubarak, M.F., Mubarik, M., Rejeb, A., Nilashi, M.: Identifying industry 5.0 contributions to sustainable development: a strategy roadmap for delivering sustainability values. Sustainable Product. Consumption 33, 716–737 (2022). https://doi.org/ 10.1016/j.spc.2022.08.003 16. Burow, K., Klein, P., Thoben, K.-D.: An approach to digitalising the manufacturing steps of large-scale components by using an industrial pilot case. In: Kim, D.Y., von Cieminski, G., Romero, D. (eds.) Advances in Production Management Systems. Smart Manufacturing and Logistics Systems: Turning Ideas into Action: IFIP WG 5.7 International Conference, APMS 2022, Gyeongju, South Korea, September 25–29, 2022, Proceedings, Part I, pp. 183–189. Springer Nature Switzerland, Cham (2022). https://doi.org/10.1007/978-3-031-16407-1_22 17. ENHANCE. strENgtHening skills and training expertise in humAN maChinE interaction. https://www.biba.uni-bremen.de/forschung/projekte/abgeschlossene-projekte.html (2021). Zugegriffen: 21 Juni 2023

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18. strENgtHening skills and training expertise in humAN maChinE interaction (Enhance). https://www.eitmanufacturing.eu/news-events/activities/strengthening-skills-and-trainingexpertise-in-human-machine-interaction/ (2021). Zugegriffen: 21 Juni 2023 19. Burow, K., Klein, P. (Hrsg.) Towards an application on how to handle digital thread information to close the gap between design and manufacturing. approved, but not published, yet (2023) 20. What is AutomationML? https://www.automationml.org/about-automationml/automatio nml/. Zugegriffen: 19 Mai 2023 21. Newnes, L., Lattanzio, S., Moser, B.R., Stjepandi´c, J., Wognum, N. (Hrsg.) Transdisciplinary Engineering for Resilience: Responding to System Disruptions. Advances in Transdisciplinary Engineering. IOS Press (2021) 22. AutomationML Tools. https://www.automationml.org/about-automationml/aml-tools/. Zugegriffen: 19 Mai 2023 23. OPC Foundation. https://reference.opcfoundation.org/ (2023). Zugegriffen: 19 Mai 2023 24. OPC 30040: AutomationML. https://reference.opcfoundation.org/AML/v100/docs/ (2016). Zugegriffen: 19 Mai 2023 25. Vectis Cobot Welding Tool. https://www.universal-robots.com/plus/products/vectis-automa tion/vectis-cobot-welding-tool/

Textile Industry Circular Supply Chains and Digital Product Passports. Two Case Studies Bjørn Jæger(B)

and Sivert Myrold

Molde University College, PB 2110, 6402 Molde, Norway {bjorn.jager,sivert.myrold}@himolde.no

Abstract. The textile industry is the second largest polluting industry in the world resulting in significant social and environmental impacts throughout its supply chain. The industry experience increased stakeholder pressure to improve sustainability. The European Commission presented the EU strategy for sustainable and circular textiles to address issues such as the social and environmental costs of fast fashion, textile waste, the disposal of unsold textiles, and unethical labor practices. The concept of a Digital Product Passport (DPP) is proposed by EU as a key action under the EU’s Circular Economy Action Plan for the textile industry to mitigate such challenges. Central to DPP implementation are textile brand owners as they operate global supply chains getting products from manufacturing plants to stores. This paper investigates to what extent textile brand-owners are aware of the coming DPP requirements and how they approach DPP implementation by posing the research question: How is the textile industry using DPPs to move towards circular supply chains? Two case studies, one of a global enterprise and a small-and-medium sized enterprise found that major efforts into the use of DPP has been done by the global enterprise, while the SME company is still evaluating how to proceed to deploy DPP in their supply chain. The results are valuable for textile companies evaluating DPP for circularity improvements, and for researchers by challenges identified for future research. Keywords: Digital Product Passport · Textile industries · Supply Chain Management · Circular Economy

1 Introduction The textile industry is the second largest polluting industry in the world with carbon emissions exceeded only by maritime transportation and international flights, resulting in significant social and environmental impacts throughout its supply chain [1–3]. The industry experience increased stakeholder pressure to improve sustainability [1, 4, 5]. In March 2022, the European Commission presented the EU strategy for sustainable and circular textiles to address issues such as the social and environmental costs of fast fashion, textile waste, the disposal of unsold textiles, and unethical labor practices [6, 7]. The circular economy (CE) offers a more sustainable alternative to the linear economy representing a fundamental shift in economic systems by decoupling economic growth © IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 350–363, 2023. https://doi.org/10.1007/978-3-031-43688-8_25

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from the consumption of the earth’s finite resources in a manner that can make the textile industry more resilient and regenerative [8]. According to de Jesus and Mendonça [8], technology innovation is an essential driver in facilitating the shift towards a CE. 1.1 Digital Product Passport (DPP) Saccani et al. [9] call for investigations on how digitalization provides benefits to overcome circular economy obstacles in the textile industry. One central digital technology innovation is the concept of a Digital Product Passport (DPP). In 2022, the EU proposed a textile DPP as one of the key actions under the EU’s Circular Economy Action Plan (CEAP) for the textile industry [7]. The textile DPP is emphasized as a strategic forward-looking action to introduce clearer information on textiles [7]. DPP is the central tool for sharing data on composition, material origin, and chain of custody providing information throughout the entire life cycle and beyond. DPP can be further extended to comprise data on social and environmental aspects of the textiles’ life cycle leading to DPP becoming a prerequisite of the transition to a circular economy [10, 11]. A DPP contains data required by stakeholders to take informed decisions with regards to [10]: 1. 2. 3. 4. 5. 6. 7.

Usage and maintenance Product identification Products and materials Guidelines and manuals Supply chain and reverse logistics Environmental data Environmental compliance

However, in practice the exchange of DPP data for closing resource loops is still found to be at a low maturity level [10]. Industrial managers should proactively reconfigure data flows to account for decision-making in a reverse supply chain. Jensen et al. [10] calls for research that explores the use of DPPs by industries. Since DPPs offers a chance to solve current problems such as fast fashion, textile waste, the disposal of unsold textiles, and unethical labor practices. Therefore, this paper investigates digitalization in the textile industry by addressing specifically the DPP technology by posing the research question (RQ): RQ: How is textile industry using DPPs to move towards circular supply chains? Specifically, to investigate the research problem we did two case studies on how the use of DPPs were perceived by the textile companies H&M Group and Skogstad Sport. The investigation was conducted as an embedded multiple case study where the primary data collection strategy was semi-structured interviews with the case companies.

2 Background The textile industry is one of the most significant industries in the global economy, with an estimated revenue of 1.53 trillion U.S. dollars in 2022 (Smith 2023). However, despite its economic importance, it is the second largest polluter in the world. Its carbon

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emissions exceed those of all maritime transportation and international flights combined, resulting in significant social and environmental impacts throughout its supply chain [2]. The textile industry’s focus on fast fashion and constant trend changes encourages people to dispose of clothing that is no longer in fashion. Fast fashion is low-cost clothing that mimics luxury fashion trends, produced using a just-in-time manufacturing philosophy and quick response strategies. This allows fast fashion to take only weeks to get from the product design stage to the market. Fast fashion offers consumers a constantly changing selection of clothing, promoting impulse shopping, emotional purchasing, and a throwaway culture [12]. This trend for global textile production is a doubling between 2000 and 2015 while the average utilization of each garment has decreased over the past few decades [13]. The European Commission estimates that one truckload of textiles goes to landfill or incineration every second and less than 1% of the material used in the production is recycled into new clothing [6]. Furthermore, up to 35% of all the microplastics released into the environment can be traced back to textile products with an estimated 500 thousand tons of microplastic fibers dumped in the oceans annually [2, 6]. Despite its negative impact, the textile industry continues to operate using a linear model of extracting, producing, and disposing of resources, mainly driven by fast fashion. As a result, the textile industry is responsible for some of the most significant environmental impacts on our planet. This includes using more than 98 million tons of non-renewable resources every year, including oil for synthetic fibers, fertilizers for cotton plantations, and chemical products for dyeing and finishing fibers and fabrics. The textile industry also uses 93 billion cubic meters of water resulting in severe water scarcity in many regions around the world. Furthermore, the production releases chemicals into waterways leading to water pollution and harm to aquatic life. In addition, the textile industry is emitting an estimated 1.2 billion tons of CO2 every year [2]. The supply chains in the textile industry are among the longest and most complex of any industry, primarily because most manufacturers outsource production to countries with lower labor costs []. Companies in the textile industry often lack complete visibility into the sources of the raw materials used in their production, making it difficult to get a complete understanding of their environmental and social impact. As a result, companies may unknowingly use materials produced with unethical practices such as forced labor. The lack of visibility has resulted in a widespread problem with worker exploitation, with the industry facing significant challenges regarding low wages, poor working conditions, and limited workers’ rights [14]. 2.1 EU Strategy for Sustainable and Circular Textiles In March 2020 the European Commission adopted the circular economy action plan as one of the main building blocks of the European Green Deal. The European Green Deal is the EUs roadmap to becoming the first climate-neutral continent through transforming the EU into a modern, resource-efficient, and competitive economy [6]. In March 2022 the European Commission presented a proposal as part of the circular economy action plan to make sustainable products the norm in the EU. The commission proposed new rules to make almost all physical goods on the EU market more sustainable, circular, and energy efficient throughout their whole lifecycle. Included in this package was the EU strategy for sustainable and circular textiles [7]. This strategy outlines the European

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Commission’s plan to make all textile products more durable, repairable, reusable, and recyclable, and to a great extent made of recyclable fibers and free of hazardous chemicals by 2030. The strategy also includes tackling fast fashion to create long-lasting, high-quality textiles, make profitable re-use and repair services widely available, and ensure good working conditions and fair wages for production workers [7]. The European Commission outlines several key actions in the textile strategy including setting design requirements for textiles, tackling overproduction and overconsumption, and incentivizing circular business models. However, one of the key actions outlined in the textile strategy is very relevant to the objectives of my research, namely the introduction of digital product passports in the EU market [6]. 2.2 Digital Product Passport (DPP) A DPP collects and stores product-related data throughout a product’s life cycle and shares it across the entire supply chain [18]. The European Commission wants to use DPPs as a tool to enhance transparency on products sold in the EU market to promote the shift towards a circular economy. DPPs have the potential to make the environmental and social impact of products visible, traceable, and easily accessible, enabling companies to create more circular products and reduce waste and resource consumption. By linking performance requirements to the DPP data, the European Commission aims to incentivize circularity. DPP regulations are currently being drafted and the European Commission plans to make DPP mandatory on all textile products sold in the EU market by 2030 [4]. One of the key questions when it comes to the implementation of DPPs is concerning data storage. First, it needs to be decided whether data storage should be EU- or company-managed. After this, the question is whether to store data centralized or decentralized. Centralized storage options, such as cloud storage, offer significant benefits due to wide use, ease of implementation, and low cost. Decentralized options such as the blockchain have a high cost and novelty but allow for higher data security, transparency, and traceability [4]. One of the key questions when it comes to the implementation of DPPs is concerning data storage. First, it needs to be decided whether data storage should be EU- or company-managed. After this, the question is whether to store data centralized or decentralized. Centralized storage options, such as cloud storage, offer significant benefits due to wide use, ease of implementation, and low cost. Decentralized options such as the blockchain have a high cost and novelty but allow for higher data security, transparency, and traceability [4]. Textile reverse supply chains can be divided into closed-loop or open-loop recycling supply chains [25]. Closed-loop recycling indicates that the recycled material that is gained from the original textile product can be used in an identical product with similar quality properties, meaning that the raw material only moves in a closed circle through the circular supply chain. Open-loop recycling indicates that the recycled material’s properties differ from the properties required for the original purpose, and the recycled material is therefore utilized in other types of products [25]. A natural actor to introduce the DPP in the textile supply chain is by the brand-owner/retailer since the brand-owner has the intellectual property rights (IPR) and is controlling the products supply chain.

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An example of textile circular supply chain with DPP-responsible indicated is illustrated in Fig. 1.

Fig. 1. Example of textile circular supply chain with DPP-responsible indicated (adapted from [25])

3 Method To answer the research question, case studies of two textile industry actors have been conducted. Case study research is suitable for this research [19, 20] since: (1) DPP approaches can be studied in its natural settings, which can both generate new theory and modify existing theory regarding DPP in supporting a transition towards circularity, and (2) Answering “how” questions allow us to fully understand DPP approaches from the perspective of brand owners of various size. Data was collected by carrying out digital meetings with different representatives from the SME company and for the global actor e-mail interview was the main interrogative approach, supplemented with collected secondary data from business documents as described in Table 1.

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4 Case Description 4.1 Case Company 1: H&M Group H&M Group was founded in Sweden in 1947 by Erling Persson. H&M Group initially focused on selling women’s clothing, but after a decade the company expanded into Norway and began offering men’s clothing as well. Today, H&M Group has grown to become the world’s second-largest fashion company. The group comprises several brands, including H&M, Afound, ARKET, COS, H&M Home, Monki, & Other Stories, Weekday, and the latest addition, H&M Move. As of 2021, H&M Group has over 4,800 stores in 77 markets worldwide. H&M Group focuses on omnichannel sales and online shopping is available in 57 countries. H&M Group directly employs more than 100,000 people [15]. 4.2 Case Company 2: Skogstad Sport AS Skogstad is one of Norway’s leading brands of outdoor and sportswear. Since its inception in 1937 by Halstein Skogstad, the company has been committed to producing highquality and functional clothing for the whole family. Skogstad being located in Innvik, Norway, has established distribution in several countries. Skogstad directly employs 101 people and had a revenue of 174 million NOK in 2021 and is thus considered a mediumsized enterprise. The brand’s core values are centered on environment sustainability, ethical business practices, and product design, quality, and functionality. Their vision is to create outdoor joy with quality products at a reasonable price [16]. 4.3 Data Collection The data collection approach in this research was semi-structured interviews conducted either by MS Teams or by e-mail. In addition, the companies provided relevant records and reports. For Skogstad, an interview guide was provided before the interview to allow them to prepare their responses and seek input from others within their organization if necessary. The interview focused on the case company’s current supply chain management, its measures concerning sustainability and circular economy, and its views on the potential of technology to enhance sustainability. The interview took place in February over Teams and lasted around one hour. The interview was recorded with the permission of the respondents and in compliance with privacy regulations. The interviews were transcribed and sent to the case company giving them a chance to clear up any misunderstandings and provide additional information. After the interview was transcribed, follow-up questions were sent by mail to both H&M Group and Skogstad [17].

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Skogstad Jan. 17

Skogstad home page: Om Skogstad (about Skogstad) https://skogstadsport.no/ om-skogstad-sport/

Feb. 3

Interview by MS Teams (1 h). Participants: Logistical manager and Sustainability manager (Logistikkansvarlig og bærekraftsansvarlig)

Feb. 3

Follow up questions by e-mail. Sustainability manager (bærekrafts ansvarlig)

Apr. 18

Email inquiry on DPP, Sustainability manager (bærekrafts ansvarlig)

H&M Feb. 13

Interview by e-mail on circular supply chains, traceability, and transparency. H&M Group Sustainability Department

Feb. 14

Business communication. H&M Group. 2021. H&M Group: Sustainability Disclosure 2021. https://hmgroup.com/wp-content/uploads/2022/03/HM-GroupSustainability-Disclosure-2021.pdf

Feb. 14

Business communication. H&M Group. 29 April 2022. H&M Group expands partnership with TextileGenesis, News Article. https://hmgroup.com/news/hmgroup-expands-partnership-with-textilegenesis/

Feb. 14

Business communication. H&M Group. 2023. Supply Chain. https://hmgroup. com/sustainability/leading-the-change/transparency/supply-chain/

Mar. 18

Interview by e-mail with H&M Group Sustainability Department

April 25

Follow up questions by e-mail on Traceability and transparency + EU DPP, H&M Group Sustainability Department

5 Findings 5.1 Findings Case Company 1: H&M Group H&M Group’s supply chain consists of up to 6 tiers of suppliers, from raw materials to finished garments. The number of tiers in their supply chains varies depending on the product type and materials used, ranging from a few tiers for certain products to several tiers for others, see Fig. 2. Noticeably, H&M Group does not own any factories themselves. The first tier in H&M Group’s supply chain comprises suppliers who are responsible for manufacturing and processing the products. Their tasks include garment washing, dying, cutting, sewing, and finishing the products. The second to fourth tiers consist of companies that specialize in component production and processing. These companies are responsible for yarn spinning, knitting, and producing trims such as buttons, zippers, Velcro, and labels. The fourth to sixth tiers in H&M Group’s supply chain involve raw material production. This includes everything from farmers cultivating and extracting raw materials to cotton grinning and fiber production [18]. H&M Group does business with more than 602 commercial product suppliers who manufacture products for the H&M Group brand across 1519 tier-one factories situated in Europe, Asia, and Africa. China and Bangladesh are the primary production markets

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Fig. 2. H&M Group’s upstream supply chain [18]

for H&M Group’s clothing. On average, H&M Group maintains supplier relationships for eight years. However, some suppliers have been doing business with H&M Group for more than 25 years. H&M Group aims to establish long-term relationships with its suppliers that are built on mutual trust and transparency. Currently, H&M Group’s suppliers employ approximately 1.5 million people [18]. H&M Group, like most fashion brands, do not make fashion, they buy fashion. H&M Group designs its products in-house and outsources the production. H&M Group are aware that their buying behavior and standards can impact suppliers’ practices and working conditions. Therefore, they are committed to developing responsible purchasing practices. H&M Group is a founding member of ACT, along with 18 other brands. ACT was established in 2014 to promote responsible purchasing practices and outlines five commitments: 1) including wages as itemized costs in purchasing prices, 2) offering fair payment terms, 3) improving planning and forecasting, 4) providing training on responsible sourcing and buying, and 5) implementing responsible exit strategies [18]. To reduce their environmental impact, H&M Group aims for 100% recycled or more sustainable sourced materials by 2030 and 30% recycled materials by 2025. In 2021 H&M Group achieved 17.9% recycled material and 80% more sustainable sourced materials. This was a tripling of recycled materials from the previous year and was largely due to increased volumes of recycled cotton and polyester. H&M Group bases its material selection on third-party lifecycle assessment data and benchmarks [19]. H&M Group has the world’s largest garment collecting program and has been operating this initiative since 2013. “With our size comes responsibility. The way fashion is consumed and produced today is not sustainable. We have to transform the industry we are in. Our ambition is to transform from a linear model to become circular” – Head of Sustainability. H&M Group, as a major player in the fashion industry, wants to be a leader in the transition from a linear economy to a circular economy. H&M Group joined the Ellen MacArthur Foundation as a strategic partner in 2015. In 2016, H&M group announced its ambition to become a circular business to tackle fast fashion, textile waste, the disposal

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of unsold textiles, and unethical labor. H&M Group aims to design all products for circularity by 2025 and become climate positive by 2040 [15]. H&M Group has developed a strategy for its shift towards a circular economy that comprises three pillars: circular products, circular supply chains, and circular customer journeys. Under the pillar of circular products, H&M Group aims to create durable products from sustainable and recycled materials that can be recirculated several times. With circular supply chains, H&M Group emphasizes establishing supply chains that recirculate products and support circular production processes and material flows. As for circular customer journeys, H&M Group focuses on providing accessible ways for customers to engage in circular fashion, including repairing, reusing, and recycling products [15]. In the past, H&M Group has had a silo mentality by focusing on becoming circular in each stage of their supply chain separately. However, they eventually recognized that to reach their circular ambitions they need to redesign every stage of their products’ life cycle and take a more holistic approach [19]. H&M Group views design as their first opportunity to enable more circular products and as mentioned earlier they aim to design all their products for circularity by 2025. H&M Group has aligned its circular product development tool, Circulator, with the Ellen MacArthur Foundation’s circular economy vision. H&M Group’s Circulator tool supports product teams to design products with more sustainable sourced materials and products that are more durable and/or recyclable, enabling them to remain in circulation longer [19]. Regarding the EU digital product passport (DPP) directive, H&M Group is an advocate of increased traceability and views it as a vital component of their supply chains and an enabler of the circular economy. H&M Group welcomes the EU’s initiatives for implementing digital product passports (DPPs) and are currently working towards integrating DPPs in their supply chains. H&M Group aim to have all product-related data digitally available for their customers. In their efforts of implementing DPPs they recognize the current challenges connected to the DPPs and are foreseeing a transition period to allow the EU as well as the textile industry to set up the needed infrastructure [20]. 5.2 Findings Case Company 2: Skogstad Sport AS Skogstad operates an office in China, which was established by the current CEO who lived there for more than a decade. This office comprises nine employees who work closely with all their first-tier suppliers as Skogstad has built their supplier base around that office. As a result, the company has a significant advantage in terms of insights and follow-up with their suppliers in China. All Skogstad’s production takes place in China, and they have established long-term relationships with 17 first-tier suppliers as of 2022. Representants from Skogstad travel to China tree to four times a year to review the suppliers and maintain a strong relationship. Through these visits, they gain valuable insights into the manufacturing of their products and can identify opportunities for improvement. Skogstad strives for long-term relationships with all their suppliers, some of whom have been with the company for over a decade, with an average of more than three years. Furthermore, Skogstad invests in their suppliers, having, for example,

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assisted in the development of a factory to meet the Nordic Swan Ecolabel standards [21]. To ensure the smooth operation of its supply chain, Skogstad’s China office closely monitor their suppliers and ensure that the containers are loaded and transported from the ports in China. Skogstad primarily ships their products by sea to reduce emissions but may occasionally use air transportation when time is of the essence. Skogstad places a strong emphasis on cross-docking, which their China office implements to consolidate shipments from multiple suppliers into one container, to avoid shipping partially filled containers [21]. To differentiate and prioritize shipments, Skogstad utilizes white and brown cardboard boxes for packaging their products. White boxes are used for customer pre-orders that are shipped directly to the customers, while brown boxes are used for products shipped to their warehouse in Innvik, Norway. This visual distinction makes it easy to identify white boxes for sorting to the customer and brown boxes for direct delivery to their warehouse [21]. “Sorting the white and brown cardboard boxes serves a purpose in our crossdocking operations. For instance, if our supplier has a shortage of 1000 zippers and can only deliver 4000 fleeces out of an order of 5000, we prioritize shipping the white boxes. This ensures that customers receive their pre-ordered items on time, while the delayed items are sent to our supplementary warehouse” – Logistic Manager. [21] Skogstad has good control over their shipments as their suppliers in China use the same web-based packaging system as Skogstad uses in Norway. By scanning every product, Skogstad can easily track their location and content throughout the supply chain. This ensures a high level of traceability and transparency in their transportation process. Skogstad is in the process of implementing a new system in their warehouse that will strengthen its control over its product traceability. Their current system provides full control over their products from their suppliers in China to the ports in Ålesund or Oslo. The new system will enhance this capability by scanning every box upon its arrival at their warehouse in Innvik. This will enable them to track the precise location of each box in their inventory [21]. Skogstad has control over their first and second-tier suppliers, and they are now focused on increasing traceability back to suppliers that provide them with raw materials. Skogstad has already achieved traceability back to their raw material suppliers of wool. Skogstad has access to documentation that provides information about the farm and cultivation methods of wool. Their suppliers are required to disclose information about their subcontractors, which allows them to obtain this data. Skogstad’s China office plays a vital role in this process. Skogstad’s first-tier suppliers are required to enforce the same set of requirements on their own suppliers as those imposed by Skogstad. To promote transparency and traceability across the entire supply chain, Skogstad employs two management documents: a policy for sustainable business practices that outline minimum requirements for their suppliers and a guideline document for suppliers that outlines the requirements Skogstad can impose and what the suppliers can impose on Skogstad [21].

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Regarding the EU digital product passport (DPP) this is not something Skogstad has looked into yet. However, they are aware that the EU is planning to make DPP mandatory on all textile products sold in the EU by 2030. Skogstad is focused on ensuring transparency and traceability in their supply chains and is positive to any development in this area [22]. 5.3 Summary of Findings of Case Company 1 and 2 The findings are summarized in Table 2. Table 2. A summary of findings for DPP 1

General by EU [4] • DPP is a first-of-its-kind regulatory circularity tool to create transparency by collecting and storing product data throughout a product’s life cycle and sharing it across the entire supply chain • EU strategy to make (DPP) mandatory on all textile products sold in the EU by 2030 • The DPP is a first-of-its-kind regulatory circularity tool to create transparency by collecting and storing product data throughout a product’s life cycle and sharing it across the entire supply chain • A key question to implement DPP is data storage

2

By Case Company 1: H&M Email inquiry [2] • H&M advocate increased traceability and Secondary data from H&M [23] welcomes the EU’s initiatives for implementing digital product passports (DPPs) in that regard • H&M is currently working towards integrating DPPs in their supply chains • H&M aims to have all product-related data available in DPPs for their customers • H&M recognize current challenges for DPPs • H&M foresee a transition period to allow EU and the textile industry to set up the needed infrastructure

3

By Case Company 2: Skogstad Email inquiry [22] • Skogstad has not investigated DPP in depth yet • Skogstad is aware of the EU plan to make DPP mandatory on all textile products sold in the EU by 2030 • Skogstad is focused on ensuring transparency and traceability in their supply chains and is positive to any development in this area

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6 Discussion and Conclusion Both H&M Group and Skogstad advocate for increased transparency in their supply chains, and they are positive to the DPP legislation initiative by EU. Skogstad, being an SME, is aware of DPPs but has not discussed how to implement it yet. H&M Group, being a global enterprise, are already working on integrating DPPs in their supply chains. This might be due to DPP being part of the digitalization trend requiring a skilled workforce with competences typically carried by large enterprises with resources to implement DPP [21]. For SMEs this might not be feasible due to capacity and specialist knowledge limitations [22]. Related to the data storage problem of DPPs, H&M is seeing blockchain as a potential technical solution. H&M Group stated that they are waiting to see what the EU recommends regarding technical solutions for data storage. One interesting trend towards handling the cross-actor challenges for data storage in complex global supply chains is the increasing use of blockchain technology [23]. Digitalization can be an important tool for the investigations of how actors comply with their strategies for circularity in practice as shown by the recent example of using air-tags to track secondhand garments throughout the reverse supply chain of H&M [24]. Using such tracking technology combined with DPP enables journalists, Non-Governmental Organizations (NGOs) and others to independently verify how textiles are handled after the consumer dispose the textiles. This can be seen as a strong driver for companies to implement DPPs [24]. In conclusion, the use of digital product passports (DPPs) is seen as a key action under the EU’s Circular Economy Action Plan for the textile industry. This paper investigated How the textile industry uses DPPs to move towards circular supply chains by two case studies. One of a SME textile brand owner and retailer, and the other case study of a global textile brand owner and retailer. We found that the global enterprise has made major efforts into the use of DPP while the SME company is aware of the coming DPP requirement, but they are still evaluating how to proceed to deploy DPP in their supply chain. Our research suggests that SMEs should increase digitalization efforts to overcome circularity challenges. For both global enterprises and SMEs digitalization in which DPP is a central component should be further investigated especially in expectation of the evolving use of artificial intelligence (AI) combination with distributed storage technologies like the blockchain.

References 1. Degenstein, L.M., McQueen, R.H., Krogman, N.T., McNeill, L.S.: Integrating product stewardship into the clothing and textile industry: perspectives of New Zealand stakeholders. Sustainability 15(5), 4250 (2023) 2. de Aguiar Hugo, A., de Nadae, J., da Silva Lima, R.: Can fashion be circular? A literature review on circular economy barriers, drivers, and practices in the fashion industry’s productive chain. Sustainability 13(21), 12246 (2021) 3. Morlet, A., et al.: A new textiles economy: redesigning fashion’s future. Ellen MacArthur Foundation. http://www.ellenmacarthurfoundation.org/publications. Last accessed 19 Jun 2023

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4. The EU digital product passport shapes the future of value chains: what it is and how to prepare now. World Business Council for Sustainable Development. https://www.wbcsd.org/ contentwbc/download/15584/226479/1. Last accessed 19 June 2023 5. State of Supply Chain Sustainability. https://sscs.mit.edu/wp-content/uploads/2022/07/MITCTL-State-Supply-Chain-Sustainability-2022.pdf. Last accessed 19 June 2023 6. Sustainable and circular textiles by 2030. European Commission. Publications Office of the European Union. https://environment.ec.europa.eu/strategy/textiles-strategy_en. Last accessed 19 Jun 2023 7. European Commission. COM 141 Final. EU strategy for sustainable and circular textiles, https://environment.ec.europa.eu/strategy/textiles-strategy_en (2022). last accessed 19 Jun 2023 8. de Jesus, A., Mendonça, S.: Lost in transition? Drivers and barriers in the eco-innovation road to the circular economy. Ecol. Econ. 145, 75–89 (2018) 9. Saccani, N., Bressanelli, G., Visintin, F.: Circular supply chain orchestration to overcome circular economy challenges: an empirical investigation in the textile and fashion industries. Sustain. Prod. Consum. 35, 469–482 (2023) 10. Jensen, S.F., Kristensen, J.H., Adamsen, S., Christensen, A., Waehrens, B.V.: Digital product passports for a circular economy: data needs for product life cycle decision-making. Sustain. Prod. Consum. 37, 242–255 (2023) 11. Walden, J., Steinbrecher, A., Marinkovic, M.: Digital product passports as enabler of the circular economy. Chem. Ing. Tec. 93(11), 1717–1727 (2021) 12. Wang, B., Luo, W., Zhang, A., Tian, Z., Li, Z.: Blockchain-enabled circular supply chain management: a system architecture for fast fashion. Comput. Ind. 123, 103324 (2020). https:// doi.org/10.1016/j.compind.2020.103324 13. Gueye, S.: The trends and trailblazers creating a circular economy for fashion. https://ellenmacarthurfoundation.org/articles/the-trends-and-trailblazers-creating-a-cir cular-economy-for-fashion. Last accessed 19 Jun 2023 14. Bush, J., Michael C.: Forward Thinking on the sustainability revolution in textiles and the fashion industry with Edwin Keh. https://www.mckinsey.com/industries/retail/ourinsights/forward-thinking-on-the-sustainability-revolution-in-textiles-and-the-fashion-ind ustry-with-edwin-keh (2022). Last accessed 19 Jun 2023 15. Ellen MacArthur Foundation. A journey to becoming 100% circular and climate positive: H&M Group. https://ellenmacarthurfoundation.org/circular-examples/hm-group. Last accessed 19 Jun 2023 16. Skogstad. 2023c. Om Skogstad. https://skogstadsport.no/om-skogstad-sport/. Last accessed 19 Jun 2023 17. Myrold, S.: Exploring the potential of blockchain-based circular supply chains in the textile industry. A Case Study of H&M Group and Skogstad Sport, Master’s degree thesis, Molde University College, Norway (2023) 18. King, M.R., Timms, P.D., Mountney, S.: A proposed universal definition of a Digital Product Passport Ecosystem (DPPE): worldviews, discrete capabilities, stakeholder requirements and concerns. J. Clean. Prod. 384, 135538 (2023) 19. Meredith, J.: Building operations management theory through case and field research. J. Oper. Manag. 16(4), 441–454 (1998) 20. Voss, C., Tsikriktsis, N., Frohlich, M.: Case research in operations management. Int. J. Oper. Prod. Manag. 22(2), 195–219 (2022) 21. Wiegand, T., Wynn, M.: Sustainability, the circular economy and digitalisation in the German textile and clothing industry. Sustainability 15(11), 9111 (2023) 22. Pal, R., Jayarathne, A.: Digitalization in the textiles and clothing sector. In: The Digital Supply Chain, pp. 255–271. Elsevier (2022). https://doi.org/10.1016/B978-0-323-91614-1.00015-0

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Product and Asset Life Cycle Management for Sustainable and Resilient Manufacturing Systems

Green Design: Introducing a New Methodology to Increase Environmental Sustainability in Capital Investments at AstraZeneca Filip Magnusson1(B) , Mikael Bohman1 , and Monica Bellgran2 1 AstraZeneca, Sweden Operations, Södertälje, Sweden

[email protected] 2 KTH Royal Institute of Technology, Södertälje, Sweden

Abstract. The concept of environmentalism is constantly growing as new customer demands and regulations are introduced. Preventive measures on production system design are lacking as the environment is being overlooked in capital investment projects of production equipment and systems. The objective of this study was to explore the need for environmental sustainability in capital investment projects and develop a methodology to implement resource efficiency and circularity. It is identified that the design phase of capital investment projects has a great opportunity for decreasing environmental impact in future operations. However, strategy deployment of environmental sustainability to project level is lacking, justifying the urgency for additional environmental approaches. The Green Design methodology developed introduces environmental sustainability to capital investment projects by utilizing several green-lean and circular tools. The tools are applied through identifying, evaluating, implementing, and follow-up on environmental improvements. Keywords: Green Design · Capital Investment · Production Equipment · Production System Design

1 Introduction As the sustainability concept is increasing worldwide, the new environmental paradigm to reduce negative environmental impact implies targeting climate neutral products to be sold on the world markets. However, as the environmental impact share from products gradually decreases, the share from production consequently increases. The production share motivates more focus to be put on designing environmentally sustainable production systems from the start, as found crucial by e.g., [1]. Industrial production continues to increase. A 50% decrease in production resource use is expected by 2050, whereas industrial output is expected to double by the same time [2, 3]. This half resource-double output projection suggests need for increasing production capacity while also motivating industries in becoming more environmentally sustainable regarding production (i.e., not only products). One essential part of environmentally sustainable production systems is © IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 367–381, 2023. https://doi.org/10.1007/978-3-031-43688-8_26

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the invested hardware assets. These are to be designed and operated with a life cycle perspective, hence in a resource efficient and circular way. Approximately 80% of a product’s life cycle cost, connecting also to most of the environmental impact, is determined in the early design stages [4]. This statement suggests that only 20% of environmental impact is possible to decrease during the use phase (operations). Not taking assets into account when designing a production system increases the risk of embodied linearity. Instead, circularity is now required to manage cutting resource use while simultaneously increasing output. The objective of this study was to explore the need for environmental sustainability in capital investment projects and develop a methodology to implement resource efficiency and circularity. This was fulfilled by firstly exploring the current state of environmental sustainability inclusion on capital investment project level and its relation to corporate sustainability strategies at a pharma company. Secondly, by acquiring knowledge on what approaches could be used to increase environmental sustainability through the design phase of capital investment projects. This study was limited to capital investment projects connected to production system design, i.e., equipment, systems, construction, media, and utilities. Other projects related to supply chain, IT, etc. were excluded.

2 Related Research This chapter presents the subjects studied through a pre-study and literature review. Focus is on environmental sustainability inclusion in corporations and on operations level, the interconnection between production system design and capital investments, and environmental approaches suitable for production. 2.1 Corporate and Operational Sustainability Organizations of today are being both externally pressured and internally motivated into taking a more proactive role to adjust towards sustainability in all areas of their businesses. Emerging regulations and consumer environmentalism are identified as two major factors for change [5]. New business models and strategy adjustment towards environmental processes are also required [5]. Production industries have great opportunities in reducing their environmental impact due to being resource and capital intense [6]. As part of operations, large waste generation is a main driver for change [7]. Also, the rising cost on energy and material is becoming a large motivator together with the dependency on non-renewal resources where alternatives are required [6, 7]. If environmental goals are not implemented in corporate strategies, companies are not willing to introduce environmental design activities and dedicate time on those activities [2]. Multiple large Swedish companies are implementing sustainability goals on corporate level, for example by adhering to ISO14001, initiatives on CO2 reduction by an x percentage by year y, and so on [8]. The goals set are primarily focused on production performance. Green Performance Map (GPM) was developed based on lean methods to improve environmental performance in production. GPM focus on identifying and improving ingoing and outgoing parameters to a production system. Parameters studied are for

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example energy, productive material, emissions, and waste [9]. The purpose is to enable communication and less complex environmental work, and support environmental continuous improvement (Green Kaizen) [9, 10]. The method (called Green Kaizen) is today a global standard at several manufacturing and production companies, including AstraZeneca. 2.2 Production System Design and Capital Investments This paper specifies production system as an interacting combination of people, material, machines, tools etc. designed to work together for a common purpose [11]. Production system design is generally down-prioritized due to it being considered time- and resource consuming [12]. Considering the 80% of embodied environmental issues (commonly embodied carbon) connected to the product design phase [4], it can be anticipated that production system design is essential to reduce emissions and resource consumption during operations. Changes to an up-and-running production system suggests limitations due to the hindsight costs and time restrictions [12]. Production system design and alteration is a task that belongs to the Production Engineering Department executing capital investment projects on equipment, systems, construction, media, and utilities. These projects typically follow a company specific guide based on the production system life cycle [13, 14], see Fig. 1.

Pre-study

Purchasing and Development

Assembly and Installation

Start-up

Production

Fig. 1. The production system development life cycle

Production system efficiency is necessary to realize customer needs and to stay competitive in a changing market [13]. Not only is operational efficiency necessary, but also the environmental efficiency is critical to stay competitive where customer awareness and requirements increase towards sustainability throughout the product life cycle [15]. Environmentally sustainable production system design implies focusing on the entire life cycle of products including supply chains, equipment, maintenance etc. A holistic life cycle perspective on production system design allows for identifying weak links where focus should be on decreasing the highest environmental impact. The objective when designing a production system to be environmentally sustainable is to increase the environmental performance (e.g., reduced amount of material, increased length-of-life), and minimizing resource and energy consumption [16, 17]. However, a lack in awareness and knowledge of implementation of environmentally sustainable design is identified [2]. Companies generally have difficulties in modifying design activities into environmental design activities due to the extra amount of time needed [2]. Despite a significant number of environmental design approaches available, companies lack practical implementation and use [2]. The steps of the production system development life cycle, as illustrated in Fig. 1, follows a generalized project model on capital investment. Fig. 1 suggests that production system design is achieved through capital investments in industry. Capital being the

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references to installed/constructed equipment, machines, tools, infrastructure, and facilities. It is suggested that more equipment is being procured and installed in industry due to increased automation and digitalization [18]. Environmental regulations together with resource depletion demands environmental production and waste elimination to enable circular investments [19]. Industrial production organizations, being a giant resource consumer, are required to increase knowledge in environmental design [20]. Increased knowledge suggests increased sustainability and circularity in capital investment is required to manage the environmental paradigm shift together with the digital paradigm shift [18, 20]. Hence, environmental sustainability must be included into traditional capital investment projects and aligned with incentives of profitability and operational efficiency. Scope 3 emission reporting and EU’s Taxonomy are other incentives for transforming into environmentally sustainable capital investments. Scope 3 emissions are emissions as result from assets not controlled or owned by the producing and reporting company. Thereby indirectly affecting organizations value chain impact [21]. The EU Taxonomy is a classification system to bring incentive on organizations’ environmental capital expenditure to increase the degree of environmental sustainability in investments. The increase is made by stimulating companies to measure environmental costs of their business and profits derived from environmental capital expenditure [22]. 2.3 Green-Lean and Circular Economy The literature review conducted disclosed two major subjects associated with inclusion of environmental sustainability in production context: green-lean and circular economy. Within these two, several approaches were identified as appropriate for utilizing in the design phase of capital investment projects. Lean manufacturing, as considered a large influence in the production paradigm, focuses on increasing productivity through waste elimination [15]. Companies need not only focus on improving productivity, but also environmentally. The integration of environmental sustainability, or ‘green’, and lean is increasing to include full potential of waste handling [15, 23]. Several green-lean approaches exist to increase sustainability together with productivity in industry. The define-measure-analyze-improve-control (DMAIC) method is suggested to improve production processes by eliminating defects, where environmental impact is considered a defect [24, 25]. Several approaches were proposed for identifying, evaluating, and assessing environmental impact for decision making and optimizing processes. Among them, three methods were identified as appropriate for this study. Life cycle assessment (LCA) to quantify environmental impacts of a process/equipment [25, 26]. Eco-efficiency indicators to classify areas of environmental impact [26, 27]. Environmental value stream mapping (E-VSM) to map eco-efficiency indicators for increased understanding of major environmental impacts and thereby prioritization [28, 29]. However, it is identified in research that extensive time consumption is seen as a major drawback when utilizing these approaches. This drawback must be compensated by realizing the opportunities with environmental impact and waste handling [25]. Preventing waste in total is identified by Ellen MacArthur Foundation [30] as the next sustainable paradigm shift. This shift will challenge the conventional ‘take-make-waste’

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handling of resources by recirculating resources back into the production system through circular economy [30]. The concept of circular economy builds upon three principles, (1) eliminate waste and pollution, (2) circulate products and materials, and (3) regenerate nature [30]. The literature review disclosed approaches for supporting the three circular principles through efficient use of resources and materials, and waste reduction. Four approaches were identified as appropriate for this study. Waste analysis for analyzing types of wastes included in a process/project and their opportunities for avoidance and recirculating [31]. End-of-life decision making by determining type of recirculation based on functionality of material/equipment [32]. Product-service systems introduces a leasing-based agreement on equipment where the supplier has a clear takeback initiative [33]. The reduce-reuse-recycle (3R) principle suggests a hierarchical model for design decisions where reducing material use is priority [34]. Introducing resource optimization and waste reduction aligned with circular economy is highlighted to stay competitive in production industry [34]. It can also be argued required to achieve the half resourcedouble output projection in industry [2, 3].

3 Methodology This study was carried out primarily as a case study at AstraZeneca Södertälje, Sweden. A first version of a methodology on implementing environmental sustainability in capital investment projects had been developed the previous year at AstraZeneca. To establish the foundation for the industrial case study, a pre-study was conducted involving search for key literature connected to relevant topics (see Sect. 2, Related Research). After conducting the pre-study and setting the scope for the study, an interactive case study was conducted at the case company. In addition, interviews were made with three selected large Swedish producing companies to get more insights of the problem area on the extent sustainability is included into corporations. The study was designed as an interactive case study consisting of multiple units of analysis. The purpose was to investigate both academic research and multiple companies’ environmental work to produce approaches that could be implemented at the case company. Empirical data was collected through several semi-structured interviews, analysis of internal company documents, observations, informal discussions, and participation in workshops at the case company where the new methodology was tested. The empirical data collection record of the case study is presented in Table 1. For confidentiality reasons, no other companies than AstraZeneca are disclosed. In addition, a structured literature review was conducted based on the pre-study to identify green-lean and circular economy approaches suitable for production system design. The literature review was conducted extracting academic references from external databases using search strings. Keywords connected to green-lean and circular economy as identified during the pre-study were used in the search strings, e.g., greenhouse gas emissions, production, cradle-to-cradle, and equipment. A total of 57 references were identified and analyzed as part of this study with a majority within the 2010-current time range. Inductive thematic analysis was used in the study because of its relevance when analyzing large amount of qualitative data [35], and because of the nature of interviews

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Code

Type of industry Type of study

No. of interviews

Interviewee position

AstraZeneca

Pharmaceutical

Interview, workshop, document analysis

12

Front-end study manager, project manager, process engineer, associate director of procurement, engineer manager, environmental compliance lead, sustainability lead, senior lean coach, industrial PhD

University

Academia

Workshop

3

Researchers

Company A

Automotive

Interview

2

Expert environmental sustainability, sustainability engineer

Company B

Automotive

Interview

1

Property- and environmental manager

Company C

Machine components

Interview workshop

1

Knowledge development manager

being semi-structured. The analysis was divided accordingly to the research methodologies used. The pre-study was conducted through gathering of key articles and a backwards snowballing method. References from the pre-study and literature review were thematically coded into themes. These concerned corporate sustainability, production system design, green design, capital investments and acquisitions, requirements and specifications, green-lean approaches, and circular economy. The data collected through the case study was coded into themes concerning environmental goals, circularity, green tools, goals on investment, critical factors for investment, and green requirements. The data found through empirical research and literature review was used to construct the conceptual methodology for inclusion of environmental sustainability in capital investment projects. The development of the methodology was realized through a plando-check-act (PDCA) cycle where concepts were elaborated and verified by employees with typical roles in capital investment projects through workshops.

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4 Empirical Results 4.1 Corporate Sustainability Inclusion in Studied Companies The case company, as well as the complementing companies selected for the study, had implemented sustainability strategies on corporate level to reduce emissions, waste, energy etc. up to a future point in time. However, only one company demonstrated having a goal or KPI for capital investment projects as part of their environmental sustainability strategy. None of the four companies disclosed environmental sustainability to be a critical factor when deciding on capital investments. See Table 2 for a summary of their environmental sustainability inclusion. Table 2. Environmental sustainability inclusion at companies interviewed Code

AstraZeneca

Company A

Company B

Company C

Environmental goals

98% reduction in CO2 emissions at own sites and fleet by 2026. 90% reduction in CO2 in entire value chain by 2045 with 10% carbon capture.

50% reduced CO2 emissions between 2015–2025 through electrified vehicles, renewable fuels, and waste elimination.

Goal on waste reduction through increased reuse and recycling initiatives.

Net zero CO2 emissions in all sites by 2030. Net zero supply chain by 2050.

Steps taken towards goal

Green Kaizen, Green Design Event, KPIs tracking emissions and resource usage.

GHG protocol for calculating climate impact. Lower energy consumption on new equipment.

Measuring material use efficiency and CO2 emissions. IMDS for new products.

Introducing hydropower, solar power, and geothermal energy. Reusing honing oil.

Goals or KPIs on capital investment projects

95% of capital Not specified*. projects involving a design phase should perform a Green Design Event.

Not implemented.

Not specified*.

Critical factors for investments

Cost, usability, safety.

Cost and quality.

Not specified*.

Cost.

(continued)

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Code

AstraZeneca

Company A

Company B

Company C

Environmental requirements

Requirements connected to by-products. Larger suppliers must be certified (ISO14001).

Requirements connected to CO2 emission and energy consumption. Larger suppliers must be certified (ISO14001).

Suppliers must be certified (ISO14001). No other requirements concerning sustainability or environment.

Strategy including future environmental requirements to achieve environmental goals, such as certification (ISO14001 and ISO50001).

Circularity

No current processes for reuse or refurbishment. Recycling is done, certain equipment is sold.

Certain wastes and water are being recycled or reused. No clear strategy for inclusion of CE.

New business model including circularity in form of offering renovation kits for products. Also investing in repairable systems in operations.

Laser treatment for repairing products. Refurbishment is offered on certain products.

Environmental tools

Green kaizen, Green design Event.

Environmental checklist, Green kaizen.

GPM.

Simplified LCA.

SHE handling

Larger focus on Larger focus on safety and health. safety and health due to regulatory reasons.

Larger focus on Not specified*. safety and health due to regulatory reasons.

* No knowledge was found on the matter.

4.2 Green Design – A Conceptual Methodology for Capital Investments The structured literature review concluded several approaches as appropriate to increase environmental sustainability in production companies. Four green-lean approaches were identified as appropriate for usage in production system design, and thereby within capital investments. (1) green lean six sigma and the DMAIC method, (2) LCA, (3) E-VSM, and (4) eco-efficiency indicators and assessment. Also, four circular approaches were identified, (1) waste analysis, (2) end-of-life path, (3) product-service systems, and (4) the 3R principle. All approaches are frequently occurring in theory and are concluded to be appropriate for use in producing companies (see Sect. 2 Related Research). A conceptual methodology called Green Design was developed and launched as a method at AstraZeneca, Sweden Operations in Södertälje, Sweden. The intention of

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Green Design is to decrease the gap in project sustainability and increase possibilities for environmental sustainability inclusion throughout the organization, thereby also increasing the sustainability culture. The Green Design methodology was developed based on the Green Performance Map (as a part of the method Green Kaizen) [9, 10] and DMAIC [24, 25]. The purpose was to produce tailored environmental sustainability improvements for capital investment projects in the production context (factory). The name Green Design was aligned with the name of the environmental improvement method called Green Kaizen, although targeting the design phase instead of the operations phase. The conceptual methodology, referred to as the Green Design methodology, was developed to support project teams in identifying, evaluating, implementing, and follow-up on environmental improvements in production investment projects. It was driven by the goal to make the investments as environmentally sustainable as possible throughout the life cycle of the investment (equipment, machine etc.). Prior to this study, a first stage of the Green Design methodology had been introduced to new capital investment projects through a workshop called Green Design Event. During the workshop, the project team answered a set of questions to identify environmental improvements for the project. However, in this early pilot stage, projects were left without a clear process on how to proceed towards implementing the identified and prioritized improvements. The case study followed the update of the Green Design Event, changing to fewer and more concise questions. The questions formulated were derived from the four green-lean and four circular approaches as identified in the structured literature review conducted. The questions were further divided into four categories for input into the production system, and four categories for output as derived by GPM [9]. One category was changed to better fit AstraZeneca’s need. The four input categories are (1) energy, (2) valueadding material, (3) equipment and facilities (originally non-value adding material), and (4). The four output categories are (1) emissions (air/noise), (2) productive output, (3) non-productive output, and (4) emissions (water/soil). After each Green Design Event (executed as a 2-h workshop with a broad participation within different knowledge areas), identified suggestions were to be evaluated. Evaluation was based on environmental impact and project impact, i.e., how specific suggestions were impacting the project scope, budget, timeframe, and resources. As a major complement (and part of the conceptual methodology), tools for evaluation were developed primarily based on a simplified LCA, E-VSM, and eco-indicators. These as suggested by the literature review. Post evaluation, the improvements were reviewed by the project sponsor and steering committee for further decision on implementation into the project scope. Also, the improvements were included in the requirement specification for the equipment suppliers where necessary. The Green Design methodology as developed in 2022 is illustrated in Fig. 2. Green Design was inspired by the Green Kaizen method and follows a stepwise methodology derived from DMAIC as suggested in the literature review; (1) defining the scope of the project and identify environmental problems and improvements, (2) measure and evaluate suggestions for overcoming the problems, (3) implement improvements, and (4) ensure improvements having good effect on business, and follow-up. The first

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Step 1. Define project environmental scope and identify environmental problems and improvements. Step 2. Measure and evaluate suggestions for overcoming environmental problems.

Education Green Design Event Evaluation Sponsor agreement Yes

Step 3. Implement improvements

Implementation

Step 4. Ensure improvement have good effect on business and follow up.

Share knowledge and follow-up

No

Fig. 2. The Green Design methodology developed at AstraZeneca

step (1) in the methodology was expanded to also include education due to identified lack of knowledge on sustainability. Lack of knowledge was seen as hindering the project teams’ possibility to identify environmental improvements. The Green Design Event itself was updated using less, and more generalized questions to enable further utilization areas than prior event. Questions were connected to waste analysis, end-oflife path decision making, product-service system, and the 3R principle as suggested by the literature review. Also, questions concerning maintenance and construction was included to fit AstraZeneca’s need. Step two (2) was implemented by including existing, and developing new, tools. These gave the project team possibility to calculate the project impact, i.e., scope, budget, time, and resources. Also, estimated environmental impact was calculated by measuring CO2 , energy usage, water usage and waste generation. Step three (3) on implementation dealt with identified and evaluated environmental improvements together with project impact. Some which could require anchoring with sponsor and possibly steering committee to ensure agreement on implementation. The fourth (4) and last step intended to ensure good impact on business through follow-up as well as an opportunity to enhance the Green Design methodology through sharing knowledge and experiences. The updated Green Design Event was tested on three ongoing projects at AstraZeneca Södertälje. The first test was on a project concerning cooling equipment where ten environmental improvements were identified and taken for further evaluation. The second test was on a project concerning capacity increase of a cleaning process where eleven environmental improvements were identified and taken for further evaluation. The third and last test was on a project concerning decontamination equipment where 12 environmental improvements were identified and taken for further evaluation. Due to the length of the capital investment projects, assessing the results thoroughly has not been possible as

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part of this study. However, workshops and discussions were held with project managers and teams on the continuous work required for implementation. This brief assessment concluded that the tools provided in the Green Design methodology were practical in usage since they provide project teams with possibilities for simplified LCA’s amongst others. The results functions as a basis for scope changes, improving the potential of achieving more environmentally sustainable investments in the end.

5 Analysis 5.1 Corporate Sustainability Inclusion All companies interviewed in this study have implemented sustainability goals and taken steps towards environmental initiatives motivated by the ongoing discussions on sustainability in industry worldwide. However, AstraZeneca was the only company of the four studied that have implemented a goal on capital investment project level. Engineering management decided on a KPI stating that 95% of all capital investment projects should identify environmental improvements. Actual implementation of improvements was not, however, ensured by the KPI. Identification of improvements was rather ensured. Identifying environmental necessity on corporate level implies motivation to include sustainability strategies at this level. However, the gap in knowledge as identified by Rossi et al. [2] demonstrates together with the AstraZeneca KPI that corporate strategies were not fully deployed throughout the organization. Capital investment project organizations were overlooked. All companies studied had implemented environmental work on operational level of the organization, implying that sustainability strategies were deployed to operational level. The 80% reduction opportunity when improving environmental sustainability in capital investment project design [4] justifies sustainability strategy deployment in three stages: corporate sustainability, capital investment project sustainability, and operational sustainability, as illustrated in Fig. 3.

Corporate

Priorities

Sustainability enabler

Capital Investment Project Operational improvements

80% sustainability inclusion

Operations

20% sustainability inclusion

Fig. 3. Sustainability strategy deployment

Targets are set for implementing environmental sustainability at stage 1 – corporate level. This stage sets the strategy towards change, 100% of sustainability inclusion is

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enabled in this stage. The next step is to derive the 100% into actual work. Following the concept of 80/20 environmental impact connected to design/operations. 80% of opportunity to reduce impact is in stage 2 where production systems are being designed through capital investment projects. Stage 2 decides the amount of embodied environmental issues. Stage 3 is the remaining 20% where adjustments can be made to up-and-running equipment, systems etc. to reduce energy consumption, emissions etc. (optimization). It is important to realize the operational and capital adjustments needed towards sustainability when deciding at corporate level, where priorities and improvements are fed by operations. Initiatives to improve sustainability should be cascaded throughout the organization to assure production system design in form of capital investment is reached. It could be assumed that most of the capital investment projects are brownfield, i.e., alterations made to up-and-running production systems, based on strategic re-investments made in companies. Changes must be made in the design phase of all projects, not only greenfield (new, with no or limited restrictions), to assure that embodied environmental impact is decreased. 5.2 Green Design AstraZeneca has since this study performed additional Green Design events and the Green Design methodology has begun spreading as a best practice to include other types of projects (IT, supply chain etc.). AstraZeneca has appointed an operative team of four engineers called the Green Design team with the task of developing and managing the methodology further. The team is also engaged in a research project with KTH Royal Institute of Technology together with two large manufacturing corporations and a supplier of production equipment. The analysis made in this paper draws conclusions from both work made in the study as well as work made post the study. The possibility to introduce a methodology to identify, evaluate, and implement is supported by both academic best practices together with empirical results on industrial level. The new Green Design methodology allows AstraZeneca to take the next step towards inclusion of sustainability also on project level where identified improvements require support in assuring implementation. Simplified approaches for support, such as simplified versions of LCA, VSM etc., are preferable due to the extensive work required in capital investment projects. Also, utilizing already applied tools within the organization was preferable to achieve a higher degree of acceptance within multiple departments. Tests conducted on developed tools suggested not only decrease in energy, waste, water, and/or CO2 consumption, but also decreased operational costs as a result. The outcome of the initial events implies that expertise within the concerned project area is necessary when identifying environmental improvements. In several Green Design events, most valuable input came from experienced process operators, process engineers, and service engineers, who are the people working closest to the processes. However, a facilitator is still needed to steer discussions towards focusing on sustainability and environmental improvements. The facilitator should be generally skilled in operational processes to transpose technical solutions into fitting environmental improvements. The Green Design methodology in general was deemed an appropriate way of working with sustainability in capital investment projects regardless product or industry. In addition,

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Green Design has contributed to an increased environmental corporate culture where sustainability is a larger part of capital investment projects.

6 Conclusion This study identifies four green-lean, and four circular approaches as appropriate for usage in capital investment projects. The eight approaches were used when developing a methodology for including environmental sustainability in capital investment projects at AstraZeneca. The methodology was developed by utilizing the DMAIC method where the methodology follows the four framework steps (1) identification, (2) evaluation, (3) implementation, and (4) follow-up. The first two steps utilize approaches identified in literature to produce environmental improvements and evaluate these using simplified tools which supports project managers where time is often restricted. The latter two steps have been tested post this study but with limited number of results. No conclusions have been made to these latter two steps at this point. To summarize, environmental sustainability is implemented on corporate strategy level and on operational level in the companies studied. However, in relation to capital investment projects, environmental sustainability is lagging towards other corporate focus areas. The vision of becoming a green organization is recognized on high managerial level, but not perceived as clear on project level. The recognition on project level is required based on data collected in academic references and empirically to enable future resource efficient and circular operations. This study recognizes multiple approaches for improving environmental sustainability on project level, suggesting companies to focus on the design phase of capital investment projects as a way. Looking at capital investment projects, cost is still the most important factor when deciding upon investments. For future research and due to the importance of cost at most companies, it would be most beneficial with more knowledge and tools that supports the decision-making concerning the tradeoff between cost and environmental aspects. Such tools should also include the operational costs to make it possible to compare the case of making environmental improvements vs making investments. It could be assumed that the new EU Taxonomy will support an increased focus on making more sustainable investments.

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Comparative Analysis of Sustainability and Resilience in Operations and Supply Chain Management Piotr Warmbier(B) Global Supply Chain Management, University of Bremen, Bremen, Germany [email protected]

Abstract. Given the increasing uncertainty in global supply chains, the consideration of resilience alongside sustainability has become significantly more important in the area of manufacturing strategy. However, the operations and supply chain management literature has witnessed a debate regarding the potential tradeoff between these two concepts. This study aims to delve into the similarities and differences between sustainability and resilience, exploring their theoretical background. A systematic literature network analysis was conducted on sustainability and resilience literature using CIMO (Context-Intervention-Mechanisms-Outcome) logic for the data evaluation. The results show overlaps but also a partly different focus in the development of sustainability and resilience based on the applied theories, the focus on the performance problem, and the positioning within the manufacturing strategy. This study contributes to the current discussion on the potential interplay between sustainability and resilience in operations and supply chain management. The developed framework provides guidance for integrating dual demands of sustainability and resilience within manufacturing strategy research and practice. Keywords: Operations and supply chain management · sustainability · resilience

1 Introduction In recent years, the focus of manufacturing companies has shifted from profit-oriented strategic goals to sustainable development as a critical competitive priority [1, 2]. However, this refocus has been fraught with challenges, as manufacturing companies, such as in the automotive industry, that have historically prioritised (economic) sustainability over resilience have been unable to respond to large-scale disruptions such as the Covid 19 pandemic [3, 4]. This has left operations managers with the challenge to balance economic, social and environmental performance outcomes, which has been further complicated by the constraints imposed by their operations and global supply chains [5, 6]. Given the competing demands for sustainability and resilience, manufacturing companies today must strive for both competitive priorities [7]. © IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 382–397, 2023. https://doi.org/10.1007/978-3-031-43688-8_27

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The discussion in the operations and supply chain management literature on the interplay between resilience and sustainability is contradictory. Some studies point to trade-offs between sustainable and resilience strategies, with certain sustainability practices having a negative impact on resilience and vice versa, such as redundancy vs efficiency practices [6–9]. On the other hand, other studies point to a synergy between resilience and sustainability, emphasising how sustainable and resilience strategies can enhance each other in operations and supply chains, such as certifications and eco-design practices [10, 11]. These contrasting perspectives highlight that there is no consensus in the literature on whether the interplay between resilience and sustainability should be seen as a trade-off or a synthesis. Furthermore, it should be noted that the literature reviews by Negri et al. [12] and Matos et al. [6] highlight conflicts between sustainability and resilience without structurally evaluating the origins of sustainability and resilience research and the development of the main themes. Understanding the historical background and development of these fields could provide valuable insights into the similarities and differences found in the theoretical development of these concepts in operations and supply chain literature, particularly with regard to the context, intervention, mechanisms, and outcomes (CIMO). The paper aims to explore the origins of sustainability and resilience research and the development of the main themes, in order to understand the similarities and differences between the two fields, while focusing on the problem within manufacturing operations and supply chain strategy and the ongoing debate regarding the conflict between sustainability and resilience. The research question is formulated as follows: What are the similarities and differences in the theoretical development of sustainability and resilience in operations and supply chain literature?

2 Background Holling [13], an ecologist, defined resilience as a system’s ability to absorb changes and return to equilibrium following a disturbance. The ability of a system to bounce back to its initial state was one of the first definitions of resilience in operations and supply chain management research [14, 15]. In contrast, sustainability is considered as continuous development and change. In the late 1980s, the Brundtland Report defined sustainable development as “[…] a development that meets the needs of the present without compromising the ability of future generations to meet their own needs” [16]. Seuring and Müller [2] introduced sustainability management in the context of operations and their supply chains, including the concept of a triple bottom-line from a performance standpoint, with the goal of sustainability management being able to meet a balanced approach in the performance outcome. Several studies have identified trade-offs between sustainable strategies and resilience in operations and supply chain management. Ivanov [8] highlighted how sustainability strategies, such as lower stock levels and reduced redundant practices, can negatively impact resilience and increase operations and supply chain vulnerability. Fahimni et al. [9] emphasised that green supply chains are sensitive to disruptions, and resilient supply chains may have negative effects on eco-performance outcomes. Rajesh [17] conducted an empirical study in the electrical industry, revealing trade-offs

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between resilience and sustainability, depending on the company’s business strategy. On the other hand, there is evidence of synergy between resilience and sustainability. Silva et al. [11] developed a model illustrating how resilience and sustainability can coexist, highlighting capabilities like flexibility, collaboration, knowledge management, visibility, sensing/seizing, and supply chain reengineering. Eggert and Hartmann [10] found that the implementation of sustainability strategies positively influences resilience, with lower damage from unexpected disruptions observed in companies that prioritise sustainability and possess significant experience in that domain. However, the literature review by Negri et al. [12] pointed out conflicts between sustainability and resilience, such as divergent capabilities and practices and performance dimensions, without thoroughly examining the origins of sustainability and resilience research and the development of the main research paths. This research gap presents an opportunity to delve deeper into understanding the background and origins of these concepts, providing a more comprehensive analysis of the similarities and differences between sustainability and resilience in operations and supply chain management. Within the context of manufacturing strategy, two key concepts, competitive priorities and capabilities, are essential. Competitive priorities are the strategic objectives set by decision-makers to gain a competitive advantage, while competitive capabilities encompass the specific operational capabilities and resources that enable the organisation to achieve those priorities. Out of the operations management literature the need arises to clarify how sustainability and resilience should be positioned within manufacturing operations and supply chain strategy [6], whether as competitive priorities or competitive capabilities [1, 18]. This aspect is particularly relevant for understanding the trade-offs between resilience and sustainability [6, 8], as empirical evidence suggests that these trade-offs are primarily observed at the level of competitive priorities [1].

3 Methodology A combination of two research methods was used to address our research question: the systematic literature network analysis (SLNA) [19], which combines systematic literature review (SLR) and the citation network analysis (CNA), and CIMO (ContextIntervention-Mechanisms-Outcome) logic. The SLNA was methodologically guided by Coliccia and Strozzi [19] to select the most relevant articles for analysis, and the CIMO approach for analysing the data [20]. The following subsections describe the application of the methodology by dividing the research methods into several steps (Fig. 1). 3.1 Systematic Literature Review The data collection of relevant resilience and sustainability papers in operations and supply chain management started with two parallel conducted SLR studies. The data collection of these papers was carried out on the 15th of March 2020 for both data samples in Web of Science (WoS) and Scopus. Since the main interrelation and debate between resilience and sustainability started to gain momentum from 2020 onwards, the review primarily aimed to capture the foundational and developmental aspects of these fields, which were sufficient to be addressed by examining articles up until 2019.

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The research variables were defined based on recent systematic reviews of resilience and sustainability in operations and supply chain management [21–23] and validation sessions with two academics which are summarised in a research protocol (Table 1). The transparency of the structured literature review was maintained through detailed steps and explicit selection criteria (Table 1) to evaluate the significance of papers for answering the research question [19]. The screening process and results of each step are presented in Fig. 1. The final samples resulted in 114 resilience and 171 sustainability articles which were then analysed with CNA in order to reach to the final sample.

Fig. 1. Systematic literature network analysis by Colicchia and Strozzi [19]

Table 1. Research protocol Research variable for locating Description Search string resilience

“supply chain” AND (“resilie*” or “risk” or “security” or “mitigation” or “business continuity”) AND (“capabilit*” or “element*”)

Search string sustainability

“supply chain” AND (“sustainb*” or “green” or “environment” or “ethical” or “social”) AND (“capabilit*”)

Databases

Scopus and Web of Science

Document type

Article (peer-reviewed)

Language

English

Data range

Unlimited until end of 2019

Search fields

Titles, Abstracts and Keywords

Criteria for selection

Inclusion: Resilience or sustainability in operations or supply chain management including relevant information about strategies, practice, capabilities and performance measures Exlusion: Missing access for full-paper review, not in English language, not peer-reviewed, duplicates

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3.2 Citation Network Analysis Because the citation data logic regarding cited references and the address information in Scopus is different from that of WoS, the problem of database selection had to be addressed [24]. WoS citation data is commonly used for cited reference analysis [24] and the cited reference data from WoS is based on the Science Citation Index compiled by the Institute for Scientific Information. Therefore, this study used the WoS database to collect the citation data. The final SLR samples were used in order to collect citation data from WoS on the 25th of January 2021. Articles not listed in WoS had to be excluded from the samples. This resulted in final CNA samples of 102 SCRES and 161 SSCM articles with citation data (Fig. 1). The CNA enables to study of the data from two different perspectives: a static one through the analysis of the citation network using citation counting and citation linkages as well as a dynamic one by extracting the main path through the traversal weight [19]. The historical path of the research was worked out by the dynamic perspective of the main path analysis. It allowed us to identify papers cited by very cited papers, contributing to the theory-building. The data set was processed through Pajek [19]. The static view was used to present the citation network using the VOSviewer tool to visualise both networks and to identify highly relevant articles through citation counting and intensity of linkages between the articles. Main Path Analysis (MPA): The two-step algorithm for main path analysis is [25, 26]: 1. quantifying the traversal weight of the citation and 2. extracting the main path of the citation network using traversal weight of citations. Starting with the traversal weight of citations, this study has applied the search path link count (SPLC) as recommended by Colicchia and Strozzi [19]. In the next step, several ways exist of finding the main path, the local or the global search. The global main path was proposed as a path with the largest overall traversal counts. According to Coliccia and Strozzi [19], the standard global main path helps to discover the overall significant path, which is why it was chosen for the analysis. Citation Network: From a static perspective, the CNA of the selected papers allowed us to compute a ranking of articles. The ranking can be estimated in terms of the frequency of articles being cited (locally and globally) or in terms of the closeness centrality [19] within the network. The former measure, i.e., the frequency of articles being cited, ranks the articles by the number of received citations, identifying the most cited papers. Thereby this measure can be evaluated from two perspectives: global citation score (GCS) represents the total number of citations received by a document from all publications indexed in WoS, while the local citation score (LCS) refers to the number of citations a document obtained from other documents in the specific sample. Through the second measure, closeness centrality (CC), it is possible to identify papers that are cited by very cited papers and thus that contributed to the theory-building [19]. In this sense, it identifies the articles that represent the basis of the field and that were used by many authors for the development of their contributions.

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Descriptive Analysis: To provide the reader with essential information about the literature samples [27], Table 2 contains information about the distribution of articles across the different journals within the two citation network samples. Figure 2 shows the number of published articles by year. Table 2. Top 5 journals within citation network Journal (resilience sample)

No. of articles

Journal (sustainability sample)

No. of articles

Supply Chain Management – an International Journal

14

Journal of Cleaner Production 22

International Journal (IJ) of Production Economics

6

Sustainability

16

IJ of Production Research

6

IJ of Production Economics

14

IJ of Operations & Production Management

5

IJ of Physical Distribution & Logistics Management

8

IJ of Physical Distribution & Logistics Management

5

Business Strategy and Environment

7

Fig. 2. Distribution of articles published by year

Final Sample of Articles and Content Evaluation via CIMO: By using publications identified through MPA and adding additional papers with the highest LCS and CC from the citation network, this study was able to identify 15 highly relevant papers from each sample that contributed strongly to theory building. This allowed us to analyse the content of the articles with the CIMO logic (Table 3). The CIMO logic was be applied as follows [20]: Context: Identifciation of the context in which the studies were conducted. Intervention: Determination of interventions or factors of interest that were being investigated in the literature related to positioning within manufacturing strategy. Mechanism: Examination of mechanisms or processes through which the interventions were expected to produce certain effects or outcomes. This involved understanding the underlying theories that explain the relationships between the interventions and outcomes. Outcome: Identification of the analysed performance problem in the studies.

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Resilience LCS GCS CC article

MPA Codings Sustainability LCS GCS CC article

MPA Codings

[28]

33

857

0.50 x

a, c

[29]

12

73

0.22 x

b

[15]

42

554

0.54 x

a, c

[30]

12

125

0.23

a

[31]

52

441

0.61 x

a, b

[32]

24

256

0.28

a, b

[33]

40

308

0.50

A

[34]

23

122

0.26 x

b

[35]

48

282

0.57 x

C

[36]

25

295

0.28 x

b

[37]

24

107

0.46 x

D

[38]

10

80

0.24

f

[39]

17

118

0.46

e

[40]

7

81

0.23 x

f

[41]

27

225

0.41

a

[42]

10

72

0.23

a, f

[43]

29

150

0.46 x

f, g

[44]

9

79

0.24

i, f

[45]

24

180

0.45

a, b

[46]

6

40

0.25

j

[23]

24

161

0.44

a, h

[47]

6

120

0.23

b, f, j

[48]

29

174

0.51 x

c

[49]

5

25

0.22 x

b, c

[50]

9

91

0.42

a

[51]

9

73

0.24

b

[52]

20

34

0.53

c

[53]

3

69

0.22

j

[54]

11

15

0.42

h

[55]

4

12

0.21

b

Abbreviations: Local citation score (LCS), global citation score (GCS), closeness centrality (CC); Coding theories: Resource based view (a), dynamic capability theory (b), grey theory (c), social capital (d), graph theory (e), relational view (f), system theory (g), complex adaptive systems theory (h), goal-setting theory (i), stakeholder theory (j)

4 Analysis and Results 4.1 Context The context of the resilience studies explored various aspects related to disruptions [28], uncertainty [15], risk management [28, 33, 45], modelling approaches [39] and resourcebased perspectives [31, 41, 50] in operations and supply chains. The sustainability studies examined aspects of green operations and supply management [30, 38, 53], social issues [32, 40, 42], dynamic capabilities [29, 34, 49, 51, 55], collaborative practices [40, 42], supplier development [44], supply chain visibility [46], relational capabilities [47], and stakeholder perspectives [53]. Both study samples aim to understand and improve the performance and capabilities of operations and supply chains. However, the key difference lies in their specific objectives: resilience studies primarily investigated strategies and factors that contribute to the ability of an operation and supply chain to withstand and recover from disruptions, while sustainability studies explored practices and approaches that promote environmental and social responsibility in the form of transformation.

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4.2 Intervention Regarding positioning sustainability and resilience within manufacturing strategy, the literature analysis revealed that resilience was considered as a higher-order (meta-) capability or capacity in the main contributions [31, 33, 35]. In contrast, sustainability was defined as a competitive priority within an operations and supply chain strategy [32, 34, 40]. The findings also emphasise that the more recent publications tend to consider resilience as a competitive priority [41, 43, 45]. From this, it can be summarised that due to the different goals of sustainability, driven through the referenced triple bottom line framework, sustainability was considered as a goal-oriented and resilience as a process-oriented approach in the main research endeavors. 4.3 Mechanisms The earliest definitions of resilience in operations and supply chain management research [15] referred to the ability of a system to bounce back to its initial state, a dimension akin to the concept of engineering resilience [54]. Accordingly, resilience has often been associated with robustness in the context of static supply chain systems [15, 33, 41], where a system is optimised against scarcity conditions [54]. Meanwhile, sustainability is readily considered in relation to tolerance for continuous change [32, 34, 36, 49]. The examination of the main research endeavors in the realm of supply chain sustainability and resilience indicates that early supply chain resilience studies relied heavily on resourcebased theory [15, 23, 31, 33, 41, 50], which views well-performing organisations as retaining clusters of unique resources that enable them to achieve desired outcomes with a competitive advantage [29]. Conversely, early supply chain sustainability studies were developed primarily based on dynamic capability theory [32, 34, 36, 47, 49, 51, 55]. The original conception of capabilities assumed that once a pioneering organisation had established static capabilities, rival emergent organisations would be hard-pressed to catch up, resulting in a long-term competitive advantage for the pioneering organisation [29]. Although such an assumption may hold true in a stable environment, long-term stability is not characteristic of today’s globally competitive, constantly changing commerce environment riddled with environmental uncertainties—a circumstance presumed to be the new normal [29, 54]. Dynamic capability theory moves beyond resource-based theory by assuming that companies need to be able to generate, connect, and transform capabilities that enable them to react promptly to changes in a rapidly evolving environment [29]. Dynamic capabilities may thus be considered to be of greater importance than static capabilities if they are necessary for business outcomes to be attained in changing environments in the long term [50, 54]. Especially in recent years, publications on resilience have started to look at resilience with an additional bounce-forward approach by including a growth phase in their resilience models and applying theories such as dynamic capabilities and complex adaptive systems [23, 48, 56]. Finally, Adobor et al. [54] have highlighted in their work the different dimensions of resilience, such as the classical engineering or economic resilience with a bounce-back approach and the dimension of social-ecological resilience with the bounce-forward approach. Another difference between the sustainability and resilience

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literature relates to uncertainty. In the resilience literature, risk, vulnerability and uncertainty played an essential role in theoretical development and the main research efforts involved grey theory [15, 35, 48, 52]. In the sustainability literature, on the other hand, the relationship between one’s operation and its business partners has been explored much more through the relational view and stakeholder theory [38, 40, 42, 44, 46, 47, 53]. In both sustainability and resilience literature, the role of capabilities played a vital role in the conceptual development. As described before, the most common applied theories were the dynamic capability, resource-based view theory, complex adaptive systems and relational view theory. It can be pointed out that four main categories of capabilities were applied in the literature: operations, supply chain, static and dynamic capabilities. However, the role of dynamic and collaboratively developed supply chain capabilities by using the dynamic capability [34, 36, 51, 55] and relational view theory [38, 40, 42, 47] has received greater importance in sustainability literature. 4.4 Outcome Both concepts have a common objective of comprehending and enhancing operations and supply chain performance as an output. Resilience has a facile relation to the ability to maintain stability and considerations of the influence of resilience have primarily focused on optimising a system against scarcity conditions [15, 31, 35] by focusing on the economic performance problem under scarcity [41, 54]. In contrast, sustainability is readily considered in relation to tolerance for continuous change by balancing all activities towards a balanced performance outcome of economic and socio-ecological parameters [36, 44, 55]. This balanced performance outcome approach is mainly driven by the fact that the main research endeavor in sustainability literature were referencing on the triple bottom line framework.

5 Discussion To further understand how practitioners and researchers can design sustainability and resilience into their manufacturing strategy, this paper used the results for developing a sustainability and resilience framework (Fig. 3), which is discussed below. 5.1 Theoretical Implications Context: In recent decades, sustainability has become a key focus in operations and supply chain strategy, leading manufacturers to prioritise resource minimisation and rely heavily on global supply chains. [57]. This shift has made them more susceptible to low propability but high impact risks, including extreme weather, pandemics, and geopolitical events, which can greatly impact sustainable performance [6, 58–60]. Dealing with such uncertainties can be challenging for decision-makers, requiring a resilient approach to operations and supply chains [59]. However, this literature review reveals that the consideration of high uncertainty in the sustainability literature is relatively limited compared to the resilience literature. Given the increasing prevalence of low probability

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Fig. 3. Sustainability and resilience typological framework in operations and supply chain mgmt.

and high consequence risk events like the COVID-19 pandemic and geopolitical disputes, it becomes imperative to strongly integrate both resilience and sustainability into manufacturing operations and supply chain strategies in the future. Interventions: Traditional manufacturing trade-off models [1, 18] emphasised the need for manufacturing firms to focus on a single competitive priority to gain a competitive advantage. However, in dynamic and uncertain environments, it becomes crucial to prioritise multiple factors simultaneously [61]. This paradigm shift allows manufacturing companies to embrace new strategy models using dualism approach [61]. Based on the literature review, the positioning of sustainability and resilience in manufacturing strategy requires further clarification. This study suggests to consider both as competitive priorities in order to further investigate the trade-off effect [2, 43, 45, 62]. In addition, future research could explore resilience from two perspectives: the traditional view of economic resilience, which focuses on optimising the supply chain system under conditions of scarcity to address an organisation’s economic challenges, and social-ecological resilience, which encompasses the capacity of the system to absorb disturbances, adapt to changes, and maintain essential functions and relationships while minimising negative impacts on society and the environment [54]. Mechanisms: Our findings show a resource-based view as a common theoretical lens to explain resilience. In resilience research, the resource-based view provides a basis to explore relationships among specific resources, capabilities, and performance. To address the criticism that the resource-based view is static in nature, recent resilience publications used theories such as dynamic capabilities theory, grey theory, contingency theory, systems theory, and complex adaptive systems theory that extend or complement the resource-based view. The results revealed that sustainability literature relied much more on relational view and stakeholder theory. However, there is growing recognition of the interconnectedness between resilience and sustainability, and future research efforts should bridge these theory application perspectives.Despite the increasing number of studies using different theories to explain resilience and sustainability, the need remains to use theories that can describe and explain the relationship and trade-off effects between resilience and sustainability and, on the other, provide resolution strategies for the potential tensions between competitive priorities [6, 12]. Future research in operations and supply chain management should explore the interrelationships, trade-offs

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and tensions between competitive priorities, such as sustainability and resilience and develop synthesis approaches to address these dilemmas [6]. Thereby Berti and Cunha [61] offer an related model which could be used for further research. Negri et al. [12] suggest exploring congruent capabilities as a possible synthesis approach, aligning with cumulative capability models like the Sand Cone model in the field of manufacturing strategy [1]. For this, this paper provides further guidance on analysing congruent capabilities, namely based on the distinction between different capability categories: static, dynamic, operations and SC capabilities. Outcome: Our findings also suggest that further exploration of the relationship between resilience and sustainability and a potential synthesis approach would be beneficial by aligning performance outcomes with Elkington’s [60] triple bottom-line framework. Thus, the performance problem in the context of high uncertainty would go hand in hand with the balanced performance outcome between economic, social and environmental. Recent simulation studies [8, 9] examining trade-offs between resilience and sustainability have only considered economic performance factors, where the need for more objectivity of the results can potentially be questioned. Therefore, future research could obtain more accurate results by considering three performance factors.

5.2 Practical Implications and Contribution Based on the findings and theoretical analysis, this study derived practical implications for decision-makers in the field of operations and supply chain management. Uncertainty as the New Normal: Decision-makers should recognise the increasing uncertainties caused by low probability, high consequence risk events such as the Covid19 pandemic, geopolitical disputes or environmental uncertaints [3, 6, 29, 59]. Dualistic Approach in Competitive Priorities: Competitive priorities in manufacturing strategy refer to the key areas, such as quality, delivery, flexibility, and cost, that decision-makers focus on to gain a competitive edge in the market, with recent shifts towards priorities such as resilience and sustainability reflecting evolving business dynamics and stakeholder concerns [1].Traditional trade-off models that focus on a single competitive priority within the manufacturing strategy may no longer be sufficient in dynamic and uncertain environments [1, 61]. Decision-makers should explore how to prioritise and integrate resilience and sustainability in the manufacturing strategy to address these uncertainties effectively as dualistic competitive priorities [7]. Managing Tensions: Decision-makers should try to understand the relationship between resilience and sustainability, both interrelation and contradiction and develop management strategies for potential tensions [6]. Theories related to paradox and dialectics can help to strive for a synthesis approach related to congruent capabilities in order to overcome tensions [12, 61]. Balanced Performance Outcome: Aligning performance measures with Elkington’s [60] triple-bottom-line framework, which considers economic, social, and environmental dimensions, can provide a balanced approach to evaluate resilience and sustainability strategies and practices.

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Contribution: This study expands on previous research [6, 12], examining the similarities and differences between sustainability and resilience based on the theoretical development and applied theories. It emphasises the conflict between change and stability [11, 50, 54], with early resilience literature building upon the resource-based view and recent focus on dynamic capability, while sustainability incorporates dynamic capabilities, the relational view, and stakeholder theory. Additionally, this study expands on the suggestion on joint (congruent) capabilities [12, 64] aligning with cumulative capability models like the Sand Cone model in the field of manufacturing strategy [1]. Additionally, this paper proposes positioning resilience and sustainability as competitive priorities within the manufacturing operations and supply chain strategy to explore their relationship with the paradox theory [6, 12, 63].

6 Conclusion The paper explored the theoretical background of the literature on sustainability and resilience in operations and supply chain research, seeking similarities and differences. An SLNA was conducted, analysing relevant papers according to CIMO logic. The findings revealed that the consideration of high uncertainty in the sustainability literature is relatively limited compared to the resilience literature. In the theoretical beginnings, resilience was often regarded as a higher-order capability or capacity, while sustainability was positioned as a competitive priority within operations and supply chain strategy, although more recent publications tend to view resilience as a competitive priority as well. A resource-based view was commonly used to explain resilience, but recent publications extended this view with theories such as dynamic capabilities theory. Additionally, sustainability research has consistently extended the resource-based view by incorporating dynamic capability, relational view, and stakeholder theory from their theoretical beginnings. Finally, the resilience literature primarily addressed the economic performance problem under scarcity, while sustainability literature embraced a balanced performance approach with the triple bottom line framework. This study contributes to the current discussion about the potential interplay of sustainability and resilience in operations and supply chain management based on analysing the origins of sustainability and resilience research and the development of the main themes. The sustainability and resilience typological framework guided by the CIMO logic provides guidance on how sustainability and resilience could be considered in future research and practice related to manufacturing strategy models. This paper suggests positioning both approaches as competitive priorities within manufacturing strategy operations and supply chain models to investigate potential trade-offs and tensions. The research study was comprehensive but also had some limitations. The research focus of the selected papers was limited to papers till the publication year 2019. Although the research study focused on exploring the origins of sustainability and resilience research and understanding the main theoretical themes, it is important to acknowledge that the landscape of literature on this topic has evolved since the included papers were published, which could potentially have changed the main research paths and improved the findings and arguments. In addition, the selection of literature through the SLR search string focused on supply chain literature, as it was assumed that operations

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management is a part of supply chain management within resilience and sustainability literature. However, there may be conceptual limitations in the initial collection of literature, so that operations management literature may be underrepresented. Future research should explore empirically the interplay between resilience and sustanability in operations and supply chain management, evaluating potential trade-offs and tensions [61, 63] within the manufacturing strategy under conditions of uncertainty, and investigate congruent capabilities [12, 64] from a cumulative capability model perspective [1]. In addition, further research is needed to bridge the gap between the resilience and sustainability literature by considering theoretical perspectives such as relational view, stakeholder theory and grey theory to enable a deeper understanding of the interplay of resource allocation between these concepts within own operating boundaries and supply chain partners.

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Capturing Value by Extending the End of Life of a Machining Department Through Data Analytics: An Industrial Use Case Federica Acerbi1(B)

, Davide Pasanisi2 , Valerio Pesenti2 , Luca Verpelli3 , and Marco Taisch1

1 Politecnico Di Milano, Milano, Italy

{federica.acerbi,marco.taisch}@polimi.it 2 Consorzio Intellimech, Bergamo, BG, Italy {davide.pasanisi,valerio.pesenti}@intellimech.it 3 Same Deutz-Fahr, Treviglio, BG, Italy [email protected]

Abstract. Data analytics is increasingly becoming fundamental, especially for manufacturing companies which are asked to improve their sustainable-related performances to compete on the market. In this context, hence, Industry 4.0 enabling technologies integrated with specific industrial communication protocols appear as opportunities to upgrade obsolete machines by capturing value through data collection and analysis. Data analytics implies for manufacturing companies a better monitoring of resources consumptions during the production process and an enhanced decision-makers support on both short-term and long-term decisions. Nevertheless, the extant literature shows limited attention over industrial asset circular management and the opportunity to exploit data collection from the shopfloor. Therefore, this research contribution aims at understanding how to support manufacturing companies, which are currently operating with old and close-to-be obsolete machines, in the digital transition to make them be able to encounter the eco-efficiency and circular economy principles through asset lifecycle extension. In this research it has been focused the attention on the machinery department of a manufacturing company producing agricultural machinery having problem of obsolescence of industrial assets which generates lots of avoidable scraps and increased energy consumption. Through the installation of the MT Connect protocol on obsolete industrial assets it was possible to start extracting data during the production activities enabling a deep data analysis which led to the introduction of specific maintenance activities and the optimization of the production activities. Based on these corrections it was possible to decrease the material used, due to fewer scraps, and the energy consumed. This research study enabled the company to extend obsolete assets lifecycle extracting value from them thanks to data analytics techniques and sensors. Keywords: Data Analytics · Industrial Eco-Efficiency · Asset EoL extension

© IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 398–411, 2023. https://doi.org/10.1007/978-3-031-43688-8_28

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1 Introduction Nowadays, the fourth industrial revolution represents an opportunity for the entire society and more specifically for the manufacturing sector since it is asked to introduce new strategies optimizing resources consumptions [1]. Manufacturing companies, hence, have to act on the reduction of waste generation [2], reusing the waste if created whenever possible [3], and limit both energy [4] and material consumptions [5]. These practices would enable to embrace eco-efficiency and circular economy principles [6] thus, leading towards the slowing, narrowing and closing the resources loops [7]. To address these eco-efficiency oriented goals, most of the research studies focus their attention over products lifecycle and more specifically on new design approaches [8] or new business models introduction [9]. Nevertheless, circular economy and ecoefficiency, when applied in manufacturing, cover both products and processes thus, requiring the update of several internal processes including the industrial assets management practices too [10]. Indeed, synergies between circular manufacturing strategies and industrial asset management have been highlighted considering maintenance practices as the fundamental link between the two [11, 12]. This focal point can be better exploited to achieve circular goals if data and information would be properly collected, shared, and analysed [13]. More specifically, today more than ever, industrial assets are able to generate several data, nevertheless companies need to value them through the proper extraction and analysis to gather useful knowledge [14]. Looking at CNC machines, most of the “old” CNC machines are not able to transfer information regarding their activities and alarms outside their boundaries. New Industrial Communication Protocols are able to address this issue by collecting information and measurements from built-in sensors. Among the most diffused protocols today there are: Message Queue Telemetry Transport (MTTQ) [15], OPC UA [16], and MTConnect [17]. OPC UA and MT Connect cover the communication of the devices to their interoperability, and the criticality of the increase in bandwidth consumption is very low compared to the benefits of these protocols. On the other hand, MQTT is suitable for devices with low resources, low bandwidth networks, and high latency which require the flexibility of this protocol. Among them, MT Connect works for any time of brand and it is an open software facilitating the usage from a company perspective in terms of occurring cost [17] and for its characteristics it was selected for this research. Considering the envisioned gap in terms of limited attention over industrial asset circular management and the opportunity to exploit data collection from the shopfloor to cope with this gap extending the asset lifecycle, the present contribution aims at understanding how to support manufacturing companies, which are currently operating with old and close-to-be obsolete machines, in the digital transition to make them be able to encounter the eco-efficiency and circular economy principles through asset lifecycle extension. This objective has been addressed through an industrial use case, demonstrating how data analytics techniques can enable value extraction, even from very old and obsolete machines. Indeed, the opportunities under the Industry 4.0 paradigm are many both for the company in terms of maintenance and production optimization, and indirectly for the environment, in terms of energy consumption and discarded pieces reduction, empowering a sustainable industrial asset lifecycle extension. The business

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intelligence extractable from the available vast amount of unexplored data was the driving force behind the work that is being illustrated in this research study. The remainder of this contribution is structured as follows: Sect. 2 presents the methodology employed in this contribution, Sect. 3 shows the results from the industrial case application and the data analytics process, Sect. 4 discusses the results obtained highlighting the key practical contributions and potential improvements, last Sect. 5 concludes the contribution opening for future research opportunities.

2 Research Methodology This research contribution aims at understanding how to support manufacturing companies, which are currently operating with old and close-to-be obsolete machines, in the digital transition to make them be able to encounter the eco-efficiency and circular economy principles through asset lifecycle extension. Indeed, the several benefits obtainable from this transition would cover the optimization of resources consumptions under several perspectives. So far, it has been tried to study how to optimize the asset life cycle management by adopting specific set of guidelines based on standards [18] and an initial attempt to exploit Industry 4.0 technologies has been proposed to lead towards sustainability [19] but not concrete cases have been reported especially to highlight the opportunities for small and medium enterprises. To address the research objective, it has been taken as reference an industrial use case to study in detail its industrial need which is shared by several small and medium size companies. More in detail, the selected manufacturing company operates in the production of agricultural machines and is facing a drastic change in the market requests, being the customers more and more incline to obtain sustainable-oriented solutions. Indeed, the sustainability performances of their products need to be certified across the entire product lifecycle starting from the production process itself. Therefore, eco-efficiency and circular economy-principles (i.e., extend lifecycles, reduce resources consumption, and circulate resources across several lifecycles) need to be addressed by their industrial assets too. Moreover, this changed requests are asking for several investments to update the product design, and the financial capital available need to be properly balanced to survive and be competitive on the market from both a product and process perspectives. Based on these two core assumptions, in this contribution it is studied how to exploit data, that are potentially available on the shopfloor, through economically sustainable solutions. Indeed, MT-Connect was selected as open communication standard to collect and analyse data generated from the CNC-machines with the aim extend the lifecycle of assets previously considered obsolete and not well-functioning, by indirectly improving eco-efficiency and circularity performances during the production activities. In this regard the methodology reported in Fig. 1 has been used to access the data and use them to improve the shopfloor performances through the industrial asset lifecycle extensions. This methodology is shown in sub-Sect. 3.2 as an application to the industrial use case. These steps refer to previous studies that are reported in sub-Sect. 3.2. More precisely, the methodology can be split in two specific moments: the data quality and the data analysis which enable the data usage to support the decision-making.

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Fig.1. Methodology

3 The Industrial Use Case Application In this chapter it is described the context in which the company operates highlighting the key challenges it has to face. Then, the algorithm development and application it is described highlighting the results obtained. 3.1 Context The industrial use case application focuses on the implementation of Industry 4.0 concepts for the machining department of an agricultural machinery manufacturer, which is part of a multinational group based in Italy. The company main products are tractors, harvesting machines, diesel engines and agricultural machinery. In the machining department raw materials coming from the foundry are worked and the resulting pieces are almost ready to be assembled to build the power train component for the tractor assembly lines. The department is composed of three flexible manufacturing lines with 4 in-series machines each, capable of machining around 200 pieces a day, like gearboxes, monoblocs, body unions, wheel supports, up to 68 different pieces. After being produced, the pieces are marked with a code that identifies the line, the machine, the phase, and the production day, so that it is possible to identify a posteriori where and when they were produced. The selected machines for the industrial case are Mazak FH-8800 and they have been installed around 20 years ago. According to the provider, the estimated useful life is about 7–8 years if used 24/7. Indeed, downtimes and failures have been increasing since the useful life threshold has been reached and these machines are now considered obsolete. Indeed, it has been registered an annual increment of about 3%/4%, that demanded extraordinary maintenance to oppose this trend, resulting in a cumulative cost over the years of about 200000 euros. This situation has led to an increase in the number of non-compliant parts and in the machine energy consumption, resulting in the growth of expenditures for the company and a negative impact on the environment.

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Due to several reasons, like the too high financial expenditures, the company was not ready to take into consideration the substitution of these machines in the short term. Instead, the company encouraged the implementation of Industry 4.0 - related solutions to ripe the benefits obtainable from the digitalization of the manufacturing lines thanks to the insights that can be extracted through the employment of Data Analytics. To collect data from the machines, they have been equipped with the industrial open communication protocol MT Connect [17], enabling the collection of alarms data because of an unexpected stop or a drastic reduction in working speed. A dedicated SQL database has been set up to store data coming from the machines, synchronised with data related to the production, e.g., machining time, planned pieces, machined pieces, thanks to the integration with the company MES (Manufacturing Execution System). Furthermore, every line has been provided with an interactive totem, allowing the operator to input useful information, such as discarded pieces or produced pieces and to specify the actual reason for the machine downtime. The goal is to demonstrate how the Industry 4.0 paradigm can enable value extraction, even from very old and obsolete machines, both for the company in terms of maintenance and production optimization, and for the environment, in terms of energy consumption and discarded pieces reduction, empowering a sustainable industrial asset lifecycle extension. 3.2 Data Analysis Technique and Results Ensuring the quality of data from which the analysis starts is crucial to take effective corrective actions, indeed wrong decisions might be caused by inaccurate input data leading to the famous phenomenon of “Garbage In Garbage Out” (GIGO) [20]. Wrong results can be caused by both big and small errors and their propagation and compromises the final results through the so-called “snowball effect”. To avoid these problems and to guarantee a high-quality level of analysis, data must be transformed and cleaned since they might be incomplete, inconsistent and might contain errors. Although these activities, like data preparation, cleaning, and transformation, can be considered preliminary to the real analysis, they are time-consuming tasks covering most of the time for data mining application (around the 80–90%) [21]. Several steps were carried out to prepare the data before conducting the data analysis, and they are schematically shown in Fig. 2. The most diffused definition of Data Quality is “Fitness for use” [22], which aims at describing the ability of the collected data to meet user requirements. To assess data quality in different settings, a Data Quality Model needs to be defined: it is composed of several Data Quality Dimensions and each of them has the goal of representing a specific aspect of the set of data. Four dimensions have been selected as the most relevant for this industrial case: accuracy, completeness, consistency, and timeliness. • Accuracy: measures the degree to which a data value correctly represents a real-life phenomenon Accuracy =

number of correct values number of total values

(1)

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Fig. 2. Methodology for Data Quality Part

• Completeness: measures how the set of data represents the corresponding real world. A very common way to assess completeness is to count the number of null values. Completeness =

number of non − null values number of total values

(2)

• Consistency: determines the violation of semantic rules defined over a set of data items [23]. Consistency =

number of times the rules are not violated number of time the rules have been checked

(3)

• Timeliness: is defined as “the extent to which age of data is appropriate for the task at hand” [22]. Moreover, timeliness can be split into two concepts: currency and volatility [23]. Currency represents how old the data are, so how much time is passed since the last acquisition. On the other hand, volatility is the frequency of change of a value, so the average period for which it is considered stable. A value can be considered out of date if: 1 < currency volatility

(4)

The fields extracted from the database to carry out the data analysis are reported below (some of them are registered automatically others manually): • MachineId: it uniquely identifies the line and the machine to which the stop causal refers; • StartDate: indicates when the alarm warning was created by the machine; • EndDate: indicates when the alarm warning was closed;

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• Description: the machine downtime causal inserted by the operator, chosen; from a predefined list, to justify the alarm generated by the machine; • Notes: a free field used by the operator to enter additional information regarding the machine downtime. The Data Profiling allowed to investigate the most recurrent problems. In this regard, a Pareto Analysis was carried out which showed that ten stop causals (on the x axis) accounted for the 80% (on the y axis) of the total number of stops, as shown in Fig. 3. These stops are: tool change, anomaly in the laser functioning, piece waiting, tool presetting, missing material, downtime of the machine, reworking pieces, server error, machine setting, tool damages.

Fig. 3. Pareto Analysis on Stop Causals

In particular, a single stop causal was responsible for the overwhelming majority of stops. Since the “Description” field is mandatory, it’s Completeness is 100%, but as a matter of fact, the Completeness dimension was only 25% for the “Notes” field, however, 80% of the times the operator deemed important to compile this field, it was done for the same description of machine stop. Therefore, the Data Profiling step brought to highlight two main issues with the “Description” field: i) some descriptions were too generic and had to be split into more meaningful stop causals; ii) some stop causal descriptions were not the main cause of the machine stop, but rather a collateral effect. The strategy used in the Data Cleaning step to solve these issues exploited the “Notes” field entered by the operator, that also allowed to identify records with a “Description” inconsistent respect with the “Notes” field. Moreover, for the Data Enhancement step, the knowledge of operators was exploited to enrich the information content of every stop causal; interviews with the operators involved in these operations were carried out to: • Understand whether a specific stop causal was recorded due to machine failure or because the machine was stopped due to external reasons, for example, tool replacement. • Define the minimum and maximum theoretical duration of a stop due to a specific stop causal • Define the category of the stop according to the “4M”: Man, Machine, Method and Material The Data Cleaning step also involved an Error Correction task: null values and outliers were eliminated, multiple records related to the same machine stop event were

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merged, for example when a machine was restarted during the repairing activity causing the machine to fire the same alarm again. This was done by considering the “EndDate” and “StartDate” fields and the minimum/maximum theoretical duration of the stop according to the stop cause. At the end of the Data Cleaning step, the Accuracy Data Quality Dimension has been computed, resulting in a value of 46.5%, which clearly low (to improve the Quality Dimension some strategies will be presented in chapter 4). Having prepared the data with the highest value of Data Quality Dimensions possible, Jack Knife Diagram [24] were developed to support the manager of SDF in taking the right decision regarding the maintenance activities in the Machining Department Fig. 4.

Fig. 4. Methodology for Data Analysis and Tools Part

The Jack Knife Analysis allowed to produce the following two Jack Knife Charts reported in Fig. 5 and Fig. 6. These two diagrams help the manager to immediately individuate the most critical machines/failures based on occurrence and mean duration of the stop.

Fig. 5. Jack Knife Chart for All the Stop Causals

Fig. 6. Jack Knife Chart for all the Machines considering only failures

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Moreover, to facilitate the visualization of data, PowerBI (a Microsoft platform for data visualization) dashboards were built to visualize real-time data to extract all the information of interest to ease managerial decisions. An example of dashboard is shown in Fig. 7. Reporting the “Mean Time To Repair” (MTTR) maintenance indicator for different stop causals.

Fig. 7. PowerBI Dashboard

The availability of maintenance indicators allowed maintenance managers to understand the main problems to tackle optimizing resources consumption indirectly. To report some examples of improvement, it has been noticed that the tool change was responsible for several hours of machines downtime. This discovery led to rethinking how and when to perform the tool change: operators were trained to follow a new procedure that exploited a never used machine functionality that allows to substitute the tool in “masked time”, reducing tool change downtimes by 60%. Indeed, they moved from 46.1 h/month to 14.7 h/month on average of machine stoppages to wait for the tool change and this has an indirect effect on the reduction of defective product production. Another issue was related to episodes denominated as “lack of personnel”. As a matter of fact, sometimes two machines were supervised by a single operator, leading to a two-fold increase of downtime duration for the absence of a single worker: staff management optimization strategies were adopted to address this issue by increasing the number of operators per shift improving the monitoring along the process minimising the scraps too. Additional maintenance optimizations were introduced, and in total a reduction of the MTTR of about 10% was observed comparing the first 3 months of year 2023 with the first 3 months of 2022.

4 The Industrial Use Case Discussion The above-mentioned results highlighted several sustainable - oriented opportunities for the manufacturing company involved in the case. Indeed, this industrial case showed concrete opportunities in exploiting data analytics by small and medium enterprises

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operating in the manufacturing sector making them keep advantage from the diffusion of Industry 4.0 technologies to become more sustainable. Using a standard communication protocol easily accessible, as MT Connect, has led the company to have sensorized assets able to collect several data during the operating activity which if filtered, cleaned and analyzed enable to support decision-makers in optimizing asset management by facilitating their lifecycle extension based on an optimized resource consumption approach. Therefore, it is worth to be mentioned the opportunity to enhance the decision-making process of both managers and operators especially as far as maintenance activities is concerned. Indeed, data collection, transformation and analysis enabled to acquire fundamental information to generate useful knowledge to extend industrial assets lifecycle by also improving their current performances in terms of for instance reduced number of downtimes in tools changing and MTTR. Indeed, one of the most relevant outcomes was the possibility to firstly know the performances of the industrial assets and try to quantify their impacts trying to cope with them to improve the current state based on a structured methodology and specific steps. Indeed, it was also possible to observe the causes creating a certain issue facilitating both the manager and the operators in anticipating the problems for the future. Based on that, the company was able to exploit IoT opportunities empowered by an open standard communication protocol usable for any kind of machine brand with a sustainable-oriented purpose. Thanks to data analytics, hence, it was possible to reduce the waste generation and energy consumption previously caused by a non-proper asset management. Linked to that, it was possible to continue operating by using the current available industrial assets extending their lifecycle without asking for addition investments related to assets substitutions. This enabled the asset lifecycle extension and a reduction of resource consumption both in terms of new components needed for the asset and in terms of reduced number of scraps generated by the asset. Indeed, through this research contribution it was possible to preliminary cover the emerged scientific gap putting the basis to start considering the industrial asset as a complex product to be managed along its lifecycle by implementing and embracing the sustainable and circular-related principles. Regarding data quality dimension, there are still rooms for improvements to make the company pursue a twin transition including both digitalization and sustainability. Four key strategies are worth to be mentioned to improve the current state. • • • •

Process Driven strategies [25] OEE Quality Factor Reliability Enhancement [26] Natural Language Processing (NLP) [27] Condition Based Maintenance (CBM) [28]

4.1 Process Driven Strategies Process driven strategies can be adopted to tackle the root causes of the issues found in the original dataset, and these are listed below. First, it could be useful to introduce a standard list of stop causals: as outlined in sub-Sect. 3.1., many stop causals are too generic or do not represent the real issue that led to a stop of the machine. To address this issue, a new list should be defined, capitalizing the knowledge of the operators that are experts in the machining process and

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working with the machines every day. Second, in could be enriched the stop causals data with additional features: add to every stop causal, directly in the Database, additional pieces of information, such items have been mentioned in 4.2. Third, the Time to Repair (TTR) should be directly computed on the database: the TTR indicator is of paramount importance to assess the effectiveness of maintenance activities. The computation of the TTR should consider weekdays only, and aggregate different machine stops referring to the same event. Fourth, it should be reduced the mislabeling of machine stops: it has been considered worthwhile to develop a specific training program to instruct operators to correctly classify machine stops. 4.2 Overall Equipment Effectiveness (OEE) Quality Reliability Enhancement The Overall Equipment Effectiveness (OEE) indicator is based on three fundamental factors: availability loss, performance loss and quality loss. In this regard, quality loss, which factors out manufactured pieces that do not meet quality standards, is computed considering only the first quality gate inspection. However, many defects are found at the two downstream quality gates, which means that the current measured OEE is overestimated. In future developments, operators involved in all quality gates will be provided with the possibility to declare the number of discarded pieces. 4.3 Natural Language Processing (NLP) The Natural Language Processing (NLP) subfield of Artificial Intelligence is concerned on how to program computers to process, analyze and understand text and spoken words in the same way as human beings do. One possible improvement would be to develop an NLP algorithm that is able to analyze the data manually entered by the operator in the “Notes” free field, and suggest the stop causal, or to detect if the chosen stop casual might be potentially wrong reducing entropy and errors. 4.4 Condition Based Maintenance (CBM) Another strategy that might lead to improvements, from a maintenance point of view, involves the installation of sensors on the machines belonging to the machining department. These sensors could measure useful physical quantities, including but not limited to speed, acceleration, and temperature to enable condition-based maintenance. Furthermore, this data can be linked to machine stops to boost data analysis effectiveness covering additional sustainable-related aspects such as CO2 emissions reduction.

5 Conclusions Nowadays more than ever, resources consumption optimization is becoming essential for the entire society and especially for the manufacturing sector which is also experiencing the fourth industrial revolution with the advent of Industry 4.0 technologies. Nevertheless, it is still limited the attention over a circular asset management exploiting the potentialities of Industry 4.0 technologies in capturing data to extend assets

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lifecycle and improving their performances in terms of resources efficiency. Therefore, in this work it has been shown how Industry 4.0 can be applied even to an end-of-life machining department, prolonging assets’ lifecycle and promoting, as a consequence, an optimization of resources consumption. For this purpose, through a structured methodology based on sequential steps, different actions were put in place to successfully achieve these results. MT Connect, an open Industrial Communication Protocol, permitted the extraction of information from the machines of the department and the storage in an SQL Database. Then, to carry out an effective data analysis, a structured model was developed. The Data Quality Assessment highlighted some criticalities of the data set, so different quality improvement actions were performed. Among them, Data Cleaning played a fundamental role. After this step, it was possible to extract relevant and reliable information from the data set to begin a consistent maintenance analysis. It started with the computation of the main performance indicators, such as the MTTR and the frequency of downtimes. The developed model permits the application of the Jack Knife Analysis to discover the critical machines and downtime causes. In the end, some dashboards were built to support managers in taking strategic decisions about maintenance to optimize resources usage and extending assets lifecycle. Several practical contributions are worth to be mentioned. Starting from an old machining department unable to exchange any information with the external environment, it was possible, exploiting Industry 4.0 concepts, to build a system that collects and analyses data coming from the machines. The developed model can be easily replicated and applied to different companies, departments and machines. Indeed, the input data needed are quite simple to retrieve and MT Connect is easy to be applied. Moreover, the proposed structured methodology enables to support managers in taking better decisions on maintenance activities reducing scraps, energy consumption and environmental impacts, getting closer to the circular economy paradigm through the asset lifecycle extension. Based on that, additional improvement strategies are proposed such as the improved computation of OEE. In terms of theoretical contribution, this research enabled to define a model to extend the asset lifecycle under the circular economy principles and improve the asset performances in terms of resources optimization. These outcomes and the empirical evidences highlighted that data analysis techniques favor the adoption of circular economy related principles not only for product lifecycle management but also in asset management and related production processes. In terms of limitations, the model could be extended by integrating additional methodologies and indicators to better measure the achieved results and furtherly improve the current performances. Moreover, in future research, the methodology should be applied to several companies to grasp the obtainable benefits in different sectors and on the long run a longitudinal analysis could be performed.

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18. Pais, E., Farinha, J.T., Cardoso, A.J.M., Raposo, H.: Optimizing the life cycle of physical assets – A review. WSEAS Trans. Syst. Control 15, 417–430 (2020). https://doi.org/10.37394/ 23203.2020.15.42 19. Weerasekara, S., Zhenyuan, L., Ozek, B., Isaacs, J., Kamarthi, S.: Trends in adopting industry 4.0 for asset life cycle management for sustainability: a keyword co-occurrence network review and analysis. Sustainability 14(19), 12233 (2022). https://doi.org/10.3390/su1 41912233 20. Kilkenny, Monique F., Robinson, Kerin M.: Data quality: “Garbage in – garbage out.” Health Inf. Manag. J. 47(3), 103–105 (2018). https://doi.org/10.1177/1833358318774357 21. Batini, C., Scannapieco, M.: Data and Information Quality: Dimensions, Principles and Techniques. Springer International Publishing, Cham (2016). https://doi.org/10.1007/978-3-31924106-7 22. Wang, R.Y., Strong, D.M.: Beyond accuracy: what data quality means to data consumers. J. Manag. Inf. Syst. 12(4), 5–33 (1996). https://doi.org/10.1080/07421222.1996.11518099 23. Bovee, M., Srivastava, R.P., Mak, B.: A conceptual framework and belief-function approach to assessing overall information quality. Int. J. Intell. Syst. 18(1), 51–74 (2003). https://doi. org/10.1002/int.10074 24. Seecharan, T., Labib, A., Jardine, A.: Maintenance strategies: decision making grid vs jackknife diagram. J. Qual. Maint. Eng. 24(1), 61–78 (2018). https://doi.org/10.1108/JQME-062016-0023 25. Glowalla, P., Sunyaev, A.: Process-driven data quality management: A critical review on the application of process modeling languages. J. Data Inf. Qual. 5(1–2), 1–30 (2014). https:// doi.org/10.1145/2629568 26. Franzini, A., Polenghi, A., Roda, I., Macchi, M.: System-level overall equipment effectiveness for improving asset management performance: a case study application. In: Dolgui, A., Bernard, A., Lemoine, D., von Cieminski, G., Romero, D. (eds.) Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems: IFIP WG 5.7 International Conference, APMS 2021, Nantes, France, September 5–9, 2021, Proceedings, Part IV, pp. 410–417. Springer International Publishing, Cham (2021). https://doi.org/10.1007/978-3-030-85910-7_43 27. Payette, M., Abdul-Nour, G., Meango, T.J.-M., Côté, A.: Improving maintenance data quality: application of natural language processing to asset management. In: Márquez, A.C., Fernández, J.F.G., Díaz, V.G.P., Amadi-Echendu, J. (eds.) 16th WCEAM Proceedings, pp. 582–589. Springer International Publishing, Cham (2023). https://doi.org/10.1007/978-3-031-254482_54 28. Polenghi, A., Cattaneo, L., Macchi, M., Pasanisi, D., Pesenti, V., Borgonovo, A.: Development of an advanced condition-based maintenance system for high-critical industrial fans in a foundry. IFAC-PapersOnLine 55(2), 48–53 (2022). https://doi.org/10.1016/j.ifacol.2022. 04.168

The Role of Asset Ownership in PSS Theory: An Insight from Expert Interviews Oliver Stoll1(B)

, Shaun West1

, Fabiana Pirola2

, and Roberto Sala2

1 Lucerne University of Applied Sciences and Arts, Horw CH-6048 Lucerne, Switzerland

{oliver.stoll,shaun.west}@hslu.ch

2 Department of Management, Information and Production Engineering, University of

Bergamo, Viale Marconi, 5, 24044 Dalmine, BG, Italy {fabiana.pirola,roberto.sala}@unibg.it

Abstract. The offering portfolios of manufacturing firms have become more servitized, leading to various product-service system (PSS) offerings, traditionally classified as product-oriented, use-oriented, and result-oriented. In this classification, the ownership of the products or assets plays a vital role in characterizing the offering. With industry 4.0, manufacturing firms can exploit new technologies to provide digitally-enabled PSS. On the other hand, the digitally-enabled PSS offerings introduce a new component of complexity concerning their categorization into product-, use-, or result-oriented as new aspects must be considered other than the physical product ownership during the PSS configuration. This paper shows that manufacturing companies define product-, use-, and result-oriented offerings, not only considering the ownership aspects but considering a broader perspective on the topic (e.g., risk assumption, data ownership). Based on the data from nine expert interviews from five different organizations, it was found that ownership of products and assets needs to be reconsidered for the categorization of PSS, as it might not be enough for a complete categorization, especially with digitalization as an enabling technology for PSS offerings. Keywords: Product-service system (PSS) · Digitally-enabled offerings · Ownership categorization

1 Introduction The trend towards offering bundles of products and services, known as servitization, is gaining momentum as a total market strategy for enterprises aiming to gain a competitive advantage [1–4]. Companies believe that servitization can enable them to create more sustainable value-adding capabilities, making them more resilient to competition [5] and more profitable than firms that do not participate in servitization [6]. The shift towards integrated product-service offerings delivering value in use has driven servitization to become more customer-centric than product-based offerings [7–10]. Recent papers in the servitization research community outline current and future research streams [11–15], such as the concepts of product-service systems (PSS), © IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 412–425, 2023. https://doi.org/10.1007/978-3-031-43688-8_29

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product-service differentiation, competitive strategy, customer value, customer relationships, and product-service configuration [13, 15]. In particular, product service system (PSS) is a research field of servitization [13, 16, 17]. Baines et al., [16] label PSS as a particular case of servitization and describe it as expanding a firm’s market position by adding services to the traditional functionality of a product. Research on PSS has been influenced by the emergence of Industry 4.0 and the digitalization trend. Indeed, integrating digital technologies has led to the development of Smart PSS, which provides customers with customized and sustainable solutions integrating intelligent products with data-enabled services through physical and digital infrastructures. These technologies can be used to improve PSS design or enable and enhance PSS business models [18]. However, the detailed practices and models needed to design and deliver integrated products and services solutions are still a subject of PSS research, addressing the challenges faced by manufacturers along the route to servitization [19–21] and digital servitization. One of the leading frameworks conceptualizing PSS solutions is the one proposed by Tukker [22], which classifies PSSs mainly based on asset ownership (i.e., product-, use-, and result-oriented). Although this classification and model date back to the early 2000s, a recent analysis of PSS adoption in the industry shows that solutions such as leasing, renting, sharing, and pooling, which involve the provider retaining the product ownership, remain rarely offered by manufacturing firms [4]. A closer investigation of how practitioners perceive and implement the concept of PSS and the role of asset ownership in service offerings is needed to understand the state of the industry in the digital servitization journey. Yet in some industries, financial leasing is nevertheless necessary e.g., aero [23]. Given the setting in which Tukker’s [22] classification has been carried out, and because of the recent technological advancements that have driven the inclusion of digital technologies in PSS offerings, an investigation of the main drivers that differentiate product-, use-, and result-oriented PSSs in the industry is required. Specifically, centering the classification on the concept of ownership of the physical product may overlook some features related to specific service offerings or some new and relevant aspects related to the ownership of intangible assets generated during the PSS lifecycle (e.g., operational and business data) that can be used to drive service-related decisions and activities. It might be possible that companies find it problematic to market use- and result-oriented offerings – as described by Tukker [22] – because of a misalignment between the academic – ownership-based – definition of use- and result-oriented offerings and the industrial interpretation – influenced by the market requirements and new technological risk- and data-centered challenges. This paper aims to investigate, through in-depth interviews, how practitioners interpret Tukker’s [22] PSS classification and which are the main drivers that may be considered to describe the product-, use-, and result-oriented PSSs. To this purpose, nine experts working in manufacturing companies providing service solutions to their customers have been interviewed concerning their current offering. Insights based on the information collected have been compared with the expected results from Tukker’s [22] classification.

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To achieve this objective, the paper is structured as follows: Sect. 2 provides a brief theoretical background with the definition of the main framework used in PSS and digital servitization; Sect. 3 describes the methodology used to carry out this research; Sect. 4 reports and discuss the results of the interviews. Then, Sect. 5 concludes the paper with final remarks and future research directions.

2 Theoretical Background One of the earliest frameworks in the context of PSS was provided by Tukker [22], describing three categories of PSS: product-oriented, use-oriented, and result-oriented, plus eight subcategories. Tukker’s paper [22] is still one of the leading theoretical frameworks to describe PSS offerings and to frame business models within the servitization and PSS research community. According to Tukker [22], in product-oriented service offerings, the ownership of the product is transferred to the customer, who can receive on-call (e.g., maintenance, advice, and consultancy) or contract-based (e.g., maintenance) services when needed. The additional value is generated thanks to service provision. In use-oriented service offerings, the provider retains the ownership of the product and assumes the responsibility for maintaining it in an available state. The value is generated when the customer rents, leases, or shares the product to fulfill a need. Tukker [22]. In result-oriented service offerings, the provider retains the ownership of the physical product, and the value is generated by the customer while using it and is based on the outcome of its use. The category is divided into three sub-categories; i) Activity management/outsourcing, ii) pay per service unit, and iii) functional result. In general, the categories describe the process of outsourcing customers’ activities. In this classification, the ownership of the physical product is the main discriminant that differentiates product-oriented versus use- and result-oriented PSS. The advent of Industry 4.0 and the spread of digital technologies have emphasized the transition of companies towards digital servitization business models, since digital technologies represent a fundamental enabler for the provision of advanced services (e.g., data- and performance-based). Digital servitization is an emerging sub-category of servitization research, shedding light on how digital technologies change and alter the servitization research field, providing new research opportunities [24]. With digital servitization, new businesses are possible, which enables firms to offer PSS solutions. In this context, Kohtamaki et al., [10] analyzed the digital servitization business models of companies considering three main dimensions (solution customization, solution pricing, and solution digitalization) and defined five separate business models: • Product-oriented service provider reflects the product-oriented service offerings in which a company provides products and add-on services. • Industrializer emphasizes product and service modularity to combine solution customization with efficient order delivery. • Customized integrated solution provider refers to companies that provide highly customized product-service solutions mainly based on the concept of availability, in which digital technologies play a central role in accurate data acquisition, analytics, and remote diagnostics.

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• Outcome providers refers to result-based solutions in which providers own and sell the value created by the product; this business model requires a high digitalization level to measure and optimize product/asset performance. • Platform provider refers to a fully-fledged digitally enabled service business model where the company is a platform provider that connects various providers and customers. Considering the classifications described above, it emerges that the ownership of the physical product is no longer the core element of service offerings classification since digital technologies allow greater granularity of services. Understanding whether the Tukker [22] classification still fits manufacturing companies’ new, data-enabled services and PSS offerings becomes essential. Thus, based on the three service offerings defined by Tukker [22] and the five business models defined by Kohtamaki et al., [10], the practitioners’ interpretation of PSS classification and the role of product/asset ownership will be investigated, leveraging on the insights gained from nine expert interviews.

3 Methodology The in-depth interviews were designed and conducted following the recommendations of Hennink et al., [25]. The goal of the sampling (see Table 1) was to create a homogeneity [25] around the topic of servitization and PSS. The participants qualified as experts by having five specific characteristics, which were: 1. Their role within the organization needs to be related to servitization and/or PSS. For example, an interviewee responsible for developing services or products, or influencing the company’s strategy was considered. 2. Interviewees should be closely involved in digitalization initiatives - for example, engagement in developing smart services or digital twins. 3. The interviewees should know the theoretical concept of PSS or be able to understand and describe it. 4. The organization in which the interviewee work has to be a manufacturing firm operating in a B2B environment. 5. The interviewees had to have a leadership position within the organization. For example, a candidate should be responsible for one or more of the points mentioned above. Recruitment followed a gatekeeper strategy, where trusted leaders familiar with the characteristics provided a list of suitable candidates. The candidates were contacted via email and were provided with the interview script. A total of 16 candidates were contacted. Of the 16, nine were interviewed and are presented in Table 1. The number of interviews is considered adequate for the research since thematic and coding saturation was reached, according to Hennink et al. [26]. The interviewees can be considered homogenous due to the characteristics required, and with the thematically focused interview script a smaller sample can be sufficient to reach saturation. Based on the continuous data analysis, the data quality seemed adequate, as no new learnings were made later in the interview data collection process. According to Hennink et al. [26], nine participants can be sufficient to reach saturation under the conditions of homogeneity of the participants and the study’s focus—similar findings were provided by Guest et al. [27].

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O. Stoll et al. Table 1. Sampling characteristics of the interviewees

Interviewee

Industry

Years of experience

A

Aircraft OEM

14

B

Aircraft OEM

20

C

Pump OEM

22

D

Ship Engine OEM

3

E

Turbo machinery OEM

6

F

Turbo machinery OEM

20

G

Building Automation OEM

25

H

Medical Printer OEM

12

I

Medical Printer OEM

11

3.1 Data Collection The interview process consisted of four stages, shown in Fig. 1. The candidates were contacted via email, the main body entailed a short request for the interview, and the interview guide was provided as an attachment. The interview guide consists of background information questions about the organization and the interviewee’s role. It then delves into questions about product-oriented, use-oriented, and result-oriented service offerings, including examples of such services, asset ownership, the role of digitalization, and value creation shown in Fig. 2.

Fig. 1. In-depth interview data collection process

The in-depth interviews were conducted one-to-one in a mix of physical and digital presence, and all were recorded with the interviewees’ permission. For the digital interviews, video conferencing software was used, for example, Microsoft Teams. Software such as OBS Studio and recording devices from RØDE were used for the recordings. The interview duration was between 46 to 71 min and the average duration per interview was 57 min; the total interview duration was 513 min. The recordings were transcribed using the services from gotranscript.com. They provided precise verbatim transcriptions that were double checked to ensure their quality. Then the transcripts were anonymized by removing the interviewees’ names and company names. After each interview, reflections were made on the content and initial learnings to ensure that the data quality was appropriate and that the interview script was fit for purpose. As a result of the reflections, it was established that towards the end of the collection process, no new insights were gained and that the interview guide provided similar results in each application.

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Fig. 2. Structure and questions explored through the interviews for this study.

4 Results and Discussion This section summarizes, presents, and discusses the relevant data. First, the productoriented, use-oriented, and result-oriented service offerings, which can be existing or future offerings, of the companies involved in the interviews are reported and discussed. These results indicate the ownership role. Most of these offerings do not focus on the ownership of the asset or product, yet the examples suggest that the ownership remains with the customer. 4.1 Product-Oriented Service Offerings The value creation in product-oriented service offerings is represented in business models where the value is mainly generated through sales of products and by adding some services to the products; the two sub-categories are product-related services and advice and consultancy [22]. The product-related services proposed by Tukker [22] can be, for example, a maintenance contract, a financing scheme or the supply of consumables, or take-back agreements. The results of the interviews support Tukker’s [18] categorization and the digital servitization business models from Kohtamaki et al. [7]. Kohtamaki et al. [7], described two digital servitization business models (product provider and industrializer), which can be considered aligned with the product-oriented service offerings from Tukker [22]. The product provider describes a traditional product vendor, and the industrializer describes a product vendor with a more advanced service portfolio emphasizing some external value creation with services. Product-oriented service offerings traditionally have the asset or product ownership clearly with the customer, as depicted in Table 2. However, the emergence of digital technologies has opened up new possibilities and enabled companies to transition towards use-oriented service offerings. The use of digital technologies, such as the Internet of Things (IoT) and data analytics, has enabled companies to create service offerings that are more focused on delivering value to the customer based on the use of the asset rather than its ownership. This shift towards use-oriented services has been made possible by the fact that digital technologies make it easier to collect data about how assets are being used, and this data can then be used to improve the service offering and create new value for the customer. Thus, digital technologies enable the transition from product-oriented to use-oriented service offerings.

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O. Stoll et al. Table 2. Examples of product-oriented service offerings provided by the interviewees

Industry

Result-oriented service offering

Asset owner

Aircraft OEM

We provide a digital service for our product that includes easy access to the aircraft manual, which spans 23,000 pages, making printing impossible. Digital delivery is more convenient for customers and helps manage the overwhelming content

Customer

Pump OEM

Our traditional business involves selling customized items for pumps in various industries. These items are selected from a list and sold as one-off assets

Customer

Ship Engine OEM

Our product, TRACK (Firm integrated digital Customer expert), is an onboard health monitoring system that collects signals from sensors on the engine and provides immediate support through onboard engine diagnostics

Turbo Machinery OEM

In addition to our core offerings, we provide a Customer range of MRO services for spares and field service. We have also implemented web shop activities to make it easier for customers to order parts directly from our organization via tablet or mobile. They can view the availability and lead time of parts, among other things, in addition to the many other great offerings we provide

Building Automation OEM An example of our high responsibility is the Customer maintenance of our smoke detectors, which are critical for life safety. We use data from the detectors to offer tailored services based on usage, such as more frequent maintenance for detectors in high-traffic areas Medical Printer OEM

We offer a wide range of services including web parts, spare parts, technical services, training, operator, and user support. Our validation services ensure pharmaceutical compliance, and we have a hotline and a global service network

Customer

4.2 Use-Oriented Service Offerings The value creation in use-oriented service offerings is still closely related to the product an organization sells, and according to Tukker [22], the ownership of the product remains with the organization (provider). The sub-categories are product leasing, renting or sharing, and product pooling. The corresponding business model from the digital servitization perspective could be an integrated solution provider explained by Kohtamaki et al. [10], although the ownership of the product is not discussed.

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Table 3. Examples of use-oriented service offerings provided by the interviewees Industry

Use-oriented service offering

Asset owner

Aircraft OEM

The customer operates the aircraft [via an operational or financial lease] for a specified number of flying hours, and when they require spare parts, we supply them

Customer

Pump OEM

The goal is to offer customers the option to purchase a certain volume of air instead of a physical capital item

Provider

Ship Engine OEM

After the installation of the engine, it becomes a Customer challenging task to detach it as a whole unit. The organization has thoroughly examined this concern and found that it is highly implausible to provide such a use-oriented service [leasing, renting or pooling] for the primary engine. Nevertheless, there may be a possibility to provide this service for subsystems

Turbo Machinery OEM

I believe the next level of service offered by MROs Customer (Maintenance, Repair, and Overhaul) in terms of spares and field services will involve pay-per-use solutions. We have already applied this approach in some areas, particularly for large fleets of buses. In this model, the customers entrust us with all service activities, and payment is made based on the distance covered. We charge a certain amount per kilometer, and in return, we take care of everything, from small repairs to heavy repairs and standard overhauls

Building Automation OEM The concept of use-oriented offerings is crucial Customer here. For example, in the case of meeting rooms, the primary use is for conducting meetings, which means they need to be clean and ready for use after each meeting Medical Printer OEM

The only use-oriented service offering we currently Customer have that operates on a cost-per-click model is our volume discount for ink purchases made annually

Table 3 highlights the examples reported by the interviewees of use-oriented service offerings, which can be existing or future offerings. These examples show how industries interpret the concept of result-oriented PSS: the results indicated that the ownership is not exclusively with the organization (provider) as suggested by Tukker [22]. For some interviewees, letting the customer own the product is preferable. In some cases, the ownership has to be with the customer or an intermediate leasing firm because the products are very expensive, so it is not financially feasible for an organization to own

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them. As Interviewee F explained: “Normally, machine manufacturing companies are not happy to have the assets in their books. It’s better to have the assets in customers’ books because of capital binding, so it’s coming from a financial view.” Both modes of ownership can occur with use-oriented service offerings. Nevertheless, a characteristic that applies to use-oriented service offerings is that the use of the product is directly linked to the revenue of the organization providing the PSS offering. The business models tied to the use of the product require more attention to the services and the customer’s value creation process, as the influential factors can and will affect the organization. The interpretation provided by the practitioners of use-oriented service offering is more aligned with the business model proposed by Kohtamaki et al. [10], which suggests focusing on the concept of availability. In this, digital technologies play a central role in accurate data acquisition, analytics, and remote diagnostics, without distinguishing the ownership of the asset, that is often strictly related to the financial availability of the manufacturer. 4.3 Result-Oriented Service Offerings Tukker [22] describes his third category as an agreement on a result by the organization and the customer without a specific product involved. The category is divided into three sub-categories: i) activity management/outsourcing, ii) pay per service unit, and iii) functional result [22]. In general, the categories describe the process of outsourcing customers’ activities. Kohtamaki et al. [10], provided two digital servitization business models which could fit into the third main category from Tukker [22]. First, the outcome provider business model aims to price the performance of the PSS, which is owned by the organization (provider). Second, the platform provider business model is a servicedominant model where the organizations enable interactions. Table 4 presents result-oriented service offerings, which are existing offerings or potential future offerings. As for the previous case, the sentences reflect what practitioners mean by result-oriented offering. Relating to value creation in the context of result-oriented service offerings, the ownership, as described in the use-oriented section, is not relevant as a characteristic of result-oriented service offerings. Offerings in which an organization’s revenue is tied to a result or performance achievement in collaboration with the customer can be considered the foremost distinguishing aspect of result-oriented service offerings. Overall, the interviewees agreed that these services provide the greatest opportunity for external value creation. As told by Interviewee D: “I think one sales colleague of mine coined it as the holy grail of the business, because, of course, when you consider the fuel consumption of … an LNG carrier, for example, and you get a little share of the savings that you can provide to them, that’s huge.” However, they also corroborated that determining the business value is critical for this kind of offering and that the value is tailored to each customer. The challenges here are that understanding the business context underlying the business models is difficult. The dependence is a source of risks that need to be considered for such services. The nature of these business models is very complex as the interactions, and the system itself are relevant to understand and define the value.

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Table 4. Examples of result-oriented service offerings provided by the interviewees Industry

Result-oriented service offering

Asset owner

Aircraft OEM

Our material availability contract is still focused on Customer delivering results. Additionally, we have several contracts where we are motivated by incentives, meaning that the better we perform, the greater our rewards

Pump OEM

The result-oriented service offering focuses on availability and uptime, businesses can minimize downtime and maximize productivity, which can have a significant impact on their bottom line

Ship Engine OEM

One of my colleagues described it as the “holy Customer grail” of our business. This is because we can offer significant savings to customers, especially in cases where fuel consumption is a major factor, such as with LNG carriers

Turbo Machinery OEM

In addition to committing to certain performance Customer levels, such as response times and uptime, this contract is incentivized based on achieving specific results. For example, a customer has a target of 98% availability, which means that we need to ensure their equipment is up and running for at least 98% of the time

Unclear

Building Automation OEM We also have a bonus model where the customer Customer pays for the promised savings, and we get paid more if we do better and give money back if we do worse. Both sides need to adapt, and we need to ensure a profit Medical Printer OEM

The ultimate goal is for the company to provide Unclear customers with equipment and receive a share of the profits generated by its successful operation. This could be in the form of different models, such as performance-based or output-based

4.4 Asset Ownership The last topic analyzed in the interview was related to how, in general, the ownership of the assets is explicitly mentioned in the context of PSS offerings. Table 5 showcases the prominent insights on the role of asset ownership in the context of PSS offerings. In this context, the definition of an asset is not limited to physical products but includes any resource belonging to an organization with an economic value. The interviews have shown that asset ownership is not a key characteristic for use- or result-oriented service. There were examples of use and result-oriented offerings where the ownership of the product or asset remained with the customer. There was a financial argument by an interviewee, who said that owning the product or asset is a financial

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Table 5. Summarized explicit mentions of the role of asset or product ownership in the context of PSS offerings Interviewee Summarized mentions on the role ownership of the asset A

The interviewee suggests that ownership in the aviation industry is complex and can vary depending on the type of aircraft and service being provided. There may be cases where the aviation company owns the aircraft and sells it as a service, while in other cases, the aircraft may be owned by the customer

C

Typically, the vendor would have the ownership of the asset. The usage would be with the customer

F

The utilization and financial benefits of the business model are contingent on the ownership of the assets. Usually, machine manufacturing companies prefer not to have the assets on their balance sheets, but rather on the customers’ balance sheets. This approach minimizes capital binding and is often preferred from a financial perspective. In essence, the business and usage cases serve to transform the business into a performance-driven model

G

Ownership of data is particularly unclear for digital services, as the data itself constitutes the asset. The crucial issue then becomes determining who rightfully owns this data, rather than the product that the data serves. It is possible to replace a product, but it is not possible to replace the data. Therefore, the most pressing concern is who has control over the data and what they intend to do with it. In light of this, it is important to address the question of whether the customer or the product supplier has ownership of the data

I

There are two possible ownership variations for the asset, and each has its own advantages. In the first variation, the customer owns the core of the asset and pays the supplier depending on its performance. Alternatively, a leasing concept can be adopted where either the supplier or a bank owns the asset and the customer pays a fee based on usage or on a monthly basis. There is no clear advantage of one model over the other, as it largely depends on the customer’s financial situation and needs. As a pragmatic approach, it would be best to offer both ownership variations, as well as some intermediate steps between them. This would allow customers to choose the option that best suits their requirements

liability that an organization does not want to have. Another stated that in their market it is not possible to provide leasing or rental agreements due to the nature of the industry. Still, the organization provides the entire range of PSS offerings e.g., the aircraft OEM which offers the whole range of PSS but does not own the aircraft. This highlights the importance of understanding the specific market and customer needs when designing PSS offerings. It also suggests that PSS offering definitions should consider flexible ownership options to cater to different configuration options. When it comes to digital services, an emerging issue, highlighted by interviewee G, is related to the ownership of data that are the fundamental enablers of use- and result- oriented service offerings. It becomes urgent to define who is the owner of product data, which data are gathered and used, and how and for what purposes this data is used.

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In reflection, the use of the Tukker [22] framework of asset ownership is often an oversimplification of the reality, as emerged from the interviews with industry experts. For example, in aerospace, financial intermediaries, not the airlines, own the aircraft and the engines and lease them to the airline. The airline then buys services for the maintenance of the assets. Another example is in the power industry, where the power company buys the asset and then contracts maintenance and sometimes O and M services from the OEM [28, 29]. Here we see a clear separation between asset ownership, control, and performance commitments, suggesting we must focus on responsibility and risk transfer rather than ownership as in the Tukker [22] model. Ownership is often driven by tax or local legal considerations or industry ‘norms’.

5 Conclusions and Recommendations Through nine expert interviews, the paper analyzed how manufacturing companies classify PSS into product-oriented, use-oriented, and result-oriented categories. In particular, there have been useful reflections on the role of physical product ownership and digital technologies in the definition of service offerings and their related business models. Based on the information provided, it can be concluded that product-oriented service offerings are perceived as business models that generate value through sales of products and by adding some services to the products. This supports the categorization by Tukker [22] and the digital servitization business models from Kohtamaki et al. [10]. Regarding use-oriented service offering, the interviews showed that ownership is a characteristic of PSS, but it is not exclusively with the organization (provider), as suggested by Tukker [22]. Indeed, in some cases, ownership has to be with the customer or an intermediate leasing firm if it is not financially feasible for the provider to own the product. Then, the main distinguishing factor of this type of PSS is no longer the ownership concept, but more the responsibility and risk transfer. Still, the use of the product is directly linked to the revenue of the organization providing the PSS offering. The business models tied to the use of the product require more attention to the services and the customer’s value creation process, as the influential factors can and will affect the organization’s revenue. The same applies to result-oriented services. The distinguishing characteristic that emerged is that the organization’s revenue is tied to achieving a specific result or performance in collaboration with the customer. In conclusion, it is possible to state that using ownership as a main driver to classify service offerings is often an oversimplification, and a differentiation mainly based on what determines the organization’s revenue should be more appropriate. From a theoretical point of view, this paper contributes to the research in the PSS field since it provides evidence from industry of how PSS is perceived among practitioners bringing to light some new factors that should be considered while defining PSS solutions. Thus, a future step should be refining the PSS classification framework, mainly focusing on the sources of revenue and the role of digital technologies as enablers of new and advanced service offerings. However, the main limitation of this paper is that it is based on the interviews of nine industry experts. More comprehensive research should be carried out to generalize the results. The PSS community could benefit from an improved definition of PSS offering characterization. This could increase research efficiency by improving the data collection

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and analysis of PSS offerings. The use- and result-oriented aspects of PSS could be more clearly defined, and maybe lead to a better understanding of PSS and the related dynamics in its respective servitization contexts. Equally, practitioners can benefit from a more aligned definition and the resulting findings from academia. The usage and the results are a major concern when it comes to use- and result-oriented service offerings. To better understand how PSS knowledge can be developed in a way that practitioners can better benefit from it, future research is required. A starting point could be to align the definitions of academia and practitioners and, based on that, improve current and future models relating to PSS. Moreover, a redefined definition could lead to a reinterpretation of existing knowledge and holds the potential for advancing the research within the community.

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16. Baines, T., Lightfoot, H.W., Evans, S., et al.: State-of-the-art in product-service systems. P. I. Mech. Eng. B.-J. Eng. 221, 1543–1552 (2007) 17. Baines, T., Bigdeli, A.Z., Sousa, R., et al.: Framing the servitization transformation process: A model to understand and facilitate the servitization journey. Int. J. Prod. Econ. 221, 16 (2020) 18. Pirola, F., Boucher, X., Wiesner, S., et al.: Digital technologies in product-service systems: a literature review and a research agenda. Comput. Ind. 123, 103301 (2020) 19. Baines, T., Lightfoot, H.W., Kay, J.M.: Servitized manufacture: practical challenges of delivering integrated products and services. P. I. Mech. Eng. B.-J. Eng. 223, 1207–1215 (2009) 20. Ferreira, R., Scur, G.: Nunes B preparing for smart product-service system (PSS) implementation: an investigation into the Daimler group. Prod. Plan. Control 33(1), 15 (2021) 21. Reim, W., Parida, V., Ortqvist, D.: Product-Service Systems (PSS) business models and tactics - a systematic literature review. J. Clean. Prod. 97, 61–75 (2015) 22. Tukker, A.: Eight types of product–service system: eight ways to sustainability? Experiences from SusProNet. Bus. Strateg. Environ. 13, 246–260 (2004) 23. Tot Westervlier G.V.H.: Aircraft leasing, a challenge since the 70s. In: Tu Anh, T., Hang, T.H.N., Zuniga Alcaraz, C., Boosten, G., (eds) Transportation Research Procedia. Elsevier B.V., pp 110-117 (2021) 24. Paschou, T., Rapaccini, M., Adrodegari, F., et al.: Digital servitization in manufacturing: A systematic literature review and research agenda. Ind. Mark. Manage. 89, 278–292 (2020) 25. Hennink, M., Hennink, M.M., Hutter, I., et al.: Qualitative Research Methods. SAGE Publications, London (2020) 26. Hennink, M.M., Kaiser, B.N., Marconi, V.C.: Code saturation versus meaning saturation: how many interviews are enough? Qual. Health Res. 27, 591–608 (2017) 27. Guest, G., Bunce, A., Johnson, L.: How many interviews are enough? Field Meth. 18, 59–82 (2016) 28. Gavazza, A.: Leasing and secondary markets: Theory and evidence from commercial aircraft. J. Polit. Econ. 119, 325–377 (2011) 29. Bandinelli, R., Gamberi, V.: Servitization in oil and gas sector: Outcomes of a case study research. J. Manuf. Technol. Manag. 23, 87–102 (2011)

Identifying Customer Returns in a Printed Circuit Board Production Line Using the Mahalanobis Distance Endre Sølvsberg1(B)

, Simone Arena2

, Fabio Sgarbossa1

, and Per Schjølberg1

1 Norwegian University of Science and Technology, Trondheim, Norway

[email protected] 2 University of Cagliari, Cagliari, Italy

Abstract. This paper discusses as its primary research question the viability of using the Mahalanobis Distance as a multivariate method for detecting outliers in an industrial setting. An algorithm is used to detect future customer returns in a printed circuit board production line situated in Sibiu, Romania. From the literature, there is a lack of methods, tools and guidelines concerning the paradigm of Zero-Defect Manufacturing. The novelty of the method presented includes separation of highly specialized, future outliers from other outliers, and further automation using Python, a Docker container, a graphical user interface, a searchengine and a reporting tool. This allows the method to be used without external assistance. The data used is extracted industrial datasets from Continentals datalake. The algorithm detects 20% of future outliers and has been implemented by Continental. This can possibly be improved by increasing domain knowledge. The generality of the algorithm in principle allows for use at any of Continental’s production lines. There are strong assumptions regarding the requirements for the method, including benefits of employing domain knowledge critical variable identification and detection rate improvements. Further improvements of detection rate are also discussed. The paper concludes that the algorithm can detect a percentage of highly specialized outliers with simple automation in Python, but also acknowledges limitations in terms of increased demands from data quality and domain knowledge. Keywords: Industry 4.0 · Multivariate Analysis · Automation

1 Introduction Nowadays, the notable emphasis on sustainability of manufacturing companies has determined a specific focus on production aspects including flexibility, quality, reliability, productivity, operational efficiency, and cost performance [1]. To meet the growing challenges of today’s dynamic environment, these companies must prioritize the implementation of innovative and competitive strategies to achieve the goal of sustainability improvement of their processes and systems. In this context, one of the main challenges is © IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 426–438, 2023. https://doi.org/10.1007/978-3-031-43688-8_30

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related to product quality since it could lead to severe consequences on customer satisfaction, companies’ reputation, financial performance, environmental impact, and resource usage. Therefore, production processes should be continuously improved through the implementation of the so-called zero-waste and zero-defect strategies aiming at preventing waste, reducing manufacturing lead times, limiting resources, and producing high-quality goods [2]. The advent of Industry 4.0 and the maturing of its enabling technologies have substantially boosted the rapid digital transformation to manage production complexity and enhance data management for processes understanding and advanced problem solving. This has contributed to the evolution of the traditional manufacturing industries to a new class of smart factories characterized by fully interoperated, automated, and optimized production flow through the adoption of emerging technologies such as CyberPhysical Systems (CPS), Artificial Intelligence (AI), Internet of Things (IoT) and Big Data [3]. The integration between physical and digital processes, where sensors, connected devices, equipment, and production systems continuously collect and share data, has enabled a consistent approach based on data-driven decision-making strategies. The valuable information and knowledge provided by this vast amount of available data can be leveraged into a plethora of potential applications. One promising application concerns production quality improvement and waste reduction. In this scenario, Zero Defect Manufacturing (ZDM) is a disruptive paradigm focused on data-driven approaches aiming at realizing the vision of zero defects following the concept of “to do the right thing the first time” [4]. Thus, ZDM exploits Industry 4.0 tools to prevent defects and errors in a production process through detection, prediction, prevention, and repair strategies [5]. As precisely reported by Wang [4] and Psarommatis et al. [6], these strategies include the following steps: (i) data acquisition from sensor-equipped machinery, collection, storage, and cleaning; (ii) automatic signal processing, filtering, and feature extraction; (iii) data mining and knowledge discovering for diagnosis and prognosis; (iv) gathering information about monitored defects; (v) online predictive maintenance and (vi) re-configuration and reorganisation of the production process. In the last years, by exploiting the potential of the enabling technologies of Industry 4.0, two specific strategies, i.e. detection and prediction, have significatively attracted the researchers’ interest [7]. Especially, the great potential of AI and Machine Learning (ML) algorithms have created new opportunities for more effective quality management and advanced problem-solving since they are capable to process complex datasets analysing different factors and scenarios, identifying structures and patterns, predicting future behaviours, and making optimal decisions [2]. Concerning ZDM detection strategy based on AI approaches, Tabernik et al. [8] proposed a deep learning technique for surface-anomaly detection within electric commutator production, Okaro et al. [9] presented a ML algorithm for the automatic detection of faults for addictive manufacturing applications, Soualhi et al. [10] used an unsupervised classification technique for outliers detection and diagnosis for quality assessment purpose. Concerning ZDM prediction strategy, Peres et al. [11] adopted different ML algorithms aiming at predicting dimensional defects in a real automotive multistage assembly

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line, Wang K. et al. [12] proposed a deep learning approach for batch process quality prediction, Ranjan et al. [13] presented an AI-based technique for quality prediction in micro-drilling. Finally, thanks to the increasing interest in these topics, several comprehensive reviews are recently proposed aiming at providing the state-of-the-art perspective, the emerging open challenges, and the future directions [1, 2, 6, 7, 14–16]. As emerged from [16], the current literature on ZDM shows a lack of methods, tools, and guidelines for its proper implementation in manufacturing facilities, especially the adoption of data-driven approaches and technique is still a challenging aspect. Thus, this paper presents the preliminary development of a Machine Learning algorithm for the detection of customer returns from a Printed Circuit Board (PCB) production line. The main goal is to provide an easy-to-implement tool to perform effective diagnostic tasks in supporting an agile and informed decision-making process. The anomaly detection process is carried out by adopting the Mahalanobis Distance (MD) to monitor and recognize the anomalous observations on the sensors’ data. The remainder of this paper is organised as follows: Sect. 2 describes the production line used as a pilot case. Section 3 illustrates the methodology used to detect customer returns and the ML algorithm implemented. In Sect. 4, the achieved results are presented and discussed. Finally, conclusions and future research are reported in Sect. 5.

2 Continental Pilot Case This chapter will first outline the Continental pilot, with challenges and goals. The chapter also presents the resulting research problems from Continental’s side. Subchapters include a description of the EU project QU4LITY and a detailed description of the pilot. The main challenge concerning the Continental pilot case deals with faulty PCBs undetected by the in-line testing suite and manual testing, resulting in customer returns. The main goal was to use available test data from Continental’s datalake to identify specific PCBs that are candidates for future customer returns through extraction of criticalto-quality (CTQ) variables, multivariate statistical analysis and detection of specific outliers related to future customer returns. The resulting research problems were then to: (i) find a suitable statistical method for identifying outliers specifically related to customer returns and separate these from other statistical outliers and normal units, and; (ii) examine the viability of automating the method to allow for rapid in-line sampling and analysis. This would ideally also allow for Continental personnel to utilize the method on site with little or no external expert assistance. 2.1 QU4LITY QU4LITY, or “Autonomous Quality Platform for Cognitive Zero-defect Manufacturing Processes through Digital Continuity in the Connected Factory of the Future” was an EU project, part of the Horizon 2020 program and was active between 2019 and 2022. The project lead was ATOS Spain, with 45 partners including large industrial corporations, small and medium-sized enterprises (SME)s, research institutes, universities, digital

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innovations hubs and industrial associations. In QU4LITY, there were 14 lighthouse projects, of which five were manufacturing equipment pilots and the remaining nine process pilots. 2.2 Pilot Description The Continental pilot, one of the nine process pilots, concerns a combined Surface Mount Device (SMD) and final assembly line situated in Sibiu, Romania. The manufacturing capacity is on average 30–40 million units per year. The four expected outcomes from the pilot were data mining in production systems to provide early indicators and trends from process signals, facilitate the creation of new applications that include the entire value chain, digital modelling and zero-defect strategies and physical interpretation and initiation of real-time reaction plans for shop floor visualization management. This paper focuses on the first expected outcome. The pilot group consisted of members from Continental, ATB Bremen, SINTEF and the Norwegian University of Science and Technology, and included multidisciplinary expertise within the fields of IT, quality engineering, domain expertise and statistical analysis. The production line guides the PCBs through laser marking, paste printing, automatic placement machines and reflow ovens. In addition, there are three in-line testing stations; solder paste inspection, automatic optical inspection and a final in-circuit test. Finally, there are sensors placed along the production line. These are product specific, environmental and line equipment sensors.

3 Methodology This chapter describes the methodology employed in the paper. It also describes some challenges and requirements and an outline of the data extraction limitations. The chapter discusses the exploratory analysis through descriptive statistics and both single- and multivariate methods in detail. It also explains how the Mahalanobis Distance (MD) was selected, with some justifications. A subchapter describes the MD with a bit more detail. Initial pilot group discussions included identifying a suitable unit for analysis. Data for the unit had to be available from the datalake, and the data should also be as complete as possible, with little to no missing variable data. PCBs for automotive cameras were selected as the best candidate. External pilot members were then given access to the part of the datalake where this unit was situated. This was again restricted to test data, and no production data was made available. A challenge when extracting data from the datalake at Continental was structure. Data is structured so that 20–30 columns and around 1700 lines describe one specific unit. With no initial domain expertise, a decision was made to focus on data from one of the in-line testing stations. The data then had to be formatted to allow for the use of statistical software. To avoid including non-pertinent variables and to maximize probable CTQ variables, domain expertise from on-site in Sibiu was subsequently included in the pilot group, resulting in a dataset of confirmed customer returns, increased knowledge on how data was organized for the different production and test steps throughout the

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production process, in addition to the identification of probable CTQ variables from all steps of the process. These variables were extracted from the datalake and formatted to allow for analysis. Historical datasets were used based on test data from the production line, datasets with confirmed customer returns and constructed datasets with various numbers of confirmed customer returns and sample sizes from the production line. An initial descriptive analysis was done to get an overview of the datasets. This included means, standard deviations, min, max and quartile values, skewness and kurtosis. A Kolmogorov-Smirnov test was conducted to test for normal distribution [17]. A correlation matrix was constructed to detect linear relationships between variables using Pearson correlation. As part of the exploratory analysis an Individual Value and Moving Range (I-MR) analysis was performed for all variables to look at performance over time and to identify anomalies like outliers, trends, shifts, oscillations and so on, based on the tests utilized. I-MR is an individual counterpart to the traditional X-Bar R chart used in Statistical Process Control (SPC) [18]. The analysis detected anomalous data, most importantly samples exceeding the 3-sigma upper control limit. The testing was done using 100 sample time series from start to end of the datasets. An example chart from testing is shown in Fig. 1. The software used is Minitab.

Fig. 1. Example I-MR chart.

Other single variate exploratory tests include Run Chart tests, an F-test comparing a random dataset with a confirmed customer returns dataset and Grubbs’ tests on all variables. This initial exploratory testing and analysis using univariate methods was able to identify two of the units from the confirmed customer returns dataset in the Ftest and Grubbs’ tests. No other single variate testing found any significant difference between normal and customer returns datasets.

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The first multivariate test conducted used a Z-transformation on all variables in a random and a customer returns dataset, using absolute values to determine mean and cumulative sigma. An F-test comparing the two datasets found them to be significantly different, and a boxplot found two extreme outliers in the customer returns dataset. The second test conducted was the Hotelling’s T2 test for Generalized Variance, which is a multivariate counterpart to I-MR and X-Bar R charts [19]. The chart is shown by: −

−

T 2 = n(X − X )S −1 (X − X ),

(1)

where 1 Xijk , n

(2)

1 X jk n

(3)

n

X =

i=1

−

m

Xj =

k=1

and

⎡

2

S 1 S 12 ... S 1p

⎤

⎢ ⎥ ⎢ ... S 2 ... S ⎥ 2 2p ⎥, S=⎢ ⎢ ... ... ... ... ⎥ ⎣ ⎦ 2 ... ... ... S p

(4)

where 1 2 Sjk , m

(5)

1  (Xijk − X jk )2 , n−1

(6)

1 Sjhk m

(7)

1  (Xijk − X jk )(Xihk − X hk) n−1

(8)

m

2

Sj =

k=1

n

Sjk2 =

I =1

m

2 Sjh =

k=1

and n

Sjhk =

i=1

|S| is the determinant of the sample covariance matrix and |Si | is the determinant of the sample covariance matrix for sample i. |S| is the center line of the chart. Upper and lower control limits are calculated by (Fig. 2). LCL =

|S| 1/2 (b1 − 3b2 ), b1

(9)

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where μ

1 (n − i), μ (n − 1) i=1 ⎡ ⎤ μ μ μ (n − i)⎣ (n − j + 2) − n − j ⎦,

b1 = b2 =

1 (n − 1)2μ

i=1

j=1

(10)

(11)

j=1

and UCL =

|S| 1/2 (b1 + 3b2 ) b1

(12)

The T2 chart shows a clear outlier and one point close to the UCL. The chart for Generalized Variance shows two clear outliers, and a third outlier just above the UCL. The chart is shown in Fig. 4.

Fig. 2. T2-Generalized Variance chart.

The last multivariate method employed before settling on a preferred method was the Principal Component Analysis (PCA) [20]. The method is most often used to reduce the dimensionality of datasets by identifying a smaller number of uncorrelated variables from a large dataset but can also be used to identify outliers in non-Euclidian space. The test calculates a set of orthogonal eigenvectors of the covariance matrix of the covariance matrix of the variables. The result is a principal component matrix, where the first component accounts for the largest data variation and so on. The main goal is to explain the maximum amount of variance using the minimum number of components. The eigenvectors contain coefficients corresponding to each variable and are the weights for each variable to calculate principal component score. The scores are calculated by Z = YV, where Z is the principal component matrix, Y a raw data matrix (n *

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p) and V a matrix of eigenvectors. The proportion of sample variance explained by the kth principal component is calculated by λk λ1 + λ2 + ... + λp

(13)

The analysis showed that 96,3% of the variance of the original dataset could be represented by using two principal components. As part of the PCA analysis, the Mahalanobis Distance was employed to find unit distances to a center point P. This testing provided a good way to identify more of the confirmed customer returns. This includes easy checks of datasets, change of limits, change of variables and so on. Easy of automation is also a consideration that will be discussed in a later part. A final decision was made to focus solely on this method. The Mahalanobis Distance is a useful multivariate outlier detection method, in that it removes correlation in the dataset, which can be a challenge in high-dimensional datasets [21]. 3.1 Mahalanobis Distance The Mahalanobis Distance (MD) outlier detection method is unitless and scale invariant. It measures the distance of a point P from a distribution D in an n-dimensional nonEuclidean space [21]. The distance from a point to the distribution is calculated by

  n      T Xi − X n Vn−1 Xi − X n (14) MD = i=1

where Xi is the data value vector at row I, X the mean vector and Vn-1 the inverse of the covariance matrix [22]. The square of the MD is approximately chi-squared distributed with n degrees of freedom, where the degrees of freedom are equal to the number of variables [23], and therefore it is possible to find a suitable critical distance based on the confidence level and degrees of freedom by using a chi-squared table.

4 Results and Discussion This chapter presents the results from the MD analysis, and a subchapter describes how the Python automation was done. The chapter describes how the algorithm was placed in a virtual container with other tools inside the Continental network. The discussion includes perspectives on detection rate, requirements and limitations. Initial analysis using the MD was done by inserting 50 confirmed customer returned PCBs into a random dataset, using confidence levels of 95, 99 and 99,9. Setting the confidence level too low would result in a critical limit inside normal PCBs in addition to the outlying units. A sufficiently high confidence level would ensure that the critical limit avoids normal units, and still identify the outliers, as shown in Fig. 3. Experimenting with different sample sizes provided insight into how to better detect customer return units with a minimized amount of false alarms.

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Fig. 3. Mahalanobis Distance with limit examples.

The initial detected about 4% of the customer returned units. Experimenting with different critical limits, sample sizes and CTQ variables improved the detection rate to 10 and finally 20%. There is also an economic constraint in terms of selecting the critical limit. There is a cost to removing units from production and a benefit to removing the customer returns from production. The goal was to find a point that maximizes the units detected and minimizes the units removed from production. 4.1 Automation Using Python A decision was made to automate the MD algorithm using Python. The reasoning included Python being open source, the data extraction and formatting team mainly used Python, and it was the preferred tool for the Continental IT engineers working on the datalake. The data was extracted from the Continental datalake and formatted to fit the algorithm. The formatted data were provided in.csv format, and then transformed into a matrix object (df1) with N * M dimensions. The rows with any missing data were excluded. The degrees of freedom (DoF) are equal to the number of variables or the number of columns. The mean vector of is then calculated for every column. The algorithm calculates the covariance matrix and its inverse, and performs an identity test. Maintaining the matrix notation, the means vector is subtracted from the initial data matrix df1, and the result (df1*) is multiplied with the inverse covariance matrix. The intermediate result is stored in a temporary object (temp). Further, the square of the MD is calculated as the product of temp and the transposed matrix of df1*. The result is a diagonal matrix (MD_squared), where the elements on the diagonal are the squares of the MD for each row of the initial data matrix df1. These elements are extracted from MD_squared and appended to df1, which then becomes a matrix of N * (M + 1) dimensions. The Chi square critical value is calculated (critical_value), with a 0.9xx level of confidence and degrees of freedom DF. The outliers are

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identified as any data points above the set critical value. The Python code for the MD calculation is shown in Fig. 4.

Fig. 4. Python code calculating Mahalanobis Distance, MD2 and critical limit.

The Python code was tested both manually and using software. Another test included conducting a data pipeline test, including data extraction, formatting, MD algorithm activation and output of MD for all included units and a table of all units above the critical limit. After a successful test, a Docker container [24], which is a virtual environment that allows for applications, dependencies and related libraries to function in any environment, was placed inside the Continental network. The container included the MD algorithm, a Graphical User Interface (GUI) including a search engine and a reporting tool. The goal was that the quality team on-site and any other relevant personnel at Continental could use the algorithm autonomously. An overview of the method is shown in Fig. 5. In the final review meeting of the QU4LITY project, pilot management reported successful algorithm implementation in Sibiu, and a continuing detection rate of 20%. This implementation seems to support the viability of the method in an industrial Big Data environment. Using the MD also solves Big Data challenges like heterogeneity, statistical accuracy and efficiency. The confidence level will remain unchanged at the chosen level with dimensionality upscaling, and the method is able to analyze datasets up to a day’s production of units on-site in less than a second. The generality of the method allows for usage in other production lines at Continental and may also be interesting for other manufacturers and in other sectors for detecting customer returns or other specialized outliers in big datasets. Although these results provided a promising starting point that ultimately led to the implementation of the solution, there were several requirements observed to make that possible. Domain knowledge through process and process line knowledge is assumed to be highly important to be able to filter out non-pertinent variables. Domain knowledge also contributes towards identifying the CTQ variables for the use-case in question

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Fig. 5. Method architecture.

and discovering if there are any CTQ variables missing or not measured. Including non-pertinent variables or missing CTQ variables most likely will skew the analysis output. Cloud storage solutions, including the Continental datalake, contain large amounts of data that can be hard to navigate without any form of domain knowledge. Including domain knowledge through on-site expertise seems to be essential in providing insight into the production process from start to finish, variables importance and linking data structure with the physical production line and where in the process they stem from. Using available domain expertise can help with data filtering and using expertise to identify CTQ variables and suggest experiments to better detect outliers. Still, the domain expertise is probably not complete, and, there is still likely incomplete knowledge and insight related to the customer return outliers, leading to some variables being left out of the analysis or some that are perhaps not even measured. Increasing domain knowledge and a deeper knowledge on the CTQ variables in the analysis would likely improve the 20% detection rate significantly, and potentially lead to increased success in research related to condition monitoring or even prediction capability. The Mahalanobis Distance and other multivariate analysis methods are important and often more powerful tools when the univariate methods can’t produce significant results and the outliers result from combinations of variables, not unique variables. This importance can further be improved when basic automation is easy to implement. Some apparent negatives are increased demands on use-case domain knowledge, structure, data completeness, synchronicity, capture rate, sample sizes and CTQ variable knowledge to mention some.

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5 Conclusion The work in the Continental pilot, where they presented a challenge with undetected customer returns from a PCB production line, has resulted in an outlier detection method using the Mahalanobis Distance. The method can detect 20% of the customer returns as specific multivariate outliers. The method, combined with Python automation, a GUI, a search-engine and a reporting tool has to the implementation of the method at Continental factory in Sibiu, Romania. The general nature of the method also provides interesting possibilities in utilization on other process lines at Continental, in other companies and other industrial sectors. Although the assumption is that increased domain knowledge can help improve identification of CTQ variables, and increase the detection rate, the method has successfully been able to identify a significant percentage of the before undetected outliers and is therefore considered to be viable as a method. The automation using Python allows for big data analysis, with MD allowing for effective, simultaneous analysis of multiple variables and units. The inclusion of a domain expertise seems to have been of great importance in finding part of the CTQ variables and in aiding with continuous improvement of the method through rapid prototyping. 5.1 Future Work Continental has shown interest in continuing work with the method. This can open interesting possibilities in examining the importance of increased process knowledge and a higher percentage of CTQ variables, and how this can affect the detection rate and the false positives rate. The MD method described in this paper may not be feasible as a solution in many cases. The quality of the output is limited by data quality, especially concerning CTQ variables and the exclusion of non-pertinent variables. A high level of domain and process knowledge is also helpful to ensure the proper inputs. Application of requirements and the inclusion of domain expertise may also be interesting for future approaches involving AI and ML methods. Acknowledgements. This work has been done as part of the BRU21 program at the Norwegian University of Science and Technology, with the independent oil company OKEA as a sponsor. The contributions of Chiara Caccamo and Ragnhild Eleftheriadis, who were both coauthors in a conference paper that served as a foundation for this paper, are greatly appreciated and acknowledged.

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4. Wang, K.-S.: Towards zero-defect manufacturing (ZDM)—a data mining approach. Adv. Manuf. 1, 62–74 (2013). https://doi.org/10.1007/s40436-013-0010-9 5. Chen, H.-T.: Quality function deployment in failure recovery and prevention. Serv. Ind. J. 36(13–14), 615–637 (2016) 6. Psarommatis, F., May, G., Dreyfus, P.-A., Kiritsis, D.: Zero defect manufacturing: stateofthe-art review, shortcomings and future directions in research. Int. J. Prod. Res. 58(1), 1–17 (2020) 7. Caiazzo, B., Di Nardo, M., Murino, T., Petrillo, A., Piccirillo, G., Santini, S.: Towards zero defect manufacturing paradigm: A review of the state-of-the-art methods and open challenges. Comput. Ind. 134, 103548 (2022) 8. Tabernik, D., Šela, S., Skvarˇc, J., Skoˇcaj, D.: Segmentation-based deep-learning approach for surface-defect detection. J. Intell. Manuf. 31(3), 759–776 (2020). https://doi.org/10.1007/s10 845-019-01476-x 9. Okaro, I.A., Jayasinghe, S., Sutcliffe, C., Black, K., Paoletti, P., Green, P.L.: Automatic fault detection for laser powder-bed fusion using semi-supervised machine learning. Addit. Manuf. 27, 42–53 (2019) 10. Soualhi, A., Clerc, G., Razik, H.: Detection and diagnosis of faults in induction motor using an improved artificial ant clustering technique. IEEE Trans. Industr. Electron. 60(9), 4053–4062 (2012) 11. Peres, R.S., Barata, J., Leitao, P., Garcia, G.: Multistage quality control using machine learning in the automotive industry. IEEE Access 7, 79908–79916 (2019) 12. Wang, K., Gopaluni, R.B., Chen, J., Song, Z.: Deep learning of complex batch process data and its application on quality prediction. IEEE Trans. Industr. Inf. 16(12), 7233–7242 (2018) 13. Ranjan, J., et al.: Artificial intelligence-based hole quality prediction in micro-drilling using multiple sensors. Sensors 20(3), 885 (2020) 14. Powell, D., Magnanini, M.C., Colledani, M., Myklebust, O.: Advancing zero defect manufacturing: A state-of-the-art perspective and future research directions. Comput. Ind. 136, 103596 (2022) 15. Azamfirei, V., Psarommatis, F., Lagrosen, Y.: Application of automation for in-line quality inspection, a zero-defect manufacturing approach. J. Manuf. Syst. 67, 1–22 (2023) 16. Psarommatis, F., May, G.: A practical guide for implementing zero defect manufacturing in new or existing manufacturing systems. Procedia Comput. Sci. 217, 82–90 (2023) 17. Lilliefors, H.W.: On the Kolmogorov-Smirnov test for normality with mean and variance unknown. J. Am. Stat. Assoc. 62(318), 399–402 (1967) 18. Mukundam, K., Varma, D.R., Deshpande, G.R., Dahanukar, V., Roy, A.K.: I-MR control chart: A tool for judging the health of the current manufacturing process of an API and for setting the trial control limits in phase I of the process improvement. Org. Process Res. Dev. 17(8), 1002–1009 (2013) 19. Djauhari, M.A.: Improved monitoring of multivariate process variability. J. Qual. Technol. 37(1), 32–39 (2005) 20. Abdi, H., Williams, L.J.: Principal component analysis. Wiley Interdisc. Rev. Comput. Stat. 2(4), 433–459 (2010). https://doi.org/10.1007/978-3-030-03243-2_649-1.pdf 21. De Maesschalck, R., Jouan-Rimbaud, D., Massart, D.L.: The mahalanobis distance. Chemom. Intell. Lab. Syst. 50(1), 1–18 (2000) 22. Ben-Gal, I.: Outlier detection. In: Data Mining and Knowledge Discovery Handbook, pp. 131– 146. Springer, Cham (2005). https://doi.org/10.1007/0-387-25465-X_7 23. Gallego, G., Cuevas, C., Mohedano, R., Garcia, N.: On the Mahalanobis distance classification criterion for multidimensional normal distributions. IEEE Trans. Signal Process. 61(17), 4387–4396 (2013) 24. Potdar, A.M., Narayan, D., Kengond, S., Mulla, M.M.: Performance evaluation of docker container and virtual machine. Procedia Comput. Sci. 171, 14191428 (2020)

Sustainable Mass Customization in the Era of Industry 5.0

A Systematic Literature Review on the Developments in the Field of Flexible and Fully Automated Assembly Stations Within the Automotive Sector Bennet Schulz1,3(B)

, Katja Klingebiel2 , and Daniel Reh1

1 Volkswagen Commercial Vehicles, Mecklenheidestraße 74, Hanover, Germany

[email protected]

2 Faculty of Business Studies, Dortmund University of Applied Sciences and Arts,

Emil-Figge-Straße 44, Dortmund, Germany 3 Faculty of Mechanical Engineering, TU Dortmund University, Leonard-Euler-Straße 5,

Dortmund, Germany

Abstract. In today’s automotive assembly systems, automation is widely implemented to improve assembly time, costs, and quality. In accordance, the respective design of the fully automated assembly stations focuses on high performance and the maximum possible output, thus restricting the system’s flexibility. In consequence, inefficiencies manifest themselves in case the assembly system can neither properly adapt to sudden changes in the assembly system (e. g. the product mix, the product design, or the assembly process) nor satisfy demand changes from internal or external factors (e. g. market demands). This paper provides a systematic literature review on the flexibility of automated assembly systems laying a specific focus on design concepts and design variants, in order to fulfill today’s flexibility requirements. As a result, the three major streams for improving the flexibility of fully automated assembly stations have been identified as incorporating flexibility in assembly systems, flexible systems design, and automation potential. Applications of the findings indicate that in order to increase the flexibility of automatic assembly, the design of fully automated assembly stations should include a focus on operations-, and structural flexibility as well as the flexibility horizons. In consequence, by increasing the flexibility of automatic assembly systems, the efficiency would increase. Keywords: Flexibility · Automation · Automotive Assembly

1 Introduction The assembly as part of the automotive manufacturing process has a unique standing. It accounts for up to 50% of the total manufacturing time and causes up to 70% of the manufacturing costs [1]. Because of these characteristics, the assembly processes have to be designed with optimal efficiency, i.e. low assembly time, low assembly costs, and a high © IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 441–459, 2023. https://doi.org/10.1007/978-3-031-43688-8_31

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level of quality to ensure an economically successful manufacturing process [1, 2]. In today’s assembly, especially in automotive manufacturing, the majority of the assembly processes are still executed manually [3]. Although manual assembly is characterized as very flexible, it incorporates a lower productivity compared to an automated assembly based on fully automated assembly stations (FAAS) [1, 4], which are designed for high performance and maximum output [5, 6]. Furthermore, FAAS offer benefits over manual assembly stations regarding assembly costs and time [5]. In consequence, to achieve the best performance possible, today’s FAAS are designed as monolithic constructs that are configured for a specific use case (e.g. wheel assembly for vehicle A) [6]. However, with the current market situation, the efficiency demands for manufacturing companies are changing. Shortening product lifecycles, volatile markets, and mass customization are just a few challenges companies have to face [7–9]. To cope with these challenges, efficient assembly processes must offer more and more flexibility. The flexibility of an assembly can be defined as the capacity of the assembly to adjust to changes in the product to be assembled, the product mix, or the product sequence [10]. Consequences for the assembly arising from the flexibility requirements are for example the need to rearrange the assembly processes depending on the current product mix. Nevertheless, the rigid specialized FAAS can only react gradually to changes and thus cannot satisfy today’s flexibility requirements [6, 7]. This raises the question of how FAAS can be designed so that the current flexibility requirements are sufficiently met. Within the scientific literature, many authors discuss different methods on how to increase the flexibility of assembly systems and FAAS. These publications range from technological solutions to planning approaches. In this paper, this current research that especially focuses on the improvement of flexibility of the assembly system and more specifically the flexibility of FAAS is being identified and assessed. The focus is laid on assembly systems within the automotive industry because of their great potential arising from the currently still low degree of automation. This paper is structured as follows: After the Introduction, Sect. 2 Automotive Assembly Processes explains the basic characteristics of the automotive industry. Following, the Research Methodology for the literature review is stated in Sect. 3. Afterward, the results of the literature review are discussed in Sect. 4. Based on the discussion, the findings are introduced in Sect. 5. Section 6 of this paper concludes the literature review and gives an outlook on further research needs.

2 Automotive Assembly Processes In general, production can be described as “[…] the process […] of physically making a product from its material constituents […]” [11]. For automotive production, this process is divided into four different sections, beginning with the press shop and ending with the assembly (see Fig. 1). This makes the assembly the final process before the products get handed over to the customers.

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Body shop

Paint shop

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Assembly

Fig. 1. Automotive production process [12]

The assembly, as part of the automotive production process [13], is defined as the steps to assemble geometrically determined parts into a final product [3]. Exemplary subprocesses are the assembly of the trim or tightening of screws [14]. In general, assembly processes can be categorized into manual, hybrid, or automatic assembly with different characteristics regarding performance, flexibility, and product complexity [1, 15]. Manual assembly processes allow for production of complex products with a medium amount of output. In addition, they have the capability to handle a high product complexity which is based on the high flexibility of human abilities. The second category, automatic assembly, comprises fully automated assembly processes, which can produce a very high amount of complex products but lack flexibility due to the specific rigid design of FAAS [1, 15]. The last category, hybrid assembly processes, improves the performance of manual assembly by implementing automated processes alongside manual processes. Here, the performance increases whereas the flexibility worsens in comparison to the manual assembly processes (see Table 1). Table 1. Differentiation of assembly processes [1, 15] Assembly

Performance

Flexibility

Product Complexity

Manual

o

++

+

Automatic

++

--

++

Hybrid

+

+

o

+ + - very high + - high o – medium - - low -- - very low

The different assembly processes are implemented into various production types to form an assembly system. These production types are for example mass production, serial production, one-time production, or variant production [16, 17]. When considering the automotive industry, especially variant and serial production are relevant [8, 17]. Variant production is characterized by a medium number of variants of the product to produce, which requires flexibility of the assembly system. In contrast, serial production aims to produce a high amount of output with a low number of variants. Consequently, with the focus on a high amount of output, the need for flexibility decreases. [16, 17]. When implementing the assembly system for a serial variant production, different organization types can be realized. For the automotive industry, assembly lines are the most prominent [18]. Assembly lines describe a flow oriented system where several stations are aligned in a successive form [19]. The flow in an assembly line describes the linking of the materials and products in the progress of the assembly process. To realize this continuous flow, the individual stations on the assembly line have to have an equal workload [1]. To ensure this equal workload at every station, a time interval is defined. This cycle time acts as a reference for the number of tasks that can be allocated to

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every station [20, 21]. These task allocations are based on the product variants, number of products, or product mix integrated into the assembly system. If for example, the to be assembled products are changing, the structure of the assembly system needs to change as well in order to remain efficient [16, 22]. These changes can be due to newly developed products, integrating additional products into the assembly system, or shifting market demands, which can occur at various points in time. These points in time can be described by the periods of production planning: operational, tactical, and strategic [8, 23]. To summarize, due to the mainly manual processes, the automotive assembly system is very flexible but lacks the time and cost benefits of automatic assembly. With this design, the automotive assembly leaves room for improving the efficiency of the assembly system. This could be achieved by combination of the strengths of manual assembly (flexibility focus) and automatic assembly (performance and cost focus), e.g. by increasing the flexibility of the automatic assembly processes. Therefore, it is necessary to measure and evaluate the efficiency, as a means to determine the level of efficiency. Typically, manufacturing companies measure and evaluate assembly time, costs, and quality through defined performance indicators. Firstly, for the assembly time, an exemplary measure is the default lead time, which assesses the lead time of all necessary assembly steps [24]. Methods for defining lead times for assembly processes are for example the Methods-Time Measurement or the REFA-Time Study [24, 25]. Secondly, the assembly costs can be calculated by considering the machine hourly rates from technical equipment and the personnel costs per hour. With these components, the assembly costs per year or production unit can be evaluated [26, 27]. Lastly, the assembly quality, which primarily relates to the product quality and is for example measured by the scrap rate [28]. In contrast, for flexibility as an efficiency criterion, a quantitative evaluation method is yet to be described. However, some exemplary qualitative criteria for evaluating the flexibility of assembly systems have been defined by Schumacher et al. (2022): (I) Operations flexibility: The assembly system itself needs to be able to adjust to flexibility demands to maintain the current efficiency [29]. (II) Structural flexibility: The design of the FAAS should allow an automated station to change its structure to adjust to flexibility demands [29]. When conside ring the automotive assembly system, these general evaluation criteria (I) and (II) can be extended for the automotive assembly by integration of the periods of production planning [23]. (III) Flexibility horizon: The timeframe in which the flexibility is relevant as different timeframes (e.g. periods of production planning) require different reactions in order to satisfy the flexibility demand. In order to find a solution for increasing the efficiency of an assembly system by enhancing the flexibility of automatic assembly, the current scientific literature needs to be evaluated. Hence, a systematic literature review is conducted. This literature review investigates the topics of flexibility, assembly, and automation to further research the design of FAAS.

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3 Research Methodology To research the design of the FAAS, a systematic literature review has been conducted. Beforehand, several framework conditions had to be set. First, the research criteria need to be defined by outlining inclusions and exclusions for the criteria publication type, language, timeframe, and discipline. For the first criterion publication type, journal papers, conference papers, books, and book chapters have been chosen because in these scientific publications the latest research trends are publicized. Exclusions for the publication type have been set for standards, or advertisements because they either describe widely implemented processes or company-specific topics. For the language criterion, English and German have been included. The timeframe for the literature search has been chosen to include the last ten years of scientific literature. The last criterion, discipline, has been defined to restrict the publications to relevant fields of study. For this, the inclusions are for example automotive, engineering, or production. The exclusions are for example business, management, or leadership. The criteria with inclusions and exclusions are listed in Table 2. Table 2. Criteria for the structured literature review Criteria

Inclusion

Exclusion

Publication Type

Journal papers, conference papers, books, book chapters

Encyclopedia, standards, newsletter, advertisements

Language

English, German

Non-English/ -German

Timeframe

2013 - 2023

Before 2013

Disciplines

Automotive, engineering, production, assembly, computer science

Business, management, leadership, marketing, social science

To cover all relevant results, the renowned publishers Springer Professional, Science Direct, and IEEE Xplore have been chosen. The keywords flexibility, assembly, and automation have been selected as relevant to the search query. In order to rule out any wording differentiation by the authors, the keywords for the search query have been complemented by synonyms or related keywords. For every keyword, the phrases have been connected via the Boolean function “OR” so that the following query results: (Flexib* OR Reconfigurab* OR Rekonfigurierbar* OR Changeab* OR. Wandlung*) AND (production OR Produktion OR manufacturing OR Fertigung OR assembly OR Montage) AND (automat*). Further, the search has been restricted to the title and the abstract of the publication. The number of publications resulting from the query are shown in Table 3.

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Number of Publications

Springer Professional

3167

Science Direct

731

IEEE Xplore

74

To further sort out the publications relevant to this paper, a filter systematic has been applied. For the first filter, the field of study, the relevant disciplines have been selected to refine the search results. Further, the industry conditions have been evaluated to focus the search results on relevant industries only (e. g. manufacturing of industrial goods). The third and last filter has been applied to delete all duplicates within the search results. After applying the filters, the resulting publications have been further evaluated, by screening the abstracts for relevant publications. Thereafter, the full text of all remaining 58 publications has been reviewed. After reading the full text of these publications, 13 publications remained as relevant for the scope of this paper. For these, a forward and backward search (publications that cited the particular paper or which have been cited by the paper [30]) has been carried out. For the results of the forward and backward search, the research methodology has been conducted a second time (without the restriction of the timeframe). This added further 21 publications to the list of relevant publications, resulting in a total of 34 publications that matched the criteria. For a better analysis, the authors of the publications have been categorized into three major thematic areas derived from the analysis of the publications. First, the area of flexible manufacturing systems. Secondly, the area of flexible systems design, and lastly the area of automation potential. Furthermore, the three areas have been divided into thematic subareas which were inferred from the publications. Firstly, the area of flexible assembly systems has been divided into the subareas human-robot collaboration (HRC), flexible planning concepts, and reconfigurable manufacturing systems (RMS). Secondly, the subareas for the area of flexible systems design are modular systems design and changeable information technology (IT). The third area, automation potential, has been divided into process-wise and product-wise potential. The allocation of the 34 relevant publications to the different areas and subareas is shown in Table 4.

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Table 4. Authors per thematic area Thematic Area

Subareas

Authors

Flexible manufacturing systems

Human-robot collaboration

Lotter (2012b) Scholer (2018)

Flexible planning concepts

Garrel et al. (2014) Foith-Förster et al. (2015) Kern et al. (2015) Erlach (2020) Fries et al. (2020) Stark et al. (2020) Burggräf et al. (2021) Fries et al. (2021) Kern (2021)

Reconfigurable manufacturing systems

Koren et al. (2003) Katz (2007) Wiendahl et al. (2007) Koren et al. (2010) Azab et al. (2013) Marks et al. (2018) Prasad et al. (2019) Fechter et al. (2020) Koren (2020) Abadi et al. (2021)

Flexible systems design

Modular systems design

Klein (2014) Buck (2015) Ahmadzadeh et al. (2015) Hüttemann et al. (2019)

Changeable information technology

Andresen et al. (2005) Brusaferri et al. (2014) Fechter (2020)

Automation potential

Process-wise

Ross (2002) Deuse et al. (2014) (continued)

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Thematic Area

Subareas

Authors

Product-wise

Eskilander (2001) Boothroyd (2005) Hesse (2012) Madappilly (2021)

It can be concluded that the area of flexible manufacturing systems makes up the majority of the publications. These publications are going to be discussed in detail in the following Sect. 4 .

4 Discussion In order to identify possible solutions for increasing the efficiency of assembly systems. This analysis starts with the area of flexible manufacturing systems in Sect. 4.1. Afterwards, the area of flexible systems design is discussed in Sect. 4.2 followed by the area of automation potential in Sect. 4.3. 4.1 Flexible Manufacturing Systems To improve the flexibility of assembly systems and automated assembly stations, several authors are proposing solutions. A prominent concept to increase the flexibility of the assembly system is the HRC, where robots and humans work side by side to form a hybrid assembly system. This hybrid system aims to utilize the benefits of both manual and automated assembly, i.e. these systems benefit from the flexibility of the human factor and the performance of the automated assembly station, while still having to cope with the downsides of these types of assembly systems [31, 32]. Within the subarea of flexible planning concepts, Garrel et al.(2014) describe different kinds of flexibility and examples of how to implement flexibility into the manufacturing system, to get a better understanding of what flexibility means and how it is needed. Part of this publication is the differentiation between internal and external factors (e. g. significant output changes according to market needs [33]), that have an impact on the flexibility needs of the assembly system [34]. Furthermore, Kern (2015) proposes a solution to reduce the rigid characteristics of the assembly lines with the concept of the modular assembly system. This concept describes an assembly system where different assembly processes can be handled through modular stations (e. g. manual stations) in which these processes are executed. When implementing this model, only modules that are required for the current product are used. All other modules are not being used in this case. This model allows the product within the assembly system to follow its individual flow through the assembly. This increases flexibility because not every product has to follow the same flow on the assembly lines [35, 36].

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Further publications describe additional concepts for increased assembly flexibility. Foith- Förster et al. (2015), for example, propose a concept with a focus on the flexible planning of assembly processes within the assembly system. This planning concept proposes process modules to build a flexible assembly system [37]. Another concept to increase the flexibility of assembly systems was proposed by Fries et al. (2021) by introducing the fluid manufacturing system that enables the assembly system to ideally satisfy the market demand, even with today’s volatile markets [38]. The foundations for the fluid manufacturing system were described by Fries et al. (2020), where production planning and control and production plant planning are compared with a focus on customer-specific manufacturing systems [39]. In addition, Erlach (2020) tries to solve the rigid design of FAAS by implementing the changeability of a manufacturing system as part of the initial planning process. With this approach, changeability is part of the system and is easier to implement when needed [40]. Burggräf et al. (2021) describe a manual for the planning of production plants. One special focus is the modularization on all levels of the production plant from the whole plant down to assembly stations [41]. The RMS, proposed by Koren et al. (2003), introduces the concept of customized flexibility where a manufacturing station can process every product of a product family. A product family includes all products that share certain characteristics (e. g. geometry). Furthermore, the concept of the RMS requires that the manufacturing stations are designed in a way that they are able to be flexible within a predefined corridor (e. g. 10–100 parts/h). For the customized flexibility and the flexibility corridor, the authors describe six characteristics (scalability, convertibility, diagnosability, modularity, integrability, and customization) to accomplish the RMS. [42–47]. In extension to the RMS, Wiendahl et al.(2007) introduce the reconfigurable assembly system (RAS) where the RMS concept is transferred into an assembly setting. The RAS again uses HRC to maximize the flexibility of the assembly system [48]. To implement the reconfiguration of manufacturing systems, Abadi et al. (2021) propose a modular design methodology to facilitate the implementation. In this methodology, they are identifying modules for different products for which they then configure the manufacturing machine [49]. In addition, Azab et al. (2013) introduce a control loop for the reconfiguration of manufacturing systems, where an evaluation of the current flexibility level is conducted, followed by an examination of whether or not the current level is still sufficient under the current circumstances [50]. A different method for quantifying the changeability of manufacturing systems is proposed by Fechter et al. (2020), where evaluation criteria get identified which can be used to rate the changeability of production plants [51]. In summary, the literature regarding flexible manufacturing systems shows several publications trying to increase the flexibility of manufacturing systems via planning concepts or design guidelines for reconfigurable manufacturing systems. 4.2 Flexible Systems Design Within the area of flexible systems design, the technical implementation for increased flexibility is described. The question is how to design the hard- and software of automated systems to accomplish increased flexibility. To achieve a flexible systems design Buck (2015) describes a modularization of mechatronic components and their reusability. With these newly generated mechatronic

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modules, a toolbox can be created. In utilizing this toolbox, process oriented mechatronic systems can be achieved. The methodology for modularization applied by Buck (2015) is the method of software design pattern, where general, reusable solutions can be created [52]. Another approach was introduced by Klein (2014), who dealt with constructing modular production stations that are based on assembly processes. To identify specific modules, Klein (2014) analyses the design of existing production stations and applies the modularization method design structure matrix [53], which was introduced by Steward (1981) [54]. Further models were published by Hüttemann et al. (2019) where mobile robots perform different, simple assembly processes in changing locations [55]. The implementation of modular robotic systems is thoroughly analyzed by Ahmadzadeh et al. (2015). The authors describe various design options for modular robotic systems to satisfy different flexibility needs. Their publication comprises methods to implement different aspects of modularity (e. g. self-reconfiguration or self-assembly) [56]. For changeable IT, Fechter (2020) describes intuitive programming and a robot periphery for HRC, which should help to implement the changeability of robots within the assembly system [57]. Furthermore, Andresen et al. (2005) introduce a model to evaluate the need for change in the IT system of companies. In addition to this, they define factors that describe the changeability of IT systems. Through these, they are able to react to external change factors, because the IT system is analyzed and prepared for change [58]. An additional IT strategy is proposed by Brusaferri et al. (2014). They design an IT architecture method to control and verify reconfigurable manufacturing systems, based on cyber physical systems [59]. Concluding, in the area of flexible systems design, modularity and flexibility are described for industrial robots and IT systems, which assist in utilizing the implementation of modular systems. 4.3 Automation Potential To automate assembly processes in the assembly system, processes with the potential for automation need to be identified. This identification is either done by a process-wise evaluation or a product-wise evaluation. The process-wise automation potential can be described by the characteristics of the specific assembly process that is supposed to be automated (e. g. joining process direction). Depending on those characteristics an assembly process is more or less able to be automated. To determine whether or not an assembly process is automatable, Ross (2002) proposes a quantitative method to evaluate the automation potential. This method evaluates process characteristics, for example, the joining direction. For the final evaluation of the automation potential, an indicator is determined which then can be compared to a threshold indicator to conclude whether or not an automation potential exists [60]. An alternative method is described by Deuse et al. (2014) where a set of factors is added to the human motion sequences (see the concept of methods time measurement [61]). With this, basic human motion sequences can be defined for automated assembly processes [62]. Regarding the product-wise automation potential, the product’s design is evaluated. For this, Hesse (2012) describes guidelines concerning the product’s automatability and ability to be assembled [63]. Furthermore, Bolz (1985) and Boothroyd (2005) define design guidelines, that assist in enabling a product to be assembled automatically [64,

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65]. Eskilanders (2001) proposes further design guidelines and a systematic method for a qualitative evaluation of the product’s ability to be assembled automatically [66]. Madappilly et al. (2021) specify the methodology proposed by Eskilander (2001) to tailor the need of a particular industry, where for example a high level of customization is necessary [67]. Summarizing, with the process- and product-wise automation potential, assembly processes or product parts can be evaluated regarding their automatability. This is the foundation to increase the degree of automation in the assembly systems. In conclusion, the three areas of flexible manufacturing systems, flexible systems design, and automation potential are all widely publicized and relevant in today’s scientific world. The following Sect. 5 evaluates these publications to work out future research needs.

5 Findings A first finding from the discussion of current research trends regarding the flexibility of FAAS is that different flexibility terms are used to describe the adjustment of assembly systems. These different terms are flexibility, changeability, and reconfigurability. The first type, flexibility, is defined by Kern (2021) and Wiendahl et al. (2007) as quick and easy adjustment of the assembly characteristics within certain boundaries (without changing its configuration). According to this definition, a FAAS is able to operate within a predefined flexibility-corridor, here, the characteristics of the assembly system (such as output) can differ to a certain extent [35, 48]. This means for example that within a flexibility-corridor, the attachment of one particular clip can be executed on multiple products or in different cycle times. If the assembly characteristics need to be changed beyond the current flexibilitycorridor, the next adjustment type, changeability, becomes relevant. The changeability implies, according to Wiendahl et al. (2010), Kern (2021), Foith-Förster et al. (2015), and Fechter et al. (2020) a change in the assembly characteristics that extends beyond the defined flexibility-corridors (e. g. attaching a different clip or multiple clips). Changing the flexibility-corridors improves the general ability to react to different demands (e. g. from the market) [15, 35, 37, 51]. The ability of a FAAS to change flexibility-corridors is only possible to a certain degree, i.e. can be described in form of a change-corridor. To even further adjust a FAAS to external or internal demands, reconfigurability needs to be an attribute of FAAS. The reconfiguration is defined by Wiendahl et al. (2010), Koren et al. (2010), and Wiendahl et al. (2007), as the response (cost-effective, quick, and efficient) of the assembly system to market changes, i.e. the assembly characteristics can be changed when needed (with changing its configuration) [15, 43, 48]. With the ability to reconfigure, an automated assembly station would be able to alter its changecorridors. This can occur, for example, when a new product gets introduced into the assembly system and the FAAS needs to switch its assembly process from attaching clips to setting plugs. For the further analysis of the identified literature, the three criteria for increasing the flexibility of fully automated assembly have been chosen to be applied as additional criteria groups. These groups were defined as operational flexibility factors (group (I)), structural flexibility factors (group (II)), and flexibility horizon (group (III)). To ensure a

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thorough analysis, the criteria groups (I)-(III) need to be further refined. The characteristics from the RMS and RAS [42–48] could be applied here, so that the reconfigurability as an adjustment type gets included in the evaluation. For the first and second criteria group, the six characteristics for implementing RMS function as the initial evaluation criteria. The characteristics of diagnosability, convertibility, and scalability have been defined by Koren (2020) as operational characteristics [44]. Therefore these three criteria have been assigned to group (I). The three remaining RMS criteria customization, integrability, and modularity have been described as structural characteristics by Koren (2020). Hence, these are assigned to the group (II) [44]. In addition to the characteristics defined by Koren (2020), mobility [48] and reusability [54] will be added to the criteria of group (II). Group (III) can be defined by the periods of production planning [23, 68]. The results of the criteria definition are shown in Table 5. Table 5. Literature evaluation criteria Criteria Groups

Criteria

(I) Operations flexibility factors

Diagnosability Convertibility Scalability

(II) Structural flexibility factors

Customization Integrability Modularity Mobility Reusability

(III) Flexibility horizon

Operational Tactical Strategic

Using these 11 criteria, the results of the literature review can be evaluated to point out which criteria are mentioned by which publication. This allocation is shown in Table 6 by using an “x” for mentioning the criterion and by using a “-” otherwise. Hence, many authors focus their scientific publications on flexible manufacturing systems in order to enable manufacturing systems to cope with current flexibility demands. These concepts for flexible manufacturing systems lack certain aspects (like for example the integration flexibility horizons), that are necessary to increase the efficiency of assembly systems. Another largely publicized area deals with flexible systems design. Within this area, the automation technology in soft- and hardware is designed in order to increase the structural flexibility of the automated stations. Gaps within the publications regarding flexible systems design comprise the system’s design in combination with flexibility horizons.

x

−

x

Kern et al. (2015)

x −

x

−

−

x

−

Ahmadzadeh et al. (2015)

−

x

Foith-Förster et al. (2015)

−

−

Klein (2014)

Buck (2015)

−

−

−

−

Deuse et al. (2014)

−

−

x

−

x

−

−

−

x

x

Brusaferri et al. (2014)

−

x

-

-

−

x

−

x

x

x

x

x

x

x

−

-

-

Azab et al. (2013)

x −

x

x

Lotter (2012b)

−

x

−

x

x

Hesse (2012)

Wiendahl et al. (2007)

Koren et al. (2010)

x

−

Katz (2007)

−

x

−

x

Boothroyd (2005)

x

−

Andresen et al. (2005)

−

−

x

x

Koren et al. (2003)

x

x

Ross (2002)

−

−

x

x

Customization

x

x

−

−

x

x

x

x

x

−

x

x

x

−

x

x

−

x

Integrability

Structural flexibility factors Scalability

Convertibility

Operations flexibility factors

Diagnosability

Eskilander (2001)

Authors

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

−

x

Modularity

Table 6. Authors per evaluation criteria

−

-

−

x

−

x

−

x

−

x

−

−

x

x

x

x

x

−

x

x

− −

−

−

−

x

−

−

x

x

x

−

−

−

Reusability

−

−

Mobility

x

x

−

−

−

−

−

x

-

−

−

x

−

−

x

x

x

x

Operational

−

−

−

−

−

−

−

−

-

−

−

x

−

−

−

−

−

−

Tactical

Flexibility horizon Strategic

(continued)

−

−

−

−

−

−

−

x

-

-

−

−

−

−

−

−

−

x

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x

−

x

−

−

−

−

x

−

Kern (2021)

Madappilly (2021)

x

x

−

−

−

−

x

x

x

x

x

x

−

x

x

x - authors mention evaluation criterion - - authors do not mention the evaluation criterion

x

x

−

x

Fries et al. (2021)

Stark et al. (2020)

−

−

−

Koren (2020)

x

−

x

x

Fries et al. (2020)

x

x

−

−

−

Fechter et al. (2020)

−

−

−

Fechter (2020)

x x

Burggräf (2021)

−

−

Erlach (2020)

x

x

x

Abadi et al. (2021)

x

−

x

−

Prasad et al. (2019)

x

−

x

−

Hüttemann et al. (2019)

x

Scholer (2018)

x

−

x

x

x

x

x

x

x

−

x

−

x

x

x

x

Integrability

Customization

Scalability

Structural flexibility factors

Convertibility

Operations flexibility factors

Diagnosability

Marks et al. (2018)

Authors

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

Modularity

Table 6. (continued)

−

−

−

−

x

x

−

x

x

−

x

−

−

−

x

−

x

x

−

x

x

−

−

x

x

−

x

−

x

−

−

−

x

−

−

x

−

x

−

x

−

−

−

−

−

−

−

x

−

−

x

−

x −

Tactical −

Operational −

−

Flexibility horizon Reusability

x

x

x

Mobility

Strategic

−

−

−

x

−

−

−

x

−

−

−

x

−

−

−

454 B. Schulz et al.

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Lastly, the area of automation potential is covered. This area defines design guidelines so that product parts can be assembled automatically through automated assembly stations. The literature analysis showed that this area regards most of the evaluation criteria in some form but lacks a clear focus on the flexibility horizons and selected flexibility factors such as mobility. In conclusion, many of the operations and structural flexibility factors have already been described by current literature for the areas of flexible manufacturing systems, flexible systems design, and automation potential. But all these scientific publications lack the transfer of these criteria to the flexibility horizons.

6 Conclusion and Outlook Today’s automotive assembly systems are usually organized as assembly lines and characterized by mostly manual assembly processes with a low degree of automation. The few fully automated assembly processes are specialized assembly stations with a focus on performance. These FAAS are rigid and cannot cope with internal or external demands for change. This restrains the assembly system to function with optimal efficiency. To optimize the efficiency of automotive assembly systems, enhancing the flexibility of FAAS could prove to be advantageous. To explore this topic, a systematic literature review has been conducted, resulting in 34 relevant publications covering the areas of flexible manufacturing systems, flexible systems design, and automation potential. These identified publications were evaluated according to the criteria groups operations flexibility, structural flexibility, and flexibility horizon which try to assess the flexibility of assembly systems qualitatively. The results of the literature evaluation show that many of the evaluation criteria are described by current literature, only the transfer of the operations- and structural flexibility criteria to the flexibility horizon is absent. This transfer should be the topic of future studies, where design guidelines for the different flexibility factors of FAAS should be defined with a focus on the flexibility horizon. By doing so, the automatic assembly stations could be enabled to react to sudden changes and flexibility demands. This would result in an increased efficiency of the assembly system. Acknowledgments. The results, opinions, and conclusions expressed in this paper are not necessarily those of Volkswagen Aktiengesellschaft.

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40. Erlach, K.: Die schlanke und wandlungsfähige Fabrik. In: Erlach, K. (ed.) Wertstromdesign: Der Weg zur schlanken Fabrik, pp. 291–328. Springer Berlin Heidelberg, Berlin, Heidelberg (2020). https://doi.org/10.1007/978-3-662-58907-6_4 41. Burggräf, P., Schuh, G., (eds) Fabrikplanung: Handbuch Produktion und Management 4, 2nd ed. 2021. Springer Berlin Heidelberg; Springer Vieweg, Berlin, Heidelberg (2021). https:// doi.org/10.1007/978-3-662-61969-8 42. Koren, Y., Heisel, U., Jovane, F., et al.: Reconfigurable Manufacturing Systems. In: Dashchenko, A.I. (ed.) Manufacturing Technologies for Machines of the Future, pp. 627– 665. Springer, Berlin Heidelberg, Berlin, Heidelberg (2003). https://doi.org/10.1007/978-3642-55776-7_19 43. Koren, Y., Shpitalni, M.: Design of reconfigurable manufacturing systems. J. Manuf. Syst. 29, 130–141 (2010). https://doi.org/10.1016/j.jmsy.2011.01.001 44. Koren, Y.: The Emergence of Reconfigurable Manufacturing Systems (RMSs). In: Benyoucef, L. (ed.) Reconfigurable Manufacturing Systems: From Design to Implementation. SSAM, pp. 1–9. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-28782-5_1 45. Prasad, D., Jayswal, S.C.: A review on flexibility and reconfigurability in manufacturing system. In: Chattopadhyay, J., Singh, R., Prakash, O., (eds) Innovation in Materials Science and Engineering. Springer Singapore, Singapore, pp 187–200 (2019). https://doi.org/10.1007/ 978-981-13-2944-9_19 46. Marks, P., Yu, Q., Weyrich, M.: Survey on flexibility and changeability indicators of automated manufacturing systems. In: 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA). IEEE, pp 516–523 (2018) 47. Katz, R.: Design principles of reconfigurable machines. Int. J. Adv. Manuf. Technol. 34, 430–439 (2007). https://doi.org/10.1007/s00170-006-0615-2 48. Wiendahl, H.-P., ElMaraghy, H.A., Nyhuis, P., et al.: Changeable manufacturing - classification, design and operation. CIRP Ann. 56, 783–809 (2007). https://doi.org/10.1016/j.cirp. 2007.10.003 49. Abadi, C., Manssouri, I., Abadi, A.: Reconfiguration of Flexible Manufacturing Systems Considering Product Morpho-Dimensional Characteristics and Modular Design, pp. 559–565. https://doi.org/10.1007/978-3-030-62199-5_49 50. Azab, A., ElMaraghy, H., Nyhuis, P., et al.: Mechanics of change: A framework to reconfigure manufacturing systems. CIRP J. Manuf. Sci. Technol. 6, 110–119 (2013). https://doi.org/10. 1016/j.cirpj.2012.12.002 51. Fechter, M., Dietz, T.: Bewertung der Wandlungsfähigkeit eines Produktionssystems. In: Bauernhansl, T., Fechter, M., Dietz, T. (eds.) Entwicklung, Aufbau und Demonstration einer wandlungsfähigen (Fahrzeug-) Forschungsproduktion, pp. 11–17. Springer, Berlin Heidelberg, Berlin, Heidelberg (2020). https://doi.org/10.1007/978-3-662-60491-5_3 52. Buck, R.: Entwurfsmuster für den Aufbau von Baukästen für das Funktionale Engineering: Design patterns for the creation of libraries for functional engineering. Stuttgarter Beiträge zur Produktionsforschung, Band 41. Fraunhofer Verlag, Stuttgart (2015) 53. Steward, D.V.: Systems analysis and management: Structure, strategy, and design. PBI, New York (1981) 54. Klein, P.W.: Methode zum Engineering von Produktionsanlagen durch Wiederverwendung von Modulen. Universität Siegen, Institut für Produktionstechnik (2014) 55. Hüttemann, G., Buckhorst, A.F., Schmitt, R.H.: Modelling and assessing line-less mobile assembly systems. Procedia CIRP 81, 724–729 (2019). https://doi.org/10.1016/j.procir.2019. 03.184 56. Ahmadzadeh, H., Masehian, E.: Modular robotic systems: Methods and algorithms for abstraction, planning, control, and synchronization. Artif. Intell. 223, 27–64 (2015). https:// doi.org/10.1016/j.artint.2015.02.004

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57. Fechter, M.: Wandlungsfähige Roboter für die Automobilproduktion. In: Bauernhansl, T., Fechter, M., Dietz, T. (eds.) Entwicklung, Aufbau und Demonstration einer wandlungsfähigen (Fahrzeug-) Forschungsproduktion. A, pp. 55–68. Springer, Heidelberg (2020). https://doi. org/10.1007/978-3-662-60491-5_6 58. Andresen, K., Gronau, N., Schmid, S.: Ableitung von IT-Strategien durch Bestimmung der notwendigen Wandlungsfähigkeit von Informationssystemarchitekturen. In: Ferstl, O.K., Sinz, E.J., Eckert, S., et al. (eds.) Wirtschaftsinformatik 2005, pp. 63–82. Physica-Verlag HD, Heidelberg (2005). https://doi.org/10.1007/3-7908-1624-8_4 59. Brusaferri, A., Ballarino, A., Cavadini, F.A., et al.: CPS-based hierarchical and self-similar automation architecture for the control and verification of reconfigurable manufacturing systems. In: Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA). IEEE, pp. 1–8 (2014) 60. Ross, P.: Bestimmung des wirtschaftlichen Automatisierungsgrades von Montageprozessen in der frühen Phase der Montageplanung. ForschungsberichteIWB : Berichte aus dem Institut für Werkzeugmaschinen und Betriebswissenschaften der Technischen Universität München, Bd. 170. Utz Verlag, München (2002) 61. Böge, A., Böge, W.: Handbuch Maschinenbau. Springer Fachmedien Wiesbaden, Wiesbaden (2014). https://doi.org/10.1007/978-3-658-06598-0 62. Deuse, J., Roßmann, J., Kuhlenkötter, B., et al.: A methodology for the planning and implementation of service robotics in industrial work processes. Procedia CIRP 23, 41–46 (2014). https://doi.org/10.1016/j.procir.2014.10.066 63. Hesse, S.: Montagegerechte Produktgestaltung. In: Lotter, B., Wiendahl, H.-P. (eds.) Montage in der industriellen Produktion, pp. 9–48. Springer, Berlin Heidelberg, Berlin, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29061-9_2 64. Bolz, R.W.: Design for automated assembly. In: Bolz, R.W. (ed.) Manufacturing Automation Management, pp. 171–178. Springer, US, Boston, MA (1985). https://doi.org/10.1007/9781-4613-2541-3_37 65. Boothroyd, G.: Assembly automation and product design, 2nd ed. Manufacturing Engineering and Materials Processing, vol 66. Taylor & Francis, Boca Raton (2005) 66. Eskilander, S.: Design for Automatic Assembly - A Method for Product Design: DFA2. Dissertation, Royal Institute of Technology (2001) 67. Madappilly, P.J., Mork, O.J.: Review and modification of DFA2 methodology to support design for automatic assembly (DFAA) in the maritime industry. Procedia CIRP 100, 744–749 (2021). https://doi.org/10.1016/j.procir.2021.05.050 68. Krog, E.H., Richartz, G., Kanschat, R., et al.: Kooperatives bedarfs-und kapazitätsmanagement der automobilhersteller und systemlieferanten. Logistik Manag. 3, 45–51 (2002)

Mixed Integer Programming for Integrated Flexible Job-Shop and Operator Scheduling in Flexible Manufacturing Systems Reza Ghorbani Saber1,2(B) , Pieter Leyman1,2 , and El-Houssaine Aghezzaf1,2 1

Department of Industrial Engineering and Product Design, Ghent University, Technologiepark Zwijnaarde 46, 9052 Zwijnaarde, Belgium 2 Industrial Systems Engineering, Flanders Make@UGent, Sint-Martens-Latemlaan 2B, 8500 Kortrijk, Belgium {Reza.GhorbaniSaber,Pieter.Leyman,ElHoussaine.Aghezzaf}@ugent.be Abstract. In this paper, we propose a mixed-integer programming model for an integrated flexible job shop and operators shift-based scheduling in a Flexible Manufacturing System (FMS). The objective is to schedule a given set of jobs while taking operators schedule into account. Operators are qualified to operate a set of machines, and thus are only able to execute operations scheduled on these machines during their active shifts. An operation can be executed on a qualified machine in presence of a qualified operator. Furthermore, we tested the proposed MIP model on a set of generated instances to evaluate its performances. The MIP model provides optimal solutions for small scale instances. For larger instance the computational time and solutions gaps are still quite high. We are currently investigating how to reduce the computation time and improve the gaps for industrial size problems. Keywords: Scheduling · Flexible Manufacturing Systems · Dual Resource-Constrained Scheduling · Mixed Integer Programming

1

Introduction

A flexible manufacturing system (FMS) is a production system that is designed to allow some level of flexibility that enables it to cope with variations in type and quantity of the products being manufactured. These flexible manufacturing systems exploit flexible automation, computer controlled machines and high levels of technological integration to produce a large variety of products in different quantities. FMS is classified as a type of Computer-Integrated Manufacturing (CIM) system. CIM systems are categorized into three different types, namely, process-oriented systems, product-oriented systems, and Flexible Manufacturing System. FMSs are capable of producing varied parts without the need for manual setups and change-overs. This makes it possible to produce multiple types of products within the same facility without increasing labor [1]. c IFIP International Federation for Information Processing 2023  Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 460–470, 2023. https://doi.org/10.1007/978-3-031-43688-8_32

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The implementation of a FMS is becoming increasingly more widespread, as the demand for customized products at costs that are comparable to those of mass production continue to grow. Implementing a flexible manufacturing system entails various majors decisions. These decisions include upgrading the factory floor with advanced technology and equipment that can handle various product types and sizes, such as automated machinery and robotic arms designed to quickly reconfigure themselves to handle different jobs. In addition, the system must be integrated with adapted planning, scheduling and execution software come close to the effectiveness and efficiency of mass production systems. Effective planning and scheduling tools form the key to achieving high performance in a flexible manufacturing system. By increasing the shop-floor integration with the planning/scheduling software, unexpected changes can be managed quickly and intelligently. In an FMS, the scheduling problem in particular must take into account the different product paths through the machines, the multifunctional machines and the skills of the operators. It is indeed owing to an effective planning and scheduling that the production manager can ensure that the shop-floor is used is a cost-efficient manner. In the following paragraphs, the proposed scheduling problem, that may be applicable for a FMS, is explained. A typical machine scheduling problem consists a set of jobs, each consisting in turn of a set of operations, which must be processed on a set of qualified machines. Among all different types of machine scheduling problems, the job shop scheduling problem (JSSP) is one of the most practical cases. In a job shop scheduling problem there are predefined precedence relations between the operations of a job, but in general there are no predefined precedence relations between operations of different jobs on a machine. In addition, a machine is capable to handle only one operation at a time, and an operation can be processed on one machine at a time. Usually, the goal is to find a schedule of the jobs’ operations on the qualified machines in order to optimize some predefined objective function, such as minimizing makespan or total tardiness of jobs. The JSSP is shown to be NP-hard and is one of the most intractable problems in combinatorial optimization [2]. A large number of solution methods have been developed to solve it, which can be classified as either exact/approximate or heuristics methods (see [3–9]). The classical job-shop JSSP must be enriched to cope with the additional complexity of the flexible manufacturing systems, which might have additional requirements, such as multi-functional machines, pools of machines for certain types of operations, human resource skills and their availability. The flexible job shop scheduling problem (FJSSP) is a generalized form of the JSSP, which considers multiple machines for an operation along with multi-functional machines which are able to process more than one type of operation. These requirements add more flexibility in the decision variables of the problem, and as a result it would be even “more complex” than the JSSP. In the FJSSP an assignment of operations to machines would reduce the problem to the JSSP. For further reading on the FJSSP, see [10–14]. In this research paper, we consider an extended version of the FJSSP that appears in FMS, and considers more assumptions and constraints than the

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FJSSP. The main assumptions which make our problem different from the FJSSP are as follows: • There are a set of operators; each operator has a given skill that limits them to work with certain types of machines or in certain types of assembly stations • There is a shift-based system for operators timetabling, which imposes operators’ timetabling constraints This extended problem, which considers both machines and operators as the resources for the scheduling, is addressed as the Dual Resource-Constrained Flexible Job Shop Scheduling Problem (DRCFJSSP), which is NP-hard due to the fact that the JSSP, the reduced version of the problem, is known to be NP-hard [2]. In [15], the authors develop a variable neighborhood search metaheuristic with a new encoding for the solution. In [16], the authors have taken operator fatigue as an objective function to be minimized and solve the problem through a bi-objective optimization approach using NSGA-II. In another study, Yazdani et al. propose two meta-heuristics based on Simulated Annealing (SA) and Vibrating Damping Optimization (VDO) for the problem [17]. In [18], the authors consider the problem with makespan and due dates related criteria and developed an iterated greedy algorithm to solve the problem. Finally, a short review on DRCFJSSP is provided in [19]. In all of the mentioned studies, the assumption is that only one operator needs to be assigned to an operation during its processing time, and the possibility that the processing of an operation taking more than one shift is ignored. Thus, based on the shift-based timetabling for operators, we have to think of a way to assign eligible operators in different shifts involved in the processing time of such operations. However, in [20] the authors mentioned that in a shift-based system they assume that there are operators with exactly the same performance which can replace each other, but this assumption could be insufficient in many real-world applications in flexible manufacturing systems, since typically there are a limited number of operators capable of working with the same machine. In this study, we not only consider machines and operators as resources but also consider the assignment of operators to operations, which could take more than one shift. The remainder of this paper is structured as follows: in Sect. 2, a complete problem description and the MIP model are presented. In Sect. 3, an illustrative example is presented. In Sect. 4, the computational results on some larger instances are presented, and finally in Sect. 5, some conclusions and future research avenues are discussed.

2

Problem Description and Mathematical Modeling

We consider a manufacturing environment, which consists of a set of jobs J and a set of machines M , grouped in cells. Each job j ∈ J consists of a set of

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operations which we denote by θj , and in general the set of all operations is denoted by θ = ∪j∈J θj . For each operation i ∈ θ, the set of eligible machines for its processing is denoted by Mi , and the set of its predecessors is denoted by Γi . The other parameters of the model are presented defined in Table 1 below. Model Parameters:

Table 1. Sets and parameters of the model Set/Parameter Definition J

Set of jobs with index j

M

Set of machines with index m

H

Set of available shifts with index t

δt

The duration of a shift t ∈ H

Tmax

The duration of the scheduling horizon; Tmax =

θ

Set of all operations with index i

θj

Set of operations of a job j ∈ J;

W

Set of operators with index w

Wm

Set of operators who are capable of working with machine m ∈ M

Wi

Set of operators who are capable of processing operation i ∈ θ

Γi

Set of predecessors of an operation i ∈ θ

Mi

Set of machines that are eligible to process an operation i ∈ θ

Pim

Processing time of an operation i ∈ θ on machine m ∈ M



j∈J

 t∈H

δt

θj = θ

Model Decision Variables: The decision variables of the model are defined as follows: Cmax : Continuous variable indicating the makespan of the schedule Si : Continuous variable indicating the start time of an operation i ∈ θ Ximt : Binary variable indicating if operation i ∈ θ is assigned to machine m ∈ M and starts at shift t ∈ H Ywmt : Binary variable indicating if operator w ∈ W is assigned to machine m ∈ M at shift t ∈ H Zikm : Binary variable indicating if operation i ∈ θ precedes operation k ∈ θ when both of them are assigned to machine m ∈ M

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Model Objective Function and Constraints: The mathematical model is presented as follows. Cmax

Min

(1)

s.t.  

Cmax ≥ Si +

Pim Ximt

∀j ∈ J, ∀i ∈ θj

(2)

∀i ∈ θ

(3)

δ h ≤ Si

∀i ∈ θ, ∀m ∈ Mi , ∀t ∈ H

(4)

δh + Tmax (1 − Ximt )

∀i ∈ θ, ∀m ∈ Mi , ∀t ∈ H

(5)

∀k ∈ θ, ∀i ∈ Γk

(6)

∀i, k ∈ θ, i > k, ∀m ∈ M

(7)

∀i, k ∈ θ, i > k, ∀m ∈ M

(8)

∀w ∈ W, ∀t ∈ H

(9)

t∈H m∈Mi

 

Ximt = 1

t∈H m∈Mi

Ximt

t−1  h=1

Si ≤

t−1  h=1

Sk ≥ Si +

 

Pim Ximt

t∈H m∈Mi

Sk ≥ Si + Pim − Tmax (3 − Zikm   − Ximt − Xkmt ) t∈H

t∈H

Si ≥ Sk + Pkm − Tmax (2 + Zikm   − Ximt − Xkmt ) 

t∈H

t∈H

Ywmt ≤ 1

m∈M





Ywmt +

m∈M

Ywmt+2 +

m∈M



Ywmt+1 ≤ 1 ∀w ∈ W, ∀t ∈ {1, .., |H| − 2}

m∈M

(10) Tmax



Ywmh ≥ Si + Pim −

w∈Wm ∩Wi

− Tmax (1 − Ximt ) Si ≥ 0 Tj ≥ 0 Ximt ∈ {0, 1}

h−1 

δt

t =1

∀i ∈ θ, ∀t, h ∈ H, h ≥ t, ∀m ∈ M (11) ∀i ∈ θ ∀j ∈ J ∀i ∈ θ, ∀m ∈ Mi , ∀t ∈ H

(12) (13) (14)

Ywmt ∈ {0, 1}

∀w ∈ W, ∀m ∈ M, ∀t ∈ H (15)

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Zikm ∈ {0, 1}

465

∀i ∈ θ, ∀k ∈ θ, ∀m ∈ M (16)

In the proposed model, objective function (1) minimizes the makespan of the schedule. Constraints (2) show the relationship between the makespan and the completion time of all jobs. Note that the completion time of a job is greater than the completion time of all its operations, which is indicated in the right hand side of this equation. Constraints (3) shows that the processing of an operation has to be done on an eligible machine and also be started at a shift. Constraints (4) and (5) restrict the start time of an operation to be in the shift that the operation is assigned to. Constraints (6) guarantee that an operation can be started once all of its predecessors are completely done. Constraints (7) and (8) are sequencing constraints that ensure a machine would not one operation  at  process  more than  in X − X a time. In these two constraints the part 2 − imt kmt t∈H t∈H the right hand side indicates that if the operation is assigned to the corresponding machine, then the equations should be active. Note also that the part (1 − Zikm ) in Eq. (7) and Zikm in Eq. (8), make one of the equations active if operation i precedes operations k or vice versa. Constraints (9) ensure that each operator at a shift can be assigned to at most one machine. Constraints (10) are the availability constraint, which indicate that if an operator is assigned to a shift, then that operator should not be assigned to the two following shifts. Constraints (11) are the most important contribution of this model, which ensure that during the processing of an operation, in all the shifts that the operation is involved in, there should always be eligible operators who are assigned to the machine which is processing the operation. In this equationthe term Si + Pim is the completion time of operation i on machine h−1 m and t =1 δt is the start time of shift h, so it is clear that the subtraction of these two terms can be positive or negative. In case of a positive result, the model is forced to assign a operator to machine m at shift h. Finally, constraints (12)–(16) indicate the type of variables. As an illustration for constraints (11), we refer to Fig. 1. In this Figure, operation 1 starts during shift 1 on machine 1, and the completion time of this operation is equal to 23. In this case, the mentioned expressions for shift 1, 2 and 3 are 23 − 0 = 23, 23 − 8 = 15 and 23 − 16 = 7, respectively. As a result the model will assign a operator to machine 1 during these shifts. However for shift 4, the expression is 23 − 24 = −1, which is a negative value and hence the model is not forced to assign a operator to this machine during shift 4.

Fig. 1. Constraints (11) illustration

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An Illustrative Example

In this section we present the solution generated by the above MIP model for a small test instance consisting of 5 jobs, where each jobs itself consists of 5 operations, listed in the order of processing. The shop-floor consists of 5 machines, which are operated by 6 operators during 12 shifts, each with a length of 8 h. Table 2 shows the indices of jobs and those of the operations per job. Table 2. Job information for the small instance Jobs Operations 1

1

2

3

4

2

6

7

8

9 10

5

3

11 12 13 14 15

4

16 17 18 19 20

5

21 22 23 24 25

Table 3 is the competence matrix between machines and operators, with cells containing “1” in case an operator is qualified to work with the specified machine. In all the test instances we assume that a operator who is eligible to work with a machine, is able to process all operations assigned to that machine. In other words, for a machine m ∈ M and an operation i ∈ θ, Wm ∩ Wi = Wm . Table 3. Machines-operators data Machines\operators 1 2 3 4 5 6 1

1 0 0 1 1 0

2

0 1 0 1 0 1

3

1 1 0 0 1 0

4

0 0 1 1 1 0

5

0 1 1 0 0 1

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Table 4. Operations-machines data Operations\Machines 1

2 3 4

5

1

–

– 1 –

–

2

3

– – –

–

3

–

6 – –

–

4

–

– – 7

–

5

–

– – 3

3

6

–

8 – –

–

7

–

– 5 –

–

8

–

– – –

10

9

–

– – 10 –

10

10 – – –

–

11

–

– 5 –

5

12

–

– – 4

–

13

–

8 – –

–

14

9

– – –

–

15

–

1 – –

1

16

–

– 9 –

–

17

–

3 – –

–

18

–

– – –

5

19

–

4 – 4

–

20

3

– – –

–

21

–

3 – 3

–

22

–

– – 3

–

23

–

– 9 –

–

24

10 – – –

–

25

–

4

– – –

Table 4 displays the relationship between operations and machines; the numbers indicate the processing time of the operation on that machine in hours, a dash is included in case a machine is not qualified to process the specified operation. The MIP model is solved with this instance using Gurobi 10.1 API as solver. Figure 2, displays the resulting Gantt chart of the MIP results of this small test instance. In this Gantt chart, the rectangles with the same color represent the operations of the same job. Furthermore, the start and completion times of the operations on the machines are displayed, and the operator assignments are also shown. One can easily check that the obtained schedule satisfies the constraints and that the optimal objective value is 80 units of time. The CPU computational time to obtain this solution is 105 s.

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Fig. 2. Result of MIP for the small test instance

4

Computational Results

The small test instance presented above was meant to verify that the proposed MIP model works correctly, and also visualize the expected outputs. In this section we present the results of the MIP on a set of larger instances to evaluate its performance in terms of computational time. Note that the code is written in C++ and we use the Gurobi 10.1 API as the MIP solver. All the implementations are done on a desktop with 16 GB of RAM, and an 11th Gen Intel(R) Core(TM) i7-11850H @ 3.60GHz processor. Table 5. Computational results Instance number Scale of the instance

MIP Obj Gap

1

5 × 64 × 11 × 15 × 7

101 32.2 %

2

5 × 64 × 11 × 15 × 7

111 21.6%

3

10 × 128 × 15 × 19 × 10 140 47.4%

4

10 × 128 × 15 × 19 × 10 146 48.10%

5

15 × 192 × 21 × 26 × 10 187 74.33%

6

15 × 192 × 21 × 26 × 10 156 73.1%

7

20 × 256 × 29 × 35 × 14 166 78.92%

8

20 × 256 × 29 × 35 × 14 144 76.39%

In Table 5, the computational results for the larger instances are provided. In the second column of this table, the numbers indicate the number of jobs,

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operations, machines, operators and working days, respectively. Note that we assume each day consists of three shifts and that each shift lasts 8 h. All instances have been tested with a time limit of 5 h. The column “Gap” indicates the solver gap (deviation of the best solution found from the best lower bound found so far) for the instances. The results show that the model is able to generate feasible solutions for all of these instances, however the gaps are in general still too high (more than 20%) after 5 h of computational time. This indicates that we must strengthen the current formulation to tackle large instances, and probably move to heuristic approaches for industrial size instances.

5

Conclusion

In this paper we propose a MIP model for to solve integrated flexible job shop scheduling problems and shift-based operator scheduling. These are the typical scheduling problems that must be solved for flexible manufacturing systems (FMS). The proposed formulation is illustrated on a small instance to demonstrate its validity and the output we would like to generate. Our results obtained on larger test instances show that the MIP gaps are still quite large even after 5 h of computational time. It is clear that the proposed model would require quite a large amount of computational time, when dealing with large scale problem instances. As our goal is to solve large industrial cases, we are currently developing other approaches to strengthen the MIP model, together with heuristics and meta-heuristics to obtain high-quality solutions in a reasonable amount of computational time. To develop such heuristic methods, we are investigating decomposition techniques. We would in this case use high performing meta-heuristics to solve the FJSSP combined with the operator scheduling meta-heuristics. Acknowledgments. This research work is carried out within the framework of the Schedule4FMS project funded by Flanders Make, the strategic research centre for the manufacturing industry in Flanders, Belgium.

References 1. Groover, M.P.: Automation, Production Systems, and Computer-Integrated Manufacturing. Pearson Education India (2016) 2. Garey, M.R., Johnson, D.S.: Computers and Intractability, vol. 174. Freeman San Francisco (1979) 3. Applegate, D., Cook, W.: A computational study of the job-shop scheduling problem. ORSA J. Comput. 3(2), 149–156 (1991) 4. Brucker, P., Jurisch, B., Sievers, B.: A branch and bound algorithm for the job-shop scheduling problem. Discret. Appl. Math. 49(1–3), 107–127 (1994) 5. Taillard, E.D.: Parallel taboo search techniques for the job shop scheduling problem. ORSA J. Comput. 6(2), 108–117 (1994) 6. Nowicki, E., Smutnicki, C.: A fast taboo search algorithm for the job shop problem. Manag. Sci. 42(6), 797–813 (1996)

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7. Beck, J.C., Feng, T., Watson, J.-P.: Combining constraint programming and local search for job-shop scheduling. INFORMS J. Comput. 23(1), 1–14 (2011) 8. Ku, W.-Y., Beck, J.C.: Mixed integer programming models for job shop scheduling: a computational analysis. Comput. Oper. Res. 73, 165–173 (2016) 9. Xiong, H., Shi, S., Ren, D., Hu, J.: A survey of job shop scheduling problem: the types and models. Computers Oper. Res. 105731 (2022) 10. Dauz`ere-P´er`es, S., Paulli, J.: An integrated approach for modeling and solving the general multiprocessor job-shop scheduling problem using tabu search. Ann. Oper. Res. 70, 281–306 (1997) 11. Paulli, J.: A hierarchical approach for the FMS scheduling problem. Eur. J. Oper. Res. 86(1), 32–42 (1995) 12. Mastrolilli, M., Gambardella, L.M.: Effective neighbourhood functions for the flexible job shop problem. J. Sched. 3(1), 3–20 (2000) 13. Bo˙zejko, W., Uchro´ nski, M., Wodecki, M.: Parallel hybrid metaheuristics for the flexible job shop problem. Comput. Ind. Eng. 59(2), 323–333 (2010) 14. Naderi, B., Roshanaei, V.: Critical-path-search logic-based benders decomposition approaches for flexible job shop scheduling. INFORMS J. Optim. 4(1), 1–28 (2022) 15. Lei, D., Guo, X.: Variable neighbourhood search for dual-resource constrained flexible job shop scheduling. Int. J. Prod. Res. 52(9), 2519–2529 (2014) 16. Tan, W., Yuan, X., Wang, J., Zhang, X.: A fatigue-conscious dual resource constrained flexible job shop scheduling problem by enhanced NSGA-II: an application from casting workshop. Comput. Ind. Eng. 160, 107557 (2021) 17. Yazdani, M., Zandieh, M., Tavakkoli-Moghaddam, R., Jolai, F.: Two meta-heuristic algorithms for the dual-resource constrained flexible job-shop scheduling problem. Scientia Iranica 22(3), 1242–1257 (2015) 18. Andrade-Pineda, J.L., Canca, D., Gonzalez-R, P.L., Calle, M.: Scheduling a dualresource flexible job shop with makespan and due date-related criteria. Ann. Oper. Res. 291, 5–35 (2020) 19. Dhiflaoui, M., Nouri, H.E., Driss, O.B.: Dual-resource constraints in classical and flexible job shop problems: a state-of-the-art review. Procedia Comput. Sci. 126, 1507–1515 (2018) 20. Kress, D., M¨ uller, D., Nossack, J.: A worker constrained flexible job shop scheduling problem with sequence-dependent setup times. OR Spectr. 41, 179–217 (2019)

Food and Bio-manufacturing

Towards More Sustainable Food Processing: A Structured Tool for the Integration and Analysis of Sustainability Aspects of Processing Equipment Sara Esmaeilian1(B) , Anita Romsdal1 , Eirin Skjøndal Bar2 , Bjørn Tore Rotabakk3 , Jørgen Lerfall2 , and Anna Olsen1 1

2

Department of Mechanical and Industrial Engineering, NTNU, 7491 Trondheim, Norway Department of Biotechnology and Food Science, NTNU, 7491 Trondheim, Norway 3 Nofima AS, Richard Johnsens Gate 4, 4021 Stavanger, Norway Abstract. Sustainable food production along with food security and safety demands attention. Reducing the undiagnosed impacts of the food processing sector contributes to the transition towards a more sustainable food production system. Consequently, food processing technologies and production planning should be developed or modified with caution to align with sustainability issues. Appropriate tools are needed to ensure the complete coverage of different aspects of sustainability in the design phase and to recognize the opportunities for sustainability improvements in the use phase. This study proposes a structured tool to analyze the sustainability of food processing technologies from the stakeholder’s points of view, that can be used to make more knowledgeable decisions and find manageable trade-offs. The proposed tool is adapted from the acknowledged Sustainable Development Analytical Grid (SDAG) tool. The theoretical contribution of this study is the synthesis of literature to identify sustainability criteria for integrating into the design phase, thereby enhancing sustainability across the entire life cycle. A case study from the food sector illustrates the applicability of the tool and suggests solutions to address the identified sustainability issues. Future research should strengthen the validity and applicability of the proposed tool through additional cases. Keywords: Sustainability

1

· food processing equipment · design phase

Introduction

The increased need for food globally besides population growth and on the other hand experiences of large loss of edible food resources along the whole value chain demand immediate attention. As a result, food security and safety along with c IFIP International Federation for Information Processing 2023  Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 473–488, 2023. https://doi.org/10.1007/978-3-031-43688-8_33

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sustainable food production are prioritized in the mainstream of European politics and are also the targets of the Sustainable Development Objectives (SDGs) and Agenda 2030 issued by the United Nations (UN) [1]. The food processing sector often has undiagnosed impacts on sustainability dimensions. Reducing these impacts contributes to the transition towards a more sustainable food production system [2]. For instance, halving food waste during processing could significantly reduce land use and eutrophication potential due to nitrogen release [3]. In addition, it is shown that redesigning food processing equipment (FPE) with a focus on increasing main product yield and reducing loss could have a significant contribution to the reduction of environmental impacts of the salmon supply chain [4]. Reducing water consumption by one-third in a hake filleting plant and lowering the organic content of wastewater in a herring filleting plant are some other practical examples, in which sustainability improved by optimizing or redesigning the processing technologies [5]. Sustainability can be integrated in both the design and the use phases. For the design phase, sustainability is considered as the potential design criterion. On the other hand, in the use phase of existing equipment, it is assumed as a modification. Since in the design phase, there is more freedom of action with a lower modification cost, most of the sustainability is locked into this step [6]. In other words, the design phase can be used as a leverage point for reducing sustainability impacts [4]. Accordingly, giving priority to integrating sustainability into this step is important. Improving the sustainability performance of processing technologies in the design phase means facing all three dimensions of sustainability; environment, society, and economy, at the same time. Traditionally, the design of technologies has been mainly guided by technical and micro-economic decision criteria to ensure that it is ‘fit for purpose’ with the maximum financial returns [7]. Even though some of the environmental and social criteria such as system’s emissions and health and safety are already integrated into traditional design procedures, their impacts are often overlooked. They are still considered as an ‘after-thought, once the technical and economic components have been finalized [7]. From a contingency standpoint for the design of new FPE, we must first identify the areas where existing technologies have the greatest potential to enhance sustainability performance. Appropriate tools are needed to recognize these areas and ensure the complete coverage of different aspects of sustainability. The purpose of this study is to develop a structured tool that can assist product developers in appraising the domains for sustainability improvements, which is crucial for designing a more sustainability-friendly FPE. In addition, it enables the food processing company to situate itself within a sustainability framework and presents means to enhance its performance while striving for continuous improvement. This can be achieved by analyzing the performance of the existing food processing technologies across all dimensions. To this aim, first, the sustainability aspects associated with FPEs are extracted from the literature. Next, considering these aspects, a set of sustainability objectives are defined such that their fulfillment satisfies sustainability dimensions. The objectives are categorized into different themes each allocated to one of the dimensions to

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develop the sustainability assessment tool. Finally, a case study is conducted, illustrating the applicability of the tool.

2

Theoretical Background

In this section, we begin by discussing the conventional principles of FPE design, leading to a discussion regarding the importance of integrating all sustainability dimensions in the design phase. Finally, we explore the research gap in this area. 2.1

Design of FPE

Conventionally, the principles of FPE design and manufacturing involve the assessment of sizing and costing of equipment, the body material selection, and finally equipment fabrication. According to the handbook of food processing equipment [8], two groups of construction and operational characteristics should be considered in the design stage. The construction characteristics are the design criteria that the equipment will be constructed based on such as dimension, weight, quality of materials, firmness, durability, and so on. The operational characteristics, on the other hand, are features facilitating the operation of the equipment namely convenience, ergonomics, efficiency, accuracy, effectiveness, environmental impacts, and so forth. Manufacturers often overlook environmental and social criteria during the design process, considering them only after the technical and economic aspects. This can lead to sub-optimal system performance and neglecting sustainable alternatives [7]. In addition, at the early stage of product development, one must be conscious of the eco-design paradox. Focusing solely on reducing one environmental impact without considering upstream or downstream impacts can lead to unintended negative consequences in other areas of sustainability [9]. 2.2

Importance of Integrating Each Sustainability Dimension in Design Phase

Covering all sustainability dimensions mandates acknowledging the importance of each dimension in the design phase. Failure to recognize this importance might result in hesitation to take essential measures and engage in appropriate planning. On top of the economy, environment, and society, a forward-looking view of “sustainability” becomes a synonym for the compatibility of the product with forthcoming trends in the interest industry, introducing future proof dimension. Economic: economic dimension is usually regarded as a ‘generic dimension’. Economic sustainability involves addressing issues that enable a company to maintain competitiveness in the market over a long time [10]. It also assesses the value a company creates in the short and long term and at different levels, from local to global levels [11]. As a result, it’s essential to consider the financial feasibility of each step in product development to design a product that is economically sustainable for the future.

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Environment: design for environment (DfE) is the development of products by considering environmental criteria to reduce their environmental impacts across all stages of their life cycle [12]. Studies have shown up to 90% of the environmental impacts of a product are determined in the product development process. Thus, environmental requirements should be introduced as early as possible into the design phase alongside quality, cost, and safety requirements [13]. Society: social sustainability pertains to a product’s impact on the social system in which it operates and deals with issues such as human well-being. However, this dimension is often neglected and considered a’concept in chaos’ [14]. The findings show that although the companies have adopted several kinds of International Organization for Standardization (ISO) standards, social sustainability is still absent from their operational activities [15]. Nevertheless, the extensive role of human beings in manufacturing, namely strategy, knowledge, design, control, resilience, etc., highlights the fundamental role of this dimension [16]. Consequently, human factors should be considered in addition to the technical issues during the design process [17]. Future Proof: thinking about the future is about preparedness, identifying drivers of change, and making wise decisions. Understanding the trends can help product developers proactively manage the major shifts before it becomes too late. The trend represents a profound trajectory of change that will occur over the next few decades, and while change might start gradually, it will eventually have a significant impact [18]. Therefore, having a sustainable FPE demands a design adapted to the upcoming trends in the food industry. The more compatible with the forthcoming trends, the more future-proof product. 2.3

Research Gap

A comprehensive tool is needed to address all dimensions simultaneously. The existing tools are often limited to one dimension, quantitative data dependent, not FPE-focused, or lack stakeholder input in generating sustainable solutions. Azapagic et al. (2006) and Sch¨ oggl et al. (2017) propose new methodologies for integrating different sustainability dimensions into the design step of a chemical process and automotive manufacturing, respectively [7,19]. However, these tools are customized for their specific field of application and not covering all FPE-relevant sustainability aspects. Bar (2015) conducted a life cycle assessment (LCA) study on a sorter and grader equipment to identify environmental design requirements lined to the equipment’s lifecycle. However, the study only addresses the environmental aspect of sustainability [4]. In addition, LCA as the most commonly used tool for the quantitative assessment of sustainability can only be applied to fully developed products whose components, processes, and materials, are already detected. As a result, it is inappropriate for a new product design where there is a high degree of uncertainty and limited data and experience [20]. The Global Reporting Initiative Sustainability Reporting Standard (GRI 13) offers guidelines for sustainability reporting, including the assessment of aquacul-

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ture industry’s sustainability. It offers valuable post-hoc data, insights, and identifies areas needing improvement for more sustainable product design. However, it’s a broad reporting standard that may not fully encompass all sustainability aspects of FPE and primarily focuses on environmental and social impacts, neglecting economic aspects. Another tool, the Sustainable Development Analytical Grid (SDAG), is a versatile and scientifically robust assessment tool. Developed as part of the SDG Acceleration toolkit, it forms a framework for assessing the sustainability of projects, strategies, and programs in the context of Agenda 2030. Its comprehensive coverage of sustainability dimensions and its emphasis on stakeholder participation sets it apart [21]. However, it uses a generic criterion that may not be precise enough for FPE, possibly overlooking critical aspects such as food safety, hygiene, and energy efficiency. Despite the growing literature on developing sustainability assessment tools, there remains a gap in assessing all sustainability aspects and improvement areas across all dimensions specifically for food processing technologies.

3

A Tool for Sustainability Analysis of FPE

The purpose of this section is to develop a tool for sustainability analysis specifically for FPE, bridging the research gap. To this aim, first, we examine all the aspects through which FPE impacts the sustainability dimensions across its life cycle. Then, this examination is utilized as a foundation for developing the tool and finally, the method for conducting sustainability analysis is explained. 3.1

Sustainability Aspects Associated with FPE

Examining sustainability aspects related to FPE helps to identify potential ways that a FPE impacts sustainability dimensions throughout its whole life cycle. Evaluation of these impacts can validly be used to guarantee the design of a more sustainable product and their resolution contributes to SDGs targets [32]. The aspects are extracted from the literature for different dimensions of sustainability. It has been performed by analyzing manuscripts in scientific databases, namely ScienceDirect, Scopus, and Google Scholar. The following keywords and combinations thereof were used: “food processing”, “environmental sustainability”, “social sustainability”, “economic sustainability”, “food processing equipment design”, and “sustainability impact”, “smart food processing technology”. The titles and abstracts were assessed individually for their relevance. The sources were collected in Mendeley (Elsevier), and duplicates were removed. The initial criteria for inclusion were peer-reviewed journals, books, or reports written in English. To properly analyze these aspects, it is important to establish the scope of the analysis and choose an appropriate time frame for investigation [33]. In addition, the eco-design paradox highlights the need for a holistic approach to a sustainability-induced design, where the entire life cycle of a product is taken into

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Table 1. Sustainability aspects per life cycle phase. Sustainability Dim. = Sustainability dimension, Env.= Environmental, Eco. = Economical, Soc. = Social, F.P. = Future proof. Life cycle phase Sustainability aspect

Sustainability Dim. Env Eco Soc F.P.

Manufacturing

Manufacturing complexity [8]

x

Manufacturing cost [19]

Use

x

Body Materials impact [4]

x

Durability [4]

x

Equipment weight and volume [19]

x

x

Design for clean-ability [8]

x

Design for dismantling [22]

x

x

Water consumption [23]

x

x

Washing agents use [4]

x

Food waste during processing [24]

x

Greenhouse emissions of refrigerators [24]

x

Atmospheric emissions (exhausted gases, steam, etc.) [24, 25]

x

Energy consumption [24]

x

Noise emission [23, 26]

x

x

Liquid effluents [23]

x

x

Odor emission [23]

x

x

Machinery waste products such as sludge and used chemicals and their pollution [17, 23]

x

x

x

Food safety [27]

x

Capital and operating cost [19]

x

Convenience to work with [8]

x

Energy demands for a specific task [28]

x

Task repetition [17]

x

Ergonomics conditions with considering gender, age, and the level of demanded concentration of task [8]

x

Task duration [17]

x

Monotonous task [28]

x

Safety issues [8]

x

Thermal conditions [29]

x

Lightening condition [17]

x

Occupied space of equipment and the space in between equipments in line processing [17] Industry 4.0 compatibility [27]

x

Data management and digitalization [30]

x

Smart production planning and control [31] End of life

x x

x

Reuse [19]

x

x

Recycling [22]

x

x

Material labeling [19]

x

x

Disposal [22]

x

x

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account [9]. For FPE, the sustainability aspects are classified into the three life cycle phases of manufacturing (design and fabrication), use (operation and maintenance), and end-of-life (Table 1). The categorization of sustainability aspects into life cycle phases of FPE is based on when these aspects are most relevant and influential in determining the equipment’s sustainability performance. 3.2

Development of a Tool for Sustainability Objectives for FPE

The study uses literature findings in Table 1 to develop objectives, whose fulfillment satisfies relevant sustainability dimensions and associated SDGs. For instance, minimizing food-grade water consumption contributes to SDG 6 - sustainable water management. The tool is inspired by sustainability aspects across all life cycle phases (Table 1), translated into objectives that should be considered in the manufacturing phase of equipment. These objectives also include aspects from interviews at the case company and existing sustainability frameworks mainly [4,21], and [19]. The objectives are then themed depending on the way that they impact the dimension. For instance, the objective categorized in the ecosystem protection theme could positively impact the environment by protecting the ecosystem. The framework summarizing the objectives for design consideration in the manufacturing phase is shown in Table 2. Table 2. Sustainability themes and objectives across dimensions for design consideration in equipment manufacturing process. Dim. = Dimension, IoT = Internet of Things, AI = Artificial Intelligence. Dim

Theme

Sustainability Objectives

Environment Ecosystem protection • Minimize yield loss during processing • Facilitate optimal use of rest raw material Resource efficiency

• Minimize energy consumption • Minimize food-grade water consumption • Efficient and easy clean-ability • Easy-to-dismantle • Weight and occupied space reduction • Choose low-impact body materials • Choose easy-to-clean materials • Use recyclable materials • Use durable materials • Plan for the prudent use of resources • Optimize resources nearing their end

Output control

• Identity liquid, solid and gaseous outputs • Reduce their negative environmental impacts • Reduce need for washing agents/disinfectant • Minimize noise emission • Minimize odor emission (continued)

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Table 2. (continued) Dim

Theme

Sustainability Objectives • Minimize liquid effluents • Minimize solid wastes, e.g., sludge • Manage hazardous waste properly

Climate change

• Quantify greenhouse gas (GHG) emissions • Reduce GHG emissions • Compensate for greenhouse gas emissions • Reduce atmospheric emissions like steam • Plan climate adaptation measures

Social

Food integrity

• Ensure food safety during processing • Ensure food security • Align processing impact on food quality with consumer preferences

Health

• Provide an ergonomic condition for employees • Consider gender status in ergonomic design • Reduce task duration • Reduce task repetition • Reduce susceptibility to machine pollutions • Reduce task energy requirement • Foster a healthy environment • Reduce factors causing mental health issues • Reduce irritants

Safety

• Create a feeling of security • Ensure effective safety • Provide basic safety education

User-friendly

• Easy to be trained and work with • Ensure a non-complicated FPE design

Work environment

• Reduce noise pollution • Reduce heat generation due to processing • Provide proper thermal conditions • Provide a proper lightening condition

Economic Responsible production • Producing quality goods and services • Ensure a time-efficient/immediate processing • Ensure a continuous line processing • Easy and predictive maintenance • Ensure match of needs and produced goods • Promote eco-design in product life cycle • Promote sustainable production • Implement extended producer responsibility Economic viability

• Ensure economic viability • Minimize capital and operational cost • Ensure a high-profit margin • Adhere to limiting the return on capital • Limit the financial risks

Job creation

• Enhance job creation (continued)

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Table 2. (continued) Dim

Theme Energy cost

Sustainability Objectives • Reduce energy consumption • Plan a wise use of energy

Future proof Innovation

• Increase innovation potential in equipment • Promote R&D involvement in design • Have a more automated operation • Develop robotic washing solutions • Develop new FPE drying technologies • Enhance equipment versatility • Optimize cooperation between processes • Consider future trends in the food industry • Equip FPE with Industry 4.0, e.g., IoT, AI

Risk management

• Manage risks related to new technologies • Identify risks at different operation levels • Apply the principle of prevention • Easy to control in the case of FPE failure • Promote an equitable distribution of risks • Plan for adaptation to global changes

Data digitalization • Monitor food processing during operation • Smart production planning and control, Digitalizing processing data

3.3

Method for Analysis of FPE Sustainability

The developed tool is versatile and beneficial to diverse stakeholders like product developers, FPE manufacturers, and food processors. It identifies areas for improvement and addresses all sustainability dimensions early in development, enhancing FPE sustainability. It also helps practitioners such as food processors analyze their operations’ sustainability performance. The analysis involves weighting, a performance assessment based on planned or already implemented actions, and the generation of ideas for improvements where required. This method of analysis makes it possible to prioritize the objectives that need to be addressed in a continuous improvement process. Optimal enhancement of sustainability performance necessitates giving the sustainability objectives the importance they demand. This is also a crucial step when the trade-offs between the objectives in the design process are inevitable [22]. The developed tool weighs the objectives based on the stakeholders’ perspectives, allowing the stakeholders to fully play their roles in assessing sustainability. Together with weighting analysis, assessing how well a company meets sustainability objectives through its processing technologies, and production planning demonstrates the company’s capacity to adhere to SDGs and provides a better understanding of the reasons behind that. The weighting and performance assessments are conducted based on the established tool of SDAG, whose assessment methodology is simple, efficient, and

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scientifically robust [21]. For the weighting, each objective needs to be assigned a numerical value ranging from 1 to 3 according to its level of significance, which is determined as follows. (1) desirable objective: achieving this objective is not deemed important or is not a priority. (2) important objective: achieving this objective is important but is not one of the immediate priorities related to the needs targeted by the company. (3) indispensable objective: achieving this objective is important and is an immediate priority. It is deemed indispensable to the success and aim of the company. Regarding the performance assessment, a numerical scale from 0 to 100% should be used as follows, scoring the sustainability performance of each objective. – Below 20%: This objective is not considered. – Between 20% and 39%: This objective is insufficiently considered. – Between 40% and 59%: This objective is slightly considered; with no concrete actions and measures, and minimal positive impacts are expected. – Between 60% and 69%: This objective is moderately taken into account, with planned actions, but with no innovative elements. – Between 70% and 79%: This objective is taken into account, with concrete actions and some innovative elements but still improvable. – Between 80% and 89%: This objective is well taken into account, with innovations and concrete measures, significant positive impacts are expected. – Between 90% and 100%: This objective is strongly taken into account; the company is exemplary in that respect. The overall sustainability performance of themes and dimensions determined by taking a weighted average over all the designated performance percentages for respective objectives. We propose a weighted average is more accurate than a simple average and measures an average that reflects the relative importance of each objective.

4

Case Study

In this section, we first introduce the case company and then present the case findings with regard to the sustainability of the company’s deployed FPE and production planning using the tool in Table 2. The interview, attended by researchers and the company’s operational manager, began with the participant signing a consent form and being informed about the sustainability analysis method. A survey listing all objectives for assessment and weighting was given, along with a detailed guide for interviewers. Participants were encouraged to provide additional details during the assessments. The interview was somewhat spontaneous, with questions asked based on the company’s performance in each theme, probing for areas of excellence or potential improvement.

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483

Introduction to Case Company

The interview is conducted on the salmon filleting sector of a Norwegian seafood company, whose strategy is sustainable growth in the entire value chain and satisfying quality-conscious consumers. This company could be a typical representative of a wider class of food processing companies with the same strategy. The company operates in the aquaculture industry, specifically in the farming of Atlantic salmon and rainbow trout. As of 2022, the company had around 275 employees across its land-based and sea-based locations. In 2020, it generated a turnover of NOK 2.2 billion and produced around 3.6 million smolts that year. To illustrate the applicability of the tool on an existing FPE, a commonly used FPE in the processing industry, the trimming machine is chosen. Trimming is performed after the fish has been cut into fillets. 4.2

Results and Insights from Case Study

The importance and performance percentage of each sustainability dimension given by the case company is illustrated in Fig. 1. The average weighting numbers have been normalized and presented as percentages to simplify the comparison with performance results. Figure 1 shows that the case company has a hierarchy for weighing different dimensions, with the future-proof dimension being the most important, followed by the economic, social dimensions, and the environmental dimension as the least important one. Prioritising of future proof dimension on economic dimension highlights the economic benefits of being a future-proof company. Investing in high technology and smart production increases profitability by enhancing efficiency in production time, energy consumption, and operating costs, thereby

% Importance % Performance

100 87 80

87

90

90

93 79

77

Percentage

63 60

40

20

0 Environment

Social

Economic

Future proof

Sustainability dimension

Fig. 1. Importance and performance percentage of case company for all sustainability dimensions.

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Table 3. Average weighting and performance for sustainability dimensions and themes. Ave. = Average. Dimension (Theme)

Ave. weighting Ave. performance

Environment

1.9

77%

Ecosystem protection Resource efficiency Output control Climate change

2.5 2.3 1.8 1.2

82% 78% 79% 70%

Social

2.6

87%

Food integrity Health Safety User-friendly Work environment

3 2.1 3 2.5 3

92% 79% 93% 86% 92%

Economic

2.7

90%

Responsible production Economic viability Job creation Energy cost

2.8 2.6 2 3

88% 92% 90% 90%

Future proof

2.8

79%

Innovation 2.8 Risk management and resilience 3 2.5 Data digitalization

77% 78% 88%

reducing long-term expenses. Additionally, establishing a reputation as a sustainable company with future-proof technologies improves the company’s brand image which can lead to increased sales and customer loyalty. Although the company places significant importance on the future-proof dimension, they have not achieved a comparable level of performance in meeting this dimension (Fig. 1). The average performance of this dimension is 79% meaning that it is taken into account but there is still room for improvement. The reasons for this could include insufficient financial resources, the company’s resistance to change, inadequate infrastructure, ineffective planning and strategy, difficulties in integration of new technologies, and lack of data analysis, resulting in missed opportunities for improvement in their performance. The economy is the next important dimension which is assigned the same level of performance as its weight. The high 90% of performance demonstrates that the company has strongly taken into account this dimension and has imposed enough measures such as strong financial management, marketing and sales strategies to satisfy this dimension and its relative themes.

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The social dimension is weighted as the next significant dimension, with a high priority on food integrity including food safety and security, employee safety, and an appropriate work environment in the operation sector (Table 3). Its high performance of 87% shows the corresponding themes are well taken into account with concrete measures and positive impacts are expected. A comparable performance and importance level for this dimension highlights the strong focus of the company on meeting customers’ or employees’ needs and providing excellent customer service. This could lead to increased customer loyalty, repeat business, and positive word-of-mouth marketing. The last and the least important dimension from the company’s perspective is the environmental dimension. However, the company’s performance in this area is higher than its given importance level (Fig. 1). The potential explanation is that the environmental aspects are usually met at the minimum level required by legislation [7]. As regulatory authorities prioritize environmental protection, companies tend to comply with regulations to avoid the risk of costly fines or lawsuits and improve their long-term financial stability. It is evidenced by the high average performances of 82% and 79% allocated to ecosystem protection and output control themes, respectively, compared to other themes of this dimension (Table 3). On the other hand, considering the general overlooking perspective of the company to this dimension, the company may feel that they are already doing more than necessary in this area due to legislation, giving it higher performance than the actual level that it has. The low performance and importance level assigned to the environment compared to other dimensions necessitated an urgent change in perspective. Climate change represents a new and somewhat daunting topic for many companies as evidenced by the low weight and performance given to this theme (Table 3). However, investigating its potential risks to business and required actions to mitigate those risks is of great importance [34]. The case company suggested ideas for sustainability improvements in different dimensions. Some of them are namely avoiding waste of fillets while flipping, saving electricity by turning off lights and machines during idle times, cleaning and distilling seawater to use as food-grade water, promoting a healthy work environment, using automation and high-technology equipment, being compatible with future trend of on-board processing, controlling energy usage, facilitating data digitalization, and improving risk assessment. The company also emphasizes the importance of collaboration with FPE manufacturers, researchers, and product developers to promote innovation and responsible production.

5

Conclusions and Directions for Future Research

The food processing sector often has undiagnosed impacts on sustainability dimensions and reducing these impacts contributes to the transition toward a more sustainable food production system. This paper has three main contributions. Firstly, it synthesizes the literature to identify the sustainability aspects associated with FPE and then outlines a

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set of sustainability objectives specifically for the food production system, categorized into different themes and dimensions. Moreover, it considers the future proof as one of the sustainability dimensions, which are mostly defined by three pillars of economy, society, and environment. Secondly, it proposes a framework, adapted from the acknowledged SDAG tool, enabling to prioritize the sustainability objectives and identify areas where sustainability performance has the highest potential to be improved across the whole life cycle. Providing opportunities for sustainability improvements, the framework encourages the company to the deployment of more sustainable technologies and production planning which also enhances its reputation in sustainability. Thirdly, the case study illustrates how the framework in Table 2 can be used to analyze the sustainability of a company’s technologies and its production planning. Additionally, it generates a set of ideas for improvement to be applied to the most critical sustainability objectives. Future research should strengthen the validity and applicability of the proposed tool through additional cases across food industrial sectors. It would also be interesting to interview FPE manufacturers, who are another primary stakeholder. By interviewing FPE manufacturers, we can also identify additional needs of their customers, the food processors, and discover common pain points and areas for improvement. More importantly, comparing the results of interviews with both stakeholders can help identify similarities and differences in priorities and concerns, enabling product developers to make knowledgeable design decisions. Overall, considering different stakeholders’ perspectives assists in gaining a deeper understanding of the industry’s priorities and concerns, informing design decisions and production planning, leading to a more sustainable food production system. Acknowledgments. This research was supported financially by the Research Council of Norway, project number 294641, as part of the SGS project. Many thanks to Jesper Van Der Molen for his assistance in conducting interviews for this study.

References 1. Brooks, I.: The united nations sustainable development goals in systems engineering: eliciting sustainability requirements. In: ACM International Conference Proceeding Series, pp. 196–199 (2020) 2. Kumar, P.S., Joshiba, G.J.: Energy and carbon footprint of food industry. In: Muthu, S.S. (ed.) Energy Footprints of the Food and Textile Sectors, pp. 19–44 (2019) 3. Read, Q.D., et al.: Assessing the environmental impacts of halving food loss and waste along the food supply chain. Sci. Total Environ. 712, 136255 (2020) 4. Bar, E.M.S.: Advanced food processing equipment design for a sustainable salmonid fish industry in norway. Ph.D. thesis (2015) 5. COWI: Cleaner production assessment in fish processing. Technical report (2000) 6. Lindahl, M.: Engineering designers’ requirements on design for environment (DfE) methods and tools. Ph.D. thesis (2005)

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Transforming Food Production: Smart Containers for Sustainable and Transparent Food Supply Chains Peter Burggräf1,2 , Tobias Adlon1 , Fabian Steinberg2 , Jan Salzwedel1(B) Philipp Nettesheim2 , and Henning Tschauder1

,

1 Laboratory for Machine Tools and Production Engineering (WZL) - Factory Planning, RWTH

Aachen University, Campus-Boulevard 30, 52074 Aachen, Germany [email protected] 2 Chair of International Production Management and Engineering, University of Siegen, Paul-Bonatz-Str. 9-11, 57076 Siegen, Germany

Abstract. Globally, one-third of the food produced is discarded, most of it along the food supply chain. Monitoring the food supply chain could significantly reduce rejects and waste along it. Especially in the production of perishable foods, monitoring compliance with food hygiene is demanding as multiple parameters must be maintained: e.g. temperature and pressure. Such perishable products are usually stored and transported in metal intermediate bulk containers (IBCs). IBCs are, in most cases, a black box during use, providing no additional benefit to manufacturers. Therefore, as part of the smart.CONSERVE research project, smart containers were developed to monitor the critical properties of the stored food products, such as temperature and pressure. By equipping the containers with modular sensor technology that collects relevant data from the transported food, new data can be generated along the entire supply chain and production processes. This can prevent resource waste and increase production quality and sustainability. This data includes filling levels and container locations, offering, for example, new use cases for inventory management. In this paper, we present the business model, the technical solution developed, possible scenarios for retrofitting existing containers, and the validation of the developed solutions. We discuss what is needed for actual industrial implementation and whether the concept is transferable to other industries. Lastly, we put the results and approach of our project in a broader context and reflect on perspectives for the developed solution, both in research and industrial applications. Keywords: Food Industry · Supply Chain · Sustainability · Smart Solutions · Smart Services

1 Introduction According to the Food and Agriculture Organization of the United Nations (FAO), roughly one-third of the edible parts of food produced yearly for human consumption get lost or wasted globally – about 1.3 billion tons. More specifically, about sixty per cent © IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 489–503, 2023. https://doi.org/10.1007/978-3-031-43688-8_34

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of this food is wasted along the supply chain [1]. Transport and storage of perishable goods in the food industry, such as fruit preparations, require significant attention to ensure the quality and safety of the products. The traditional methods of monitoring the temperature and inside pressure of the containers need to be revised to guarantee the products’ preservation. Therefore, an intelligent container system that can monitor the containers’ conditions and alert the stakeholders in case of deviations is essential. The food industry is currently caught between the need for cost-efficient production and the high requirements regarding the documentation of compliance with the required food hygiene throughout the entire process and supply chain. Following scandals in the food industry, e.g., mad cow disease, listeria, and bird flu, customers have become more sensitive to the quality and origin of the food. Therefore, a transparent supply chain increases customer trust [2]. In most cases, food products are commodities: they do not differ significantly and are, therefore, easily substitutable [3]. The procurement down the supply chain is mainly influenced by quality or the lowest price [2]. To remain competitive, companies must reduce their production costs or offer additional services [4]. The costs of such services must be counterfinanced by savings in the production processes as customers’ willingness to pay for them is low. This is due to the highly price-sensitive food market and the often small margins (cf. Section 2). Production costs can be reduced, for example, by reducing waste in production, overproduction or perishing of products. The data of the smart container is a basis for process optimisations in production. Besides, the solution can potentially substitute multiple process quality monitoring steps with only one device, thus simplifying food production processes. Artificial intelligence (AI) and Machine learning (ML), in particular, can be helpful to realise savings in the food supply chain. ML is a branch of AI. It uses computers to create a system to learn from data through training. The system can predict the outcomes of questions based on previous learning [5]. With the help of ML, the data of a transparent supply chain could be used to create value by providing data-based services. ML applications are used to optimise production in various areas. In order release, AI can give load- and demand-based scheduling suggestions for manufacturing [6]. In addition, ML algorithms have been developed to improve demand forecasting. However, creating added value through enhanced supply chain transparency leads to higher costs in the short term, while the longer-term revenues are uncertain [7]. The authors focused on ML applications of the developed solution in a previous publication [8]. To meet these challenges, the project “smart.CONSERVE – Smart Container Services for Food Industries” aimed to equip stainless steel intermediate bulk containers (IBC) with intelligent information and communication technology using industrial research and experimental development. IBCs are used in many industries to transport and store goods such as beverages, food, chemicals or hazardous materials [9]. For hygiene and other food safety reasons, stainless steel IBCs are most commonly used in the food industry. The project aimed to develop a smart food container by upgrading standardised stainless steel IBCs with modular sensor technology. The sensors were added inside (filling level, pressure, temperature) and outside (location) the container and are modularly connected to a receiver device, the SmartCap. This represents the central monitoring system

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of the respective IBC and transmits data recorded by the sensor permanently via a cellular network to a platform/ cloud for evaluation. Another goal of the research project is the development of data-based smart services. Smart services are packages of products and services that can be individually configured via the Internet and tailored to the specific situation and needs of the respective user [10]. The smart services developed in the project are integrated flexibly and applicationspecifically into a software platform for IBC monitoring using SmartCaps. These services include, for example, inventory management, route optimisation for IBC transport or automated order-release systems. The collected data can be made available to users individually on the platform, in real-time and telematically. Four partners are involved in the project. Two universities drive research, while industrial partners contribute their practical knowledge and conduct the development. The IPEM of the University of Siegen is in charge of developing the business model and smart services. On the research side, the WZL of RWTH Aachen University leads the development of the technical solution and the production system. A major company from the food industry contributes its knowledge from the user perspective. The Packwise company has already developed a similar solution for plastic IBCs and is taking over the technical development. The project’s solution approach is divided into three sub-steps. First, a product databased business model is designed, demonstrating the potential uses of the data collected by the sensors. The created business model influenced the product requirements document used as a basis for the partly parallel second step: developing a solution for upgrading stainless steel IBCs with additional sensors and the SmartCap described above. In a third step, a retrofit concept enables the modular equipping of existing stainless steel IBCs with sensors. This contribution aims to provide an overview of the research project and a critical reflection of the results. Accordingly, Sect. 2 presents the essential elements of the developed business model. Section 3 introduces the developed technical solution. Section 4 describes different scenarios for upgrading existing containers with the created solution. Subsequently, Sect. 5 presents the test concept developed based on the restrictions of food legislation. Section 6 summarises the paper, contains a conclusion and reflects the project results.

2 Business Model This section analyses the business model of the designed solution based on the Business Model Canvas framework, a strategic management tool visually representing a company’s business model [11]. By using the Business Model Canvas, organisations can assess and refine their business models, identify potential areas for improvement, and align their strategy with market needs and opportunities. The following subsections discuss its most essential elements in the context of the research project: key partners, customer segments, value propositions, cost structure, and revenue stream of the business model.

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2.1 Key Partners For a successful business model in food logistics that makes conventional containers intelligent by equipping them with additional sensors, the cooperation of all process partners is crucial. Therefore, four key partners have been identified: the supplier of the raw material for the upstream supply chain, the fruit preparation manufacturer, the customer for the downstream supply chain, and the sensor packaging manufacturer. With these four partners, it is possible to unleash the full potential of smart containers in the supply chain. In addition, a manufacturer of certified stainless-steel IBCs has been identified as a necessary framework partner to reach a wide range of customers in the steel IBC market. 2.2 Customer Segments The technical solution developed in smart.CONSERVE is focused on the challenges of the food supply chain. Nevertheless, the business model can appeal to companies from various industries as potential customers, such as chemicals, pharmaceuticals or cosmetics. Different user groups can be identified for each of these customer segments that benefit from the data created along the supply chain: On the one hand, user groups include companies in the food industry that deal with producing, transporting, and storing fruit preparations. These companies face significant logistical challenges for which the smart container offers an innovative solution. In a second user group are all those companies that have a direct but less significant added value from the data, such as logistics companies or the manufacturer of the sensor solution, who could use the data for future product developments. A third user group includes users with an indirect added value from the data, such as consumer protection (i.e. transparency and future requirements), associations, or end customers (i.e. carbon footprint). The business model is designed to meet the needs of both small and large companies, with the option of premium services for those seeking value-adding features. Although the solution was developed focusing on the food industry, the results and the business model can be applied to other industries that use steel containers. 2.3 Value Proposition The value proposition of smart.CONSERVE addresses the logistical challenges faced by companies in the food industry. A central component of the value proposition is the certification of the developed solution for use in the food industry, accompanied by high requirements for resistance in the cleaning process. Moreover, the sensor package promises high accuracy of sensor data regardless of the products stored and transported. With the help of a cloud application (Packwise Flow), the data is processed and made visible to customers according to their needs. This also provides an interface into widespread customer systems. The essential services include classic monitoring services, such as monitoring container data to ensure product quality, automating orders, and optimising process flow. To identify valuable services, a series of customer interviews

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were conducted. The following services provide promising areas for further research and exploration. One such service identified is shelf life monitoring using digital twins, which brings significant advancements to perishable industries. By closely tracking best-before dates, customers can receive timely alerts for production adjustments while benefiting from secure proof of ingredient shelf life [12]. This enhances traceability and optimises resource utilisation, ultimately boosting customer confidence. Another valuable service is energy-optimised storage, achieved through integrating digital twins and temperaturebased best-before profiles. This integration ensures that products are consumed within their designated shelf life, leading to minimised cooling energy consumption and a more substantial commitment to sustainability. Automated routing for container collection takes advantage of location, fill level, and shelf life tracking. Leveraging machine learning algorithms and historical data, optimal routes can be determined by considering expiration dates and fill levels [13]. The benefits encompass improved truck capacity utilisation, reduced CO2 emissions, and a decreased need for IBC production. Smart IBCs play a vital role in optimised production planning by facilitating data exchange within the supply chain. This integration, coupled with machine learning-based demand forecasting algorithms, helps reduce overproduction and wastage caused by planning inaccuracies. The result is enhanced efficiency in production, timely customer fulfilment, and increased competitiveness. 2.4 Cost Structure The cost of the SmartCap, which makes the stainless-steel containers intelligent, comprises various items identified in cooperation with the solution provider. On the hardware side, there are costs for the individual components of the SmartCap, development costs to meet the particular requirements of the food industry and production costs. On the software side, there are development costs for the cloud application, as well as the costs for ongoing operation and maintenance. In addition, the cost structure includes components for license costs, marketing and sales, and customer support. 2.5 Revenue Stream For the business model of smart.CONSERVE to be economical, the costs incurred must be matched by more revenue generated. At the same time, it is crucial to know the amount customers are willing to pay for the added value generated. To this end, we conducted a benefit analysis with various potential solution users and supported the data obtained with further interviews. Based on this data, we numerically monetised the required revenue streams. These can be broken down into a one-time payment for the SmartCap as a physical product and its initial implementation. Besides this, there is a monthly subscription payment. The monthly fee includes various product attributes for the customer. On the one hand, this amount includes customer support, the maintenance of the interfaces to existing customer systems, and the essential services described in Sect. 2.3. In addition, the willingness to pay additionally for extensions such as demand forecasting or logistics optimisation was identified, especially among larger companies.

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3 Technical Requirements and Solutions The technical solution must enable the business model’s implementation and address the container’s technical requirements and the underlying process. Chosen technical challenges and solution approaches are outlined below. The overall technical solution follows in Sect. 3.2. 3.1 Technical Requirements To investigate the exact requirements for smart IBCs, relevant information was gathered from the literature, workshops, and interviews with food manufacturers and producers of IBCs. Based on this information and the business model, four critical technical challenges were identified. Data Transmission. The task of data sensor transfer to the cloud was divided into two areas. The data must be transferred (1) from the sensor package inside the container to the SmartCap mounted outside, and (2) then, this data must be sent from the SmartCap to the cloud. While a cellular network reliably transmits data to the cloud (2), signal shielding due to the steel container surface hinders the connection from the SmartCap to the sensors inside the container (1). Different technologies were tested for this connection. A wired transmission was ruled out due to the extent of container modification required. However, Bluetooth Low Energy (BLE) and Wi-Fi experiments were successful: the container body acted like an antenna. This enabled the solution’s modular design as the internal sensors could be separated from the external SmartCap. Because of the low energy requirements, BLE was ultimately chosen. Resistance. Requirements for the resistance of the solution result from the process and environmental influences. These include the pressure and temperature inside the container, possible mechanical effects, liquids, chemical reactions, and UV radiation outside the container. However, sterility criteria pose the most critical requirements: The sensor assembly’s interior must be exposed to a temperature of at least 100 °C for 300 s or more. Usually, a washing process, followed by sterilisation with superheated steam, ensures compliance with this food safety standard. The solution developed must therefore have three properties. Firstly, cleaning the components in the existing washing processes must be possible. Secondly, the sterilisation temperature must be reached inside, and thirdly, the function of the inside sensors must be ensured beyond the sterilisation. Mounting. The mounting of the components inside and outside the container should be done with as minor modifications as possible. However, glueing solutions already implemented with plastic IBCs, cannot be applied. Because stainless steel IBCs for the food industry are standardised, they all use the same manholes, which differ only in the way of attaching the lid. Therefore, a mounting solution independent of the container’s volume was sought. There are three attachment points on the outside of the container: the lid, the top, and the legs of the container, each with process-related collision risks to be avoided: for

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the legs and top with forklifts, and for the lid with pressing tools for bayonet fittings. In addition, the surfaces are curved, putting the brackets under tension during bonding. Lastly, the sensors on the inside of the lid must be removable for cleaning. Energy Supply. The battery of the internal sensor package cannot be replaced as there is no way to open it due to sterility requirements. The available space limits the battery capacity. The only way to optimise battery life is to reduce power consumption. This can be achieved using a modular solution, particularly by deactivating individual sensors or varying the measurement and transmission intervals.

Fig. 1. Effects of sensor usage and data transmission intervals on energy demand and service life

Six scenarios were tested. In the first scenario, the fill level sensor was shut down; in the second scenario, the combined pressure and temperature sensor. The third scenario serves as a reference with the use of all sensors. As in the previous scenarios, data is transmitted twice per day. The fourth and fifth scenarios are based on the scenario (3) and feature one data transmission per hour/ minute. Scenario (6) includes monitoring every minute only during the production process; otherwise, daily (cf. Fig. 1). In conclusion, the combination of low-frequency off-site monitoring and highfrequency in-production monitoring (6) was chosen as a standard configuration as it proved to be the most appropriate option for most users. If temperature and pressure measurements are not necessary, such as for certain non-perishable goods, the corresponding sensors can be turned off, leading to a six-month increase in the lifespan of the internal sensors.

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3.2 Solution The resulting solution consists of the sensor package inside the container and a SmartCap on its surface. The sensors inside measure fill level, pressure, temperature and acceleration. This data is transmitted via BLE to the SmartCap, which tracks the container’s location and measures the ambient temperature. The SmartCap sends all the data to the cloud using a cellular network (cf. Fig. 2).

Cellular

Wi-Fi, NFC, BLE

6

Cloud and peripheral devices

5

Data transfer

4

Smart Cap

3

Sensor data transfer through lid (BLE)

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1

Container body

Fig. 2. Structure including data transfer of the developed solution

All components are mounted on the lid for minimum modification effort and independence from container volume. Based on a mounting system already developed by container manufacturers for other applications, the sensor package can be clamped to the inside of the lid. This allows (dis)mounting with existing equipment. The SmartCap has to be mounted in a way that avoids collisions during transport and production. For precise positioning of the SmartCap, the curvature of the lid had to be accommodated by adding a straight auxiliary plate.

4 Retrofitting Concept The developed retrofitting solution allows the container modifications to be limited to the lid, avoiding legal issues connected to the food safety certifications of the containers. In addition to the SmartCap and the sensor package, three components must be attached to the lid: hooks for mounting the sensor package on the inside of the lid, an auxiliary metal plate that compensates for the curvature of the lid, and a mount for the SmartCap that is glued to this metal plate. For economical and sustainability reasons, existing lids are retrofitted. These have to be taken out of production and re-integrated once retrofitted.

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4.1 Retrofit Scenarios A minimum number of smart containers is required for the business model to be viable. Ideally, the entire IBC fleet of a food manufacturer should be retrofitted. Otherwise, the necessary handling of two different IBC types impacts process complexity. However, upgrading entire IBC fleets is economically challenging. Besides, food manufacturers are expected to test smart IBCs first for only a few of their products or their most important customers. Therefore, we determined quantity intervals for different use cases based on an ABC analysis of the order quantities of customers of a food manufacturer. The A category is assigned to those products and customers connected to 80%of the orders. Based on a cost-effect-analysis conducted, we suggest the implementation of our solution either for one test product and one pilot customer, for a few chosen key products or customers (e.g., the A products/customers from the analysis), for all A products and customers, or the entire fleet (cf. Table 1) [14]. Table 1. Volume intervals and time for retrofitting Number of retrofitted IBCs

< 1,000

Use case

Single product for Chosen customers Main customers or Entire fleet one customer and/ or products products

Internal retrofitting < 20 time (days)

2,000 - 5,000

60 - 100

8,000 - 12,000

160 - 240

30,000

600

Integrating the retrofit into the running operation presents challenges. To maintain process stability, we analysed typical procedures and defined the retrofitting process accordingly. Containers and lids are usually washed separately before refilling, with lids being washed faster than containers. To limit the disruption to production, only the resulting surplus lids can be used, restricting, in our case, retrofitting to 50 daily lids. Internal retrofitting is performed at the manufacturer’s container workshop alongside standard repairs and maintenance. The retrofitting process is simple: The surplus lids from the washing process are transported to the container workshop. There, the SmartCap mount auxiliary plates and the internal sensor package hooks are welded to the lid. To facilitate this, we developed a positioning device. After a short cooling phase (ca. 15 min), the plastic SmartCap mount can be glued to the auxiliary plate. Finally, the lids are returned to the final stage of the washing process. For process simplification, it is essential to order the hooks and auxiliary plates from a supplier. Then, the retrofitting process only requires a workbench with a welding station and buffers for the incoming or outgoing lids, hooks, auxiliary plates, and SmartCap mounts. The authors developed a layout for an operational area accommodating this process on 15 to 20 m2 , small enough for typical container workshops. Based on interviews and time studies conducted in the container workshop, we assume that the mentioned 50 lids per day could be handled after a training phase as long as a separate operational area dedicated to the retrofit is created. The internal retrofitting times indicated above are based on this assumption.

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However, under these circumstances, retrofitting the entire fleet of our food manufacturer would take nearly three years, necessitating a prolonged transition period with two container systems to be operated simultaneously. Cooperation with container manufacturers or other companies could accelerate the transition. However, in this case, the washing process has to be adapted to allow for more surplus lids. Manufacturing new lids designed for SmartCap integration is an alternative retrofit option, which, however, impacts sustainability and economic feasibility due to increased resource consumption and higher costs. Careful evaluation of the costs and benefits is necessary before choosing this option. 4.2 Process Modification The integration of new components requires changes in the washing process. This includes additional steps for disassembling the SmartCap and the internal sensor package, as well as washing the sensor package, especially its filters. Subsequent re-assembly is also more labour-intensive. In the case of a partial retrofit, the integration of two container types into the running process is critical. The challenge is to enable order-related washing and to ensure sufficient availability of each lid type (different closure mechanisms). Therefore, the washing process of smart containers must be considered in production planning, which is one of the ongoing research activities. To use the collected data, the data from the SmartCap must be integrated into the existing IT infrastructure. For this, the interfaces for the process data were identified for an exemplary food manufacturer. Currently, only a fraction of the process data is measured. When data from intelligent IBCs is added, process transparency can be increased, process quality improved, and existing production processes be simplified. The prerequisite is the conversion of significant quantities, as a certain amount of data is required to enable the mentioned benefits, especially ML and AI applications. Retrofitting may also be required in systems outside the container or the limited production system. Section 3 emphasises that the solution has been designed to avoid adaptations to other processes since installation on the lid minimises interference with other processes. However, this must be investigated for applications beyond the use case considered here. There are two logistical challenges to overcome. Firstly, the SmartCap requires a cellular connection for data transmission. Cellular gaps in the logistics chain must be closed, especially in warehouses, to enable seamless monitoring. This also includes helping with location tracking in shielded areas, such as refrigerated warehouses, by attaching Bluetooth beacons. Secondly, if the containers are used beyond local logistics, it may also be helpful to connect them to surrounding telematics systems, such as those found at ports, ships, or overseas containers. However, if modifications outside the container process are required, the business model’s viability must be reassessed.

5 Validation 5.1 Test Concept Strict regulations apply to food contact materials in the food industry. A prerequisite for food contact is certification by a notified body. For prototypes, certification is not practical. Therefore, implementing the developed solution, integrating the smart container

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into the process, and subsequent testing cannot be done using the developed prototype of a smart container. Instead, the functions of the container must be tested individually, ensuring that the individual tests cover the entire container deployment. For this purpose, we developed a test plan covering all functional dimensions of the container. These are the successful measurement, data transfer from the container inside to the outside to the cloud, availability of data to manufacturers and customers, reliable mounting of the SmartCap and sensors, any-fits-any functionality, resistance of the SmartCap and sensor to process and environmental conditions, compliance with and resistance to sterilisation conditions, and lack of process obstructions. Due to the abovementioned restrictions, testing cannot be done entirely based on the functional dimensions. The underlying process must also be considered: Cleaning, filling, shipping and handling at the customer site. For the last two process sections, certification would be required if the internal sensors should have contact with food. Therefore, tests could only be performed with water-filled containers. Due to the different viscosity compared to food, for example, fruit preparations, this is not considered valid. Therefore, the process integration, attachment and sterilisation must be tested in the cleaning process, where no food is involved. Attachment from the outside and data transmission can be tested without interfering with the process. As soon as the container leaves the factory premises, only the external SmartCap is attached to allow for resistance, localisation, data transmission, and availability testing. The measurements inside can be tested using returned goods, rejects or production defects – substances with all typical characteristics but no longer suitable as foodstuff. For example, a loss of container pressure or rising inside temperatures could cause these wastes. Due to the division according to the process sections, the overall functionality can be demonstrated despite food law restrictions, mainly since no actual food is used. All functional dimensions are covered by the test plan, through which the functionality of the overall solution can be concluded. Figure 3 shows the test plan and its aims: • Test of the sensors: Sterilisation occurs, and both the sensors and the SmartCap can withstand the process conditions. The sensors and SmartCap can be linked, and process integration is successful. • Process test: Data is available in the filling process and can be accessed via the cloud. Data can be shared in line with the services defined in the business model. • Test of the SmartCap: The SmartCap determines the location and transfers the data to the cloud. For this test, containers are shipped with a SmartCap and guided through the container process. 5.2 Results Some of the previously described tests have already been carried out successfully. These include data transfer, attachment, sterilisation and some resistance and application tests. The food-specific tests are described in more detail below: • Sterilisation: The interior of the sensor assembly must be heated above 100 °C for at least 300 s. During exposure to steam, prototypes were to measure the temperature of their core. The plastics of the sensor housing were varied. Sterilisation could be demonstrated for all similar sensor assemblies regardless of the materials used.

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Shipping & Storage

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Lid Disassembly Lid & SmartCap Washing

Sensor Washing

Lid Assembly Container Assembly & Coupling Sterilisation

Filling

Pressure & Weight Test

Storage

Outgoing Inspection Shipping

Fig. 3. Test plan along the existing process

• Data transmission from inside the container to the SmartCap on the top of the container: The temperature data was transmitted via Bluetooth. The transmission was automatically paused above 85 °C to protect the components. After cooling down, the connection was successfully re-established. The data measured in the meantime could be transmitted afterwards, proving successful sterilisation. • Temperature resistance: It was shown that the sensor package’s functionality could be guaranteed even after four times the necessary holding time for sterilisation.

6 Summary and Reflection Smart containers contribute to a more transparent food supply chain. In the research project smart.CONSERVE, a business model for smart services, a technical solution and a concept for upgrading existing container fleets by retrofit were developed.

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The developed business model is applicable to a wide range of industries with only minor adjustments required. However, in the course of the project, numerous interviews revealed that the food industry is a particularly price-sensitive industry. Costs incurred for tracking, transporting and storing containers cannot be passed on to customers via product costs. Savings of the food manufacturer itself must therefore cover them. Within the project’s scope, expert discussions could only estimate these savings. As margins are tight in the food industry, preventing the loss of entire container contents is particularly important. It is also essential to protect against the financial risk of product recalls. If the solution can be transferred to other industries with more valuable products, the economic viability may be more positive. In further research, the assumptions regarding the feasibility and associated savings through essential and extension services must be validated. Such analysis could also be extended to other industries to identify those where implementation costs are likely to be absorbed by direct savings. The developed technical solution, consisting of an inside sensor package and an outside SmartCap, considers the particular requirements of metal IBCs. The division into an internal sensor package and an external SmartCap avoids modifying the container. The main food-specific challenge is the contact of the sensors with the container contents. Therefore, transferring the developed solution to other industries is possible. Decisive would be whether the requirements change due to the container’s contents or environment. Changes would have to be correspondingly extensive. For example, in potentially explosive atmospheres, both the SmartCap and the sensors would have to be adapted; in this case, only the concept could be transferred. If merely the container’s content is relevant for the adaptations, the existing solution could easily be adapted, for instance, by using different sensors or a different housing material. Extending the sensor range is also possible, but continuous deeper analysis of the contents would be much more challenging. For example, to measure pH or conductivity, the sensors must be in contact with the product. This would require extensive further development, although the basic concept could be transferred. For the foreseeable future, however, the necessary sensor technology is likely to be multiple times more expensive than the current solution, especially for food, due to the required certifications. For the application to other industries, collaboration with container manufacturers could be helpful. Another benefit of such a collaboration would be avoiding retrofits in the long term by integrating industry-specific variants of the developed solution into the container manufacturing process. Due to the high lifespan of steel IBCs of up to 40 years, upgrading existing IBCs through retrofit would remain relevant even in this case. After the successful prototype development of the smart container, further action is necessary for industrial implementation. The prototype can be optimised regarding integration into food manufacturers’ processes, data generation and application. Due to our yet-to-be-certified prototype, contact with food was not allowed in the validation phase. For this reason, a test plan for validating the components of the technical solution individually was presented. This includes the process integration and the provision of the measured data. The extent to which this data is integrated into the existing infrastructure and how the services can be implemented cannot be conclusively validated. Further research on the design of industrialisation is needed. This applies to manufacturing or retrofitting smart-enabled containers realising the business model on

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an industrial scale. Scenarios for integrating intelligent containers at the customer’s site could also be examined in this context. Ultimately, the intelligent container developed in smart.CONSERVE offers excellent potential for the food industry by improving sustainability, transparency, and digitisation of the supply chain in the long term and across companies. The transfer of the concept to other sectors is also conceivable. However, further research is necessary for this transfer, the industry-specific quantification of economic benefits and the industrialisation of the developed prototype. Acknowledgements. The project smart.CONSERVE has been supported by funds from the Federal Ministry of Food and Agriculture (BMEL) based on a decision of the Parliament of the Federal Republic of Germany via the Federal Office for Agriculture and Food (BLE) under the innovation support program (FKZ 281 A511A19).

References 1. FAO: Global food losses and food waste – Extent, causes and prevention. https://www.fao. org/3/i2697e/i2697e.pdf. Accessed 13 Apr 2023 2. Turi, A., Goncalves, G., Mocan, M.: Challenges and competitiveness indicators for the sustainable development of the supply chain in food industry. Procedia. Soc. Behav. Sci. 124, 133–141 (2014) 3. Bastian, J., Zentes, J.: Supply chain transparency as a key prerequisite for sustainable agri-food supply chain management. Int. Rev. Retail Distrib. Cons. Res. 23(5), 553–570 (2013) 4. Cheng, Y.-H., Hai-Wei, L., Chen, Y.-S.: Implementation of a back-propagation neural network for demand forecasting in a supply chain - a practical case study. In: IEEE International Conference on Service Operations and Logistics, and Informatics, pp. 1036–1041. IEEE (2006) 5. Bell, J.: What is machine learning? In: Carta, S. (ed.) Machine Learning and the City, pp. 207– 216. Wiley (2022) 6. Thorben, J.: Artificial intelligence. Use cases for industry. https://www.messe.de/apollo/han nover_messe_2020/obs/Binary/A1021863/Whitepaper%20Use%20Cases%20for%20Indu stry.pdf 7. Wognum, P.M., Bremmers, H., Trienekens, J.H., van der Vorst, J.G., Bloemhof, J.M.: Systems for sustainability and transparency of food supply chains – Current status and challenges. Adv. Eng. Inform. 25(1), 65–76 (2011) 8. Burggräf, P., Steinberg, F., Adlon, T., Nettesheim, P., Kahmann, H., Wu, L.: Smart containers. Enabler for more sustainability in food industries? In: Liewald, M. et al. (eds.) Production at the Leading Edge of Technology, pp. 416–426. Springer International Publishing, Cham (2023). https://doi.org/10.1007/978-3-031-18318-8_43 9. Biganzoli, L., Rigamonti, L., Grosso, M.: Intermediate bulk containers re-use in the circular economy: an LCA evaluation. Procedia CIRP 69, 827–832 (2018) 10. Arbeitskreis Smart Service Welt / acatech (ed.): Smart Service Welt – Umsetzungsempfehlungen für das Zukunftsprojekt Internetbasierte Dienste für die Wirtschaft. Abschlussbericht. Berlin (2015) 11. Osterwalder, A., Pigneur, Y.: Business model generation. A handbook for visionaries, game changers, and challengers. Wiley&Sons, New York (2013)

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12. Bakalis, S., Gerogiorgis, D., Argyropoulos, D., Emmanoulidis, C.: Food industry 4.0: opportunities for a digital future food engineering innovations across the food supply chain, pp. 357–368. Elsevier (2022) 13. Wang, Y., et al.: Collaborative multi-depot logistics network design with time window assignment. Expert Syst. Appl. 140(112910) (2020) 14. Burggräf, P., Steinberg, F., Adlon, T., Nettesheim, P., Salzwedel, J.: Bridging data gaps in the food industry. Sensor-equipped metal food containers as an enabler for sustainability. In: Herberger, D., Hübner, M., Stich, V. (eds.) Proceedings of the Conference on Production Systems and Logistics: CPSL 2023 - 1, pp. 687–697. publish-Ing., Hannover (2023)

Produce It Sustainably: Life Cycle Assessment of a Biomanufacturing Process Through the Ontology Lens Ana Nikolov(B) , Milos Drobnjakovic, and Boonserm Kulvatunyou National Institute of Standards and Technology, Gaithersburg, MD, U.S.A. [email protected]

Abstract. An ontological perspective to addressing sustainability issues in biomanufacturing on the journey to the circular economy is represented in this paper. Even though sustainability goes beyond the environmental dimension from the triple bottom line framework (including environmental, economic, and social dimensions), given the importance of the environmental dimension, this study has prioritized an ontology for LCA to account for sustainability. LCA is widely used for estimating the environmental burden of a given production process. However, several conventions currently exist for representing the data required for LCA. The inconsistencies when utilizing LCA data from multiple sources make human readability and understanding difficult, and subsequent data analysis protracted and inefficient. Therefore, we propose the alignment of terms and definitions provided by Industrial Ontologies Foundry (IOF) Core with LCA terminology from ISO 14040 guidelines. With a particular focus on CO2 emission assessment, we demonstrated how the Life Cycle Assessment (LCA) ontological term formalization could be applied to a biomanufacturing process use case. The ontological representation of LCA allows easier comparative analysis between changes in a manufacturing arrangement and the corresponding LCA result. The ontology-assisted comparative analysis could potentially accelerate the identification of a manufacturing process with the lowest carbon footprint and reveal the key contributors to carbon emissions. Keywords: sustainability · biomanufacturing · life cycle assessment · ontology · circular economy

1 Introduction The age of the Anthropocene is marked by humankind’s enormous influence on Earth’s climate and ecosystems, achieved through various technologies employed to serve human existence and well-being [1]. A myriad of challenges associated with preserving nature’s treasures and life as we know it have emerged as an ever-growing consequence of these events. To minimize misconduct, the focus is shifting towards taking actions to reduce encountered problems, such as limited natural resource availability, environmental pollution, reduced biodiversity, greenhouse effect, and irreversible climate change. © IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 504–517, 2023. https://doi.org/10.1007/978-3-031-43688-8_35

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However, maintaining everyday life comes with many necessities, most of which are plagued with profounding existing problems. One of the necessities certainly is product generation and demand. For instance, the carbon footprint left behind as the byproduct of many production processes comes with non-biodegradable waste generation and accumulation, which influences ecosystems, especially regarding biodiversity [2]. The emerging area of bioeconomy has a high potential to solve many of the aforementioned problems. Namely, the bioeconomy encompasses various technologies, and biomanufacturing processes (processes that utilize cells, organisms, or biomolecules to produce products or intermediaries), oriented towards minimizing new material utilization and promoting the reuse and recycling of waste materials [3]. As such, the bioeconomy, with its supporting sciences, represents a form of constant work toward sustainability and nature’s preservation. However, the resources are extensively used based on the linear economy model, which involves a take-make-dispose mechanism. At the same time, large quantities of non-reusable and often non-degradable waste are generated and discarded. These events suggest that the linear model is focused on profitability with little interest in creating a sustainable product life cycle. Contrastingly, the essence of the circular model is sustainability through the promotion of repeated use of materials with improved life cycles, upcycling, and recycling, thereby promoting the idea of a restorative and regenerative economy [4]. While the bioeconomy brings tremendous advantages to achieving targeted sustainability, transitioning from a linear to a circular model is crucial to maximize its potential and make it competitive with traditional manufacturing. This transition involves promoting reduced use of new and non-renewable sources, carbon-negative production, and waste use and transformation [5, 6]. For example, by reducing the use of fuel feedstocks and their replacement with renewable sources, a significant amount of CO2 emissions would be diminished, and fewer non-biodegradable wastes would be created. It should be noted, however, that designing a sustainable circular bioeconomy manufacturing process is challenging. Even if the core of manufacturing design is to reduce the environmental burden, sometimes the attempts to reduce one problem leads to the rise of another (e.g., the transport of materials for recycling leaves a carbon footprint behind) [7]. Bio-based products usually have smaller carbon footprints compared to equivalent products originating from fossil-based materials. However, these bio-based products may have larger environmental footprint than fossil-based materials in terms of water, land use, and eutrophication, which suggests that these products are not necessarily more environment-friendly or sustainable than fossil-based products [8]. Hence, it is of great importance to consider both the technical and economic aspects along with the social aspects and potential environmental impact of a product through its entire lifecycle and its end-to-end supply and manufacturing chain. To adequately evaluate these different aspects and understand potential areas for improvement, having holistic access to data throughout the manufacturing enterprise and enabling its subsequent transformation to knowledge and insights becomes essential. For example, having a clear understanding of how different process configurations (e.g., process parameters, genetic modification of a cell line) and raw material sources impact the production cost, yield, and environmental impact categories facilitates a more sustainable process design [9]. However, currently utilizing data to gain such insights

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is typically laborious and, in many cases, must be tailored for each process or product. This current state stems from several challenges associated with biomanufacturing data such as 1) storage of data in many different formats that are not necessarily mutually coherent, 2) lack of the appropriate metadata in many sources, 3) paper-based records and 4) lack of explicit connections across different sources. Ontologies are a potential solution to alleviate the described data challenges. Namely, ontologies are logically defined information models (smart standards). By being logically defined, the ontologies permit rigorous term definitions that are human and computer understandable [10]. The rigorous definition permits an unambiguous representation and interpretation of a particular term, regardless of the initial data source. Also, using logic in ontologies enables the explicit representation of the connections between different terms. Finally, the different connections and the underlying logic permit a consistent presence and representation of the required metadata. However, to the authors’ best knowledge, a set of mutually compatible ontologies that enable economic and sustainability analysis across the entire manufacturing digital thread for biomanufacturing is absent. Therefore, the rest of the paper is organized as follows. First, current practices regarding economic and sustainability analysis in biomanufacturing are reviewed. Next, an overview of the current biomanufacturing and economic analysis ontological landscape is given. Afterward, a structure of an ontological solution based on the IOF Core ontology is explained, along with the rationale for choosing IOF Core. Finally, the application of the proposed solution on the lifecycle analysis of a biomanufacturing process is outlined.

2 Economic and Environmental Analysis of Biomanufacturing Processes and Products The utmost driver of bioeconomy is the advancement in biological sciences, especially synthetic biology, and metabolic engineering. These advancements, such as food product fermentation, recombinant DNA technology, genetic engineering, and many more, contributed to the improvements in many sectors, namely industrial materials, biofuels, chemicals, food, and pharmaceuticals [11]. Biotechnology carries pivotal importance in response to the mandatory use of renewable bioresources dictated by bioeconomy, as it enables process optimization in a manner that maximizes resource utilization. The current state of the art of biomanufacturing and other technologies which follow the circular bioeconomy path to more sustainable production includes a fair number of processes. The most prominent ones are using raw materials (including renewable sources, but also transformed waste used as starting material) [12], waste transformation [13], reuse and recycling, carbon-negative production, and biogas and bioenergy production [6]. When designing a process for the production stage, a preliminary economic analysis is necessary. Additionally, if the process is new and innovative, economic analysis is an effective method for evaluation along with case studies [14]. Furthermore, the economic aspect should be considered in order to achieve sustainability, in addition to the environmental aspect covered by LCA. In order to be a viable practice, biomanufacturing production processes that comply to circular bioeconomy principles should be designed and performed in manner that accounts both profitability and sustainability. To reach these aims, the integrated design of process unit operations is oriented toward output

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efficiency, and waste reduction which is partially guided by techno-economic assessment (TEA). For example, TEA can help decide if a production process should be run in a batch, continuous or semi-continuous mode [15]. In the early stages of process development, TEA is employed along with life cycle assessment (LCA) to evaluate feasibility, performance, and influence on the environment. TEA is used to determine the optimal combination of technologies for the best economic performance. It is not always the case that the most suitable technology is the best available technology for process design and economic performance [16]. TEA is crucial in analyzing a process or product’s technical and economic performance and understanding the cost standards and potential economic feasibility. Nevertheless, the environmental impacts cannot be determined by using TEA. Instead, this evaluation is performed using the life cycle assessment (LCA). In LCA, the goal is to follow the product through the manufacturing process and through the use and final disposal to analyze the environmental burden at all product life cycle stages [17]. TEA and LCA represent the fundamental methods for sustainable process development. However, these methodologies heavily rely on having access to data across the entire manufacturing digital thread, along with the relevant economic and environmental factors (e.g., carbon factor). Utilizing ontologies as a base for data and knowledge representation could enable a holistic access to the relevant knowledge and information across life cycle stages. Therefore, the following section describes the current ontological landscape and the primary problem that ontologizing biomanufacturing and relevant economic factors can solve.

3 Review of Current Ontological Literature It was previously stated by Drobnjakovic et al. [9] that the major problem regarding data utilization in biomanufacturing is the lack of connectivity between data. This problem involves terms and their transformation into knowledge, particularly the interconnection of data associated with and obtained from techno-economic and sustainability analysis (e.g., cost, performance, sustainability, production metrics) [9]. The connections between process configurations, such as process parameters and economic analysis metrics, would permit more efficient data utilization for bioprocess design and optimization. Even though ontologies carry great potential to solve the majority of data challenges, the available literature on ontologies employed for bioindustrial manufacturing processes and the corresponding economic analysis is limited. Ontologies related to bioindustrial manufacturing currently only cover a very narrow part of the entire bioindustrial domain, as could be seen in examples of applicationspecific ontologies developed for biocatalysis process development and its scale-up. Some ontologies developed for the necessities of biomedical research also have certain overlaps with biomanufacturing. For example, the Allotrope Ontology [18] could be used as a basis for annotation and representation of laboratory analysis and experiments, and the Cell Line Ontology [19] could be used as a basis for annotation and representation of cell lines in biomanufacturing. There are also industrial ontologies that are manufacturing area agnostic and can therefore be used as a basis for biomanufacturing specific terms. Examplary ontologies

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of this type can be found within the Industrial Ontologies Foundry (IOF) ontological suite. The Industrial Ontologies Foundry was formed with the objective of creating a suite of interoperable ontologies that can serve as a foundation for data and information interoperability across all areas of manufacturing. For instance, the IOF Core ontology can serve as a basis for an ontology in a specific sector or operational area within the manufacturing domain ontologies (e.g., Supply Chain operational area and Maintenance operational area) [20]. The IOF Supply Chain Reference Ontology is an extension of the IOF Core to include terms supporting supply chain operation. It is industry sector agnostic and can hence be used as a basis for specific supply chain requirements in biomanufacturing sector [21]. All these examples suggest that 1) while there are specific bioindustrial manufacturing ontologies such as the biocatalysis ontology, there is a lack of general ones that can be a basis for other products and production methods, and that 2) such an ontology does not have to be constructed from scratch but can be developed by reusing concepts and aligning with the existing ontologies from the industrial and biomedical domain. For a more detailed review of the current state of biomanufacturing ontologies, see Drobnjakovic et al. [9]. The literature is also scarce regarding the current state-of-the-art ontologies tied to the circular economy. To our knowledge, not many publicly available ontologies describing the bioeconomy domain could be found, apart from key terms lists and glossaries. However, some examples exist, such as a publication by Pacheco-López et al. [22], in which an ontology that models activities and associated manufacturing processes to assess waste-to-resource routes economic effectiveness in chemical manufacturing, was created. A publication by Bicchielli et al. [23] shows an ontology that models terms associated with sustainability and bioeconomy. The BiOnto ontology was created with the specific purpose of accessing the knowledge within the BIOVOICES social platform, designed to connect social platform content with stakeholders [23]. The ontological formalization of the term bioeconomy was created, as well as the constructs surrounding sustainability. The term bioeconomy was then linked to constructs surrounding sustainability. Additionally, there are several ontologies that model concepts relevant to LCA [24, 25]. However, all of them are stand-alone ontologies, meaning their interoperability with ontologies that model specific manufacturing and supply chain aspects might be limited. Therefore, a gap exists in modeling the connections between economic analysis metrics and manufacturing domain-specific ontologies that would ensure their mutual compatibility and interoperability. These explicit connections and the appropriate term representation would ensure a holistic and exhaustive view of the data and knowledge across the manufacturing digital thread and subsequent product utilization, including all the corresponding development studies and phases. The next section will give an overview of how an already existing ontology – the IOF Core can be used as a basis for further biomanufacturing ontology development in the context of circular economy assessment – namely LCA.

4 Creating an IOF-Compliant Ontology for Life Cycle Assessment In Sect. 2, we have described two economic analyses and assessment methodologies: TEA and LCA. We have chosen LCA and its associated terms to be the first ontologically formalized methodology. To address the question of our choice of LCA, we must

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recall some of the global problems stated in the introduction of this work, such as irreversible changes in climate and ecosystems. Conveniently, LCA is used to evaluate the environmental impacts of a given production process. Following the product’s pathway through the production process to the disposal, the environmental burden imposed by this production process is analyzed at all stages. With LCA results, a process could be optimized to the point of fulfilling both efficiency and sustainability while minimizing the negative impact on the environment. There is currently, however, a number of conventions for representing and storing the data required for LCA. The consequence of many existing conventions using different nomenclature, categorization, use, and storage of elementary flows in LCA data is the inconsistency in implementing elementary flows for life cycle inventory (LCI) and life cycle impact assessment (LCIA) data from multiple sources. The inconsistent implementation makes human readability and understanding difficult, making subsequent data analysis protracted and inefficient. To tackle the inconsistency challenge, we have taken into account the terms and definitions provided by IOF Core to assess and formalize the terminology used in LCA. The primary purpose of utilizing IOF Core is to enable explicit connections between the environmental impact metrics and the production process through the use of ontology. The IOF Core ontology is specifically chosen for the basis of ontological development because it readily contains many critical constructs for manufacturing relevant data and knowledge representation (e.g., product production process, process plan, and manufacturing process). Also, the IOF Core is a mid-level manufacturing sector agnostic ontology, thereby ensuring broad-term applicability. The IOF Core has already demonstrated its value by ensuring a consistent basis for domains such as Supply Chain and Maintenance [20]. It is also the ontology of choice for NIIMBL Biopharmaceutical manufacturing process ontology. Finally, the IOF Core is based on Basic Formal Ontology (BFO) [26], the top-level ontology choice of the OBO Foundry and Allotrope [9, 18]. Hence, by aligning with IOF Core, the LCA ontology development is likewise aligned with BFO. This ensures higher compatibility and, thus, term reuse from the most prominent biomedical ontologies. For the source of LCA terms and definition the ISO 14040 guidelines were selected. ISO 14040 is a widely utilized standard series that has a comprehensive set of terms covering all the LCA terminologies required for representation of the LCA (Table 1). The terms from Table 1 were analyzed and transformed accordingly into ontological classes and aligned with the existing IOF/BFO framework (Fig. 1). The Life Cycle Inventory Analysis, LCIA, and LCA are mapped as Planned Processes in IOF Core Ontology. The reason is that all of their definitions include different actions (e.g., evaluation and compilation) that are performed in a structured manner with a clearly specified objective (in other words, according to some plans). Two additional classes were introduced to reflect that LCA needs a clearly defined objective and a corresponding plan: LCA Objective Specification and LCA Plan Specification, respectively. The purpose of introducing these classes that do not directly correspond to the ISO standard is to represent the scope and methodology of each specific LCA consistently and explicitly.

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LCA Terms

ISO 14040

Life Cycle Assessment

compilation and evaluation of the inputs, outputs and the potential environmental impacts of a product system throughout its life cycle

Life Cycle Inventory Analysis phase of life cycle assessment involving the compilation and quantification of inputs and outputs for a product throughout its life cycle Life Cycle Impact Assessment phase of life cycle assessment aimed at understanding and evaluating the magnitude and significance of the potential environmental impacts for a product system throughout the life cycle of the product Elementary Flow

material or energy entering the system being studied that has been drawn from the environment without previous human transformation, or material or energy leaving the system being studied that is released into the environment without subsequent human transformation

Energy Flow

input to or output from a unit process or product system, quantified in energy units

Intermediate Flow

product, material or energy flow occurring between unit processes of the product system being studied

Product Flow

products entering from or leaving to another product system

Reference Flow

measure of the outputs from processes in a given product system required to fulfil the function expressed by the functional unit

Life Cycle Inventory Result

outcome of a life cycle inventory analysis that catalogues the flows crossing the system boundary and provides the starting point for life cycle impact assessment

Impact Category

class representing environmental issues of concern to which life cycle inventory analysis results may be assigned

Impact Category Indicator

quantifiable representation of an impact category

The class flow role was introduced as a basis for the different flows specified in the ISO standard. From it, the more specialized flows from the standard can be created. The flow role permits identifying inputs and outputs of interest within the context of a specific LCA analysis. Finally, Life Cycle Inventory Result and Impact Indicator were introduced as a subclass of Information Content Entity. The distinction between impact category and impact indicator has not been introduced as subclassing of impact indicator within the ontological hierarchy is sufficient to represent its impact category. In addition to placing the terms within the IOF hierarchy, their interconnections were determined and represented through IOF/BFO relational expressions (Fig. 2). The relational expressions predominantly aim to capture the flow of information within the

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Fig. 1. Introducing selected LCA term into the IOF/BFO hierarchy

LCA and the operational precedence within it. Per the definition, LCIA and LCI are phases of the LCA, which is captured through the ‘has part’ relationship. The information obtained from the LCI (the compiled inventory) is used for calculations in the LCIA that results in one or more impact indicators. This is captured through the inputs and outputs, along with precedes between the two. The output is the flow role (and its subproperties) assigned to various inputs and outputs of the processes within the LCA scope. The relationship between the LCA plan and LCA is “prescribes,” representing that the plan serves as a guide for conducting the LCA. Finally, the objective is represented as part of the plan, and its potential achievement is linked to the LCA process.

Fig. 2. Connection of the LCA terms through the IOF/BFO relational expressions

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5 Applying LCA Extension of IOF Core to a Biomanufacturing Process In Fig. 3, a representation of a part of a biomanufacturing process with a fed-batch bioreactor is shown and mapped onto BFO and IOF Core Ontology. A typical biomanufacturing process is composed of upstream and downstream processes, each has different unit operations, such as fed-batch production unit operation and filtration unit operation. Furthermore, these unit operations have inputs, outputs, and participants relevant to LCA. For example, our figure depicts inputs and outputs for fed-batch unit operation. Inputs include substrate, electric power supply, and nutrients. Outputs include production intermediary and wastewater. The production intermediary also represents the input for filtration unit operation, along with the buffer and filtration membrane. The IOF Core permits further precise classification of inputs, outputs, and participants. For example, the bioreactor and filtration system are mapped as pieces of equipment; and nutrients, substrate, and filtration membrane are mapped as raw materials. Hence, the figure demonstrates that the IOF Core constructs can be extended to the biomanufacturing domain to capture relevant LCA manufacturing information (e.g., inputs, outputs, and participants).

Fig. 3. Extension of IOF Core to represent fed-batch biomanufacturing.

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Figure 4 demonstrates a use case for LCA conducted to determine the environmental impact of a manufacturing process, specifically the amount of generated CO2 during the manufacturing processes represented in Fig. 3. At the early design stages of a manufacturing process, it is crucial to calculate the embodied carbon, which stands for carbon dioxide equivalent and encompasses all greenhouse gases. The embodied carbon is calculated by multiplying the quantity of each material and product by a carbon factor [27]. Before calculating embodied carbon, the relevant quantity information must be retrieved from the manufacturing data of interest and represented in the form necessary for further analysis. This is captured by the ‘has input’ relational expression between the quantity values (value expressions in the IOF Core ontology) of the inputs and outputs of interest from the manufacturing processes (identified through the Flow Role) and the Life Cycle Inventory Analysis that ‘has output’ the LCI Result.

Fig. 4. Representation of LCA for CO2 emission assessment in the case of the manufacturing process given in Fig. 3.

Next, these amounts are utilized with corresponding carbon factors to estimate the CO2 emissions, which are then aggregated in the total amount. This information is captured through the CO2 emission assessment process and its respective inputs and outputs.

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Consequently, the ontological representation of LCA and the biomanufacturing process permits the establishment of evident connections between the LCA findings and the associated manufacturing process. These clear connections can then potentially enable a more straightforward comparative analysis between changes in a manufacturing configuration and the corresponding LCA result. For instance, a user could query the ontology to identify within a set of manufacturing processes the one with the lowest carbon footprint. Also, the user can identify and trace the origin of the key process factors (e.g., raw materials or particular process settings) that contribute to carbon emission. Finally, it should be noted that the approach given here is not necessarily exclusive to just the manufacturing process. A similar analysis could be extended to all the supply chain operations, subsequent product utilization, recycling, and waste treatment. As an example, it is possible to construct, compare and analyze different recycling routes of a particular process input or output and determine their impact within the context of the entire production process. Thus, holistic insight can be gained throughout the product lifecycle, guiding decision-making to identify the most sustainable routes for the product.

6 Conclusions and Future Perspectives The importance of understanding the critical parameters for process improvements and different process configurations, access to data throughout the product production process, followed by its transformation to knowledge and insights, is paramount. The alignment of LCA with IOF Core ontology and the subsequent extension of IOF Core with biomanufacturing notions might contribute to this understanding in several ways: 1) The majority of data-related problems mentioned previously in this paper, such as data incoherency and representation in different formats and the lack of connections between data sources identified for the biomanufacturing sector, could be circumvented using ontology. Ontology provides logical, rigorously defined terms understandable both by humans and the computer. The rigorous definition permits unambiguous representation of connections between terms and enables consistent interpretation. 2) The ontological representation and alignment between manufacturing processes and economic analyses enable better comparison between the production process phases and provide the user with answers to questions about the parameters that should be modified and optimized. Ontologizing these concepts also assists in identifying processes and materials being the most responsible for the environmental impact in the case we showed but could also be tailored to fit other process characteristics, such as economic metrics and economic feasibility. 3) Regarding sensitivity analysis (essential for the interpretation of LCA results), ontology alignment can benefit in evaluating the LCA output. Namely, the proposed ontological framework enables (1) data interpretation consistency through the strict ontological structure, (2) permits traceability of different LCA data sources through explicit connections (e.g., participation, input, output) and (3) permits a holistic insight into all the processes associated with a particular LCA objective.

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4) The ontological standardization of relevant constructs using ISO standards and, therein, BFO, contributes to universal understanding, compatibility, and applicability. Additional applicability and coherency are ensured by using IOF Core as the mid-level ontology and adhering to hub-and-spokes principles. Our initial work was a proof-of-concept principle that demonstrated the feasibility of IOF Core in the context of the biomanufacturing process. In future work, this approach will be extended to supply chain, subsequent product utilization, recycling, and waste treatment. Other impact categories could also be assessed, such as eutrophication, acidification of soil and water, depletion of resources, and more. We have chosen LCA as the first assessment to be formalized within the IOF Framework. However, insights into a given production process’s economic feasibility and cost standards (particularly product cost during its entire lifecycle) are paramount to achieving maximal process competitiveness and sustainability. Aiming to complete the whole techno-economic and environmental impact image of a biomanufacturing process through the ontological lens, our following steps will be to formalize concepts associated with TEA and Life Cycle Cost. Future work will also explore how ontology-based LCA systems could potentially be better integrated with computer-aided design (CAD) programs (e.g. SolidWorks Sustainability CAD plugin) as well as multiphysics simulation programs to support the design of sustainable products and manufacturing processes. Disclaimer. Certain commercial software products are identified in this paper. These products were used only for demonstration purposes. Their use does not imply approval or endorsement by NIST, nor does it imply these products are necessarily the best available for the purpose.

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8. Spierling, S., et al.: Bio-based plastics - a review of environmental, social and economic impact assessments. J. Clean. Prod. 185, 476–491 (2018). https://doi.org/10.1016/j.jclepro. 2018.03.014 9. Drobnjakovic, M., Boonserm K., Frechette, S., Srinivasan, V.: Recent developments in ontology standards and their applicability to biomanufacturing. In: IDETC/CIE2023-116866, Proceedings of the ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 20–23 August 2023, Boston, MA, USA (2023). (Under review) 10. Karray, M., et al.: The industrial ontologies foundry (IOF) perspectives (2021) 11. Kardung, M., et al.: Development of the circular bioeconomy: drivers and indicators. Sustainability 13(1), 413 (2021). https://doi.org/10.3390/su13010413 12. da Ferreira, R., Azzoni, A.R., Freitas, S.: Techno-economic analysis of the industrial production of a low-cost enzyme using E. coli: the case of recombinant β-glucosidase. Biotechnol. Biofuels 11(1), 1–13 (2018). https://doi.org/10.1186/s13068-018-1077-0 13. Dahiya, D., Sharma, H., Rai, A.K., Nigam, P.S.: Application of biological systems and processes employing microbes and algae to reduce, recycle, reuse (3Rs) for the sustainability of circular bioeconomy. AIMS Microbiol. 8(1), 83–102 (2022). https://doi.org/10.3934/microb iol.2022008 14. Yang, O., Qadan, M., Ierapetritou, M.: Economic analysis of batch and continuous biopharmaceutical antibody production: a review. J. Pharm. Innov. 15(1), 182–200 (2019). https:// doi.org/10.1007/s12247-018-09370-4 15. Fu, R., Kang, L., Zhang, C., Fei, Q.: Application and progress of techno-economic analysis and life cycle assessment in biomanufacturing of fuels and chemicals. Green Chem. Eng. (2022). https://doi.org/10.1016/j.gce.2022.09.002 16. Murthy, G.S.: Techno-economic assessment. In: Biomass, Biofuels, Biochemicals, pp. 17–32 (2022). https://doi.org/10.1016/b978-0-12-819242-9.00019-1 17. Mahmud, R., Moni, S.M., High, K., Carbajales-Dale, M.: Integration of techno-economic analysis and life cycle assessment for sustainable process design – a review. J. Clean. Prod. 317, 128247 (2021). https://doi.org/10.1016/j.jclepro.2021.128247 18. Millecam, T., Jarrett, A., Young, N., Vanderwall, D., Della Corte, D.: Coming of age of allotrope: proceedings from the fall 2020 allotrope connect. Drug Discov. Today 26(8), 1922– 1928 (2021) 19. Sarntivijai, S., et al.: CLO: the cell line ontology. J. Biomed. Semant. 5(1), 1–10 (2014) 20. Drobnjakovic, M., Boonserm, K., Ameri, F., Will, C., Smith, B., Jones, A.: The industrial ontologies foundry (IOF) core ontology. In: Proceedings of the FOMI 2022: 12th International Workshop on Formal Ontologies meet Industry, Tarbes, France (2022) 21. Kulvatunyou, B., Ameri, F.: A supply chain reference ontology based on basic formal ontology. In: ASME 2019 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, IDETC/CIE 2019 (2019) 22. Pacheco-López, A., Somoza-Tornos, A., Graells, M., Espuña, A.: Synthesis and assessment of waste-to-resource routes for circular economy. Comput. Chem. Eng. 153, 107439 (2021). https://doi.org/10.1016/j.compchemeng.2021.107439 23. Bicchielli, C., Biancone, N., Ferri, F., Grifoni, P.: Bionto: an ontology for sustainable bioeconomy and bioproducts. Sustainability 13(8), 4265 (2021). https://doi.org/10.3390/su1308 4265 24. Ghose, A., Lissandrini, M., Hansen, E.R., Weidema, B.P.: A core ontology for modeling life cycle sustainability assessment on the semantic web. J. Ind. Ecol. 26(3), 731–747 (2021). https://doi.org/10.1111/jiec.13220 25. Janowicz, K., et al.: A minimal ontology pattern for life cycle assessment data. In: CEUR Workshop Proceedings, vol. 1461 (2015)

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Battery Production Development and Management

Battery Production Systems: State of the Art and Future Developments Mélanie Despeisse(B) , Björn Johansson, Jon Bokrantz, Greta Braun, Arpita Chari, Xiaoxia Chen, Qi Fang, Clarissa A. González Chávez, Anders Skoogh, Johan Stahre, Ninan Theradapuzha Mathew, Ebru Turanoglu Bekar, Hao Wang, and Roland Örtengren Division of Production Systems, Chalmers University of Technology, Gothenburg, Sweden {melanie.despeisse,bjorn.johansson,jon.bokrantz,greta.braun, arpitac,xiaoxia.chen,qifa,clarissa.gonzalez,anders.skoogh, johan.stahre,ninant,ebrut,haowang,roland.ortengren}@chalmers.se

Abstract. This paper discusses the state of the art in battery production research, focusing on high-importance topics to address industrial needs and sustainability goals in this rapidly growing field. We first present current research around three themes: human-centred production, smart production management, and sustainable manufacturing value chains. For each theme, key subtopics are explored to potentially transform battery value chains and shift to more sustainable production models. Such systemic transformations are supported by technological advances to enable superior manufacturing performance through: skills and competence development, improved production ergonomics and human factors, automation and human-robot collaboration, smart production planning and control, smart maintenance, data-driven solutions for production quality and its impact on battery performance (operational efficiency and durability), circular battery systems supported by service-based business models, more integrated and digitalized value chains, and increased industrial resilience. Each subtopic is discussed to suggest directions for further research to realise the full potential of digitalization for sustainable battery production. Keywords: Battery production · Digitalization · Industry 5.0 · Electrification · Human centricity · Sustainable value chain management

1 Introduction The electrification of society will significantly alter the industrial landscape, most notably in the automotive industry as the transport sector contributed to 24% of direct CO2 emissions in 2020 [1]. Asian battery manufacturers (China, South Korea, Japan) are currently dominating world market, but this is rapidly changing as the demand for batteries is increasing in Europe and the USA [2]. European battery production capacity is planned to grow in the coming decade, with Sweden positioned to be a globally leading battery © IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 521–535, 2023. https://doi.org/10.1007/978-3-031-43688-8_36

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manufacturing country, making the battery sector one of Sweden’s largest export industry. In addition to the urgent need for competences, this rapid industrial change also calls for sustainable battery production, resilient value chains, and an industrial ecosystem from raw material to finished product. At the European level, the European Battery Alliance (EBA250) was launched in 2017 to ensure safer traffic, cleaner vehicles, and more sustainable technological solutions achieved by creating competitive and sustainable battery cell manufacturing. The Alliance for Batteries Technology, Training and Skills (ALBATTS) is another project on green mobility project focusing on future skills, competences and training schemes, including enterprises along the battery value chain. More recently, the European Commission proposed the Net Zero Industry Act [3] setting high manufacturing capacity goals in terms of batteries, wind power, solar panels and other clean technologies; e.g., close to 90% of batteries used in Europe should be manufactured in the region by 2030. In Sweden, battery factories will require support and cooperation between government, academia, research institutes and companies. The Swedish Electric Transport Laboratory (SEEL) was formed in 2018 as a test centre for electromobility research and development within EBA250 and will establish three facilities to be operational in 2023. The aim is to consolidate efficient knowledge development and improve the conditions for collaboration in the field of electrified transport in Sweden and Europe. There are numerous initiatives at a company level as well. For example, Northvolt decided to build a LIB factory in Skellefteå which will be the largest battery plant in Europe. The company predicts that they will recruit up to 4 000 people in the next years, rising to a total of 10 000 in their plants in Sweden, making Sweden a world leader in the field of battery production. Another example is ABB’s development of production and control systems for battery manufacturing [4] for applications in Northvolt plants. To stimulate research on the development of sustainable and cost-efficient battery manufacturing, the authors propose directions for further work based on production research state of the art. While this paper does not present a complete literature review or in-depth scientific analysis, we explore and present our views on nine subtopics under three overlapping themes related to Industry 5.0 [5]: human-centred production, smart production management, and sustainable manufacturing value chains.

2 Human-Centred Production 2.1 Skill Gaps and Competence Development Driving battery production development forward, a skilled workforce is key. Battery production combines work tasks ranging from process industry (e.g., printing press, cleanroom production) to traditional assembly [6]. This leads to lower demands for traditional production operators and higher demands for specialized process operators, including requirements for safety-related skills. Current skills shortages are a challenge which can be broken down into two parallel problems: 1) there are not enough skilled workers to meet current demands for battery production; 2) demographics of many regions (like Europe, the US and China) show aging trends [7]. The number of people who could be attracted to battery industries is low and industry needs to compete with other sectors to get the existing pool of talents on board, which may be resolved by reskilling or

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upskilling of the available workforce [8]. Aging workforces increase pressure on regions like Europe, to provide both regular education related to battery production as well as up-skilling. Given that the professions are still undefined, new methods are needed to identify missing knowledge and to measure skill gaps on an individual level, as well as the subsequent tools to guide people in training selection. To find a suitable learning path for an individual, it is essential to set goals and measure their skill gap based on their educational background and previous work. This can be done using data analytics of joband skills-related databases, such as the European Skills, Competences, and Occupations (ESCO), or of job postings on professional networks. One way to bridge the skill gap is to recommend a suitable learning pathway to the individuals that could potentially become the workforce, combining different learning items in an online environment [9]. On-the-job learning for existing workforces working in related job roles is needed, besides recruiting new talent. As adult learners have varying backgrounds, jobs, and time schedules, flexibility is crucial and online learning environments are helpful. The challenges lie within the dimensions feedback from teachers or the platform, study approach of the learner, organisation and structure of the platform, relevance of the content and support from teachers, mental wellbeing of the learner, and the applicability in working life [10]. A potential solution to upskill employees is by using simulation and virtual reality tools which have the advantage that training can start before any physical hardware has been set up [11]. Substantial regional, national, and local efforts are being made to upskill existing industrial workforces for the new demands [10]. One example is the national Swedish upskilling programme Ingenjör4.0, providing industrial digitalisation skills to industry employees in modular formats suitable for work-integrated upskilling. On a European level, several initiatives address up- and reskilling of potential workers. The EIT Deep Tech Talent Initiative aims at upskilling 1 million people in deep tech. In The Automotive Skills Alliance over 100 partners are collaborating for battery manufacturing upskilling on a European level, offering several online courses. 2.2 Ergonomics and Human Factors As mentioned in Subsect. 2.1, the battery industry represents a new type of manufacturing dealing with process management and control of flowing chemicals (flow production) with several discrete operations (discrete manufacturing) as battery cells are combined into battery packs before being placed in battery-powered devices and vehicles [6]. While processes are increasingly automated, (see Sect. 2.3), many tasks are still performed by humans. Quality checks, end-of-line testing, etc. is carried out to ensure battery performance, as discussed in Sect. 3.3. In the end-of-line-testing, test devices are connected to specific cables and lines to check entire electronics and voltages. Testing is completed by a visual, manual check. Many tasks require manual handling, i.e. picking, lifting, and carrying [12], similar to physical traditional assembly. An ergonomics analysis can be performed to check that the physical loading is kept below certain threshold values, ensuring safe work conditions. Physical loadings concern issues caused by strain on the body due to strenuous postures, exertion of high forces and fatigue and depend on magnitude, duration and frequency. The ergonomics literature suggests such thresholds as well

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as methods for analysing the physical loading based on measurements or observations while work is carried out by an operator, or based on computer manikin simulations of the planned work [13]. As a result, a load value on a colour scale is ascribed to a work element or a work task (green means safe; yellow means conditionally unsafe or acceptable temporarily but needs further attention; red means the task needs redesign). In addition, cognitive issues in battery production relate to planning, surveying, and process quality control. In work tasks with extended work content, the importance of mental and cognitive conditions become pronounced, concerning issues like attention, concentration, adaption, learning, and planning. A cognitively well-designed work leads to better product quality, fewer errors, and increased well-being [14]. It may attract people to jobs, making them develop higher skills and be interested in training newcomers. It may even make them stay longer. It has also recently been found that cognitive issues are important even in jobs regarded as simpler. Simpler jobs may not be simple anymore, but combined in sequences varying a lot in many manufacturing enterprises due to increased numbers of variants being produced. Also, more of the production planning and the quality follow up is left to the operators in the process. 2.3 Automation and Human-Robot Collaboration Automation was widely adopted in manufacturing in the third industrial revolution. However, shifting from mass production to mass customization stimulated transformation towards flexible automation to meet demands for customized products and increasingly complex manufacturing tasks. Robots, and other automation technologies, have demonstrated superior ability to conduct repetitive and unergonomic tasks, fast and precisely [15]. Nevertheless, lack of flexibility and cognitive ability in robots motivates studies of human-robot collaboration [15], where the system benefit from the symbiosis between robot repeatability and high accuracy and human flexibility. Recently, Industrial 5.0 [5] has further promoted human-centric automation [15]. Human-robot collaboration can improve flexibility and efficiency of automation in battery production, addressing concerns about labour costs, shortage of human operators with high skills, and unsafe working environment due to intrinsic properties of batteries [16]. Despite recent progress, several challenges demand further research, e.g., lack of standardization on the design of batteries among different manufacturers that increases the number of variants in the structure and layout of batteries [17]. This high level of variation creates additional obstacles for robotic battery assembly and disassembly when flexible robotic manipulation planning and programming is required. Robot control systems need to react to different manipulating objects and situations so that a robot arm can reach and manipulate objects (i.e., cells, modules, and other components of batteries). Computer vision techniques have demonstrated their effectiveness in different scenarios in manufacturing [18] and have been discussed for tasks in battery production [19]; for example, using 3D point cloud instance segmentation to distinguish cables on electric vehicle battery packs for disassembly operations. Considering the successful implementation of deep learning in object detection, additional object detection algorithms can be introduced to battery production to facilitate decision-making processes and the robotic assembly and disassembly.

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Conventional industrial robots in automated production lines are surrounded by physical barriers or laser curtains for safety purposes. However, in human-robot collaboration, a human operator and a robot may work in close proximity, presenting new safety challenges [20]. Previous research has explored numerous collision detection methods, but their accuracy and reliability in actual applications need to be improved further [21]. Instead of passive reactions against potential collisions, human intention recognition, e.g. hand gesture recognition and human pose estimation, can facilitate active robotic motion planning to complete collision avoidance [21]. Recognition based on human gestures, eye movement and haptics can also improve human-robot interaction, thus promoting the efficiency of human-robot collaboration in battery production. In addition, environment perception in human-robot collaboration for battery disassembly can contribute to decision-making processes, promoting the level of efficiency and safety of battery production.

3 Smart Production Management 3.1 Production Planning and Control The production planning and control function is accountable for decision-making regarding demand forecasting, sales and operations planning, master production scheduling, inventory and capacity planning, shop floor production control and monitoring, and ensuring the timely delivery of manufactured products [22]. Production planning issues related to the maintenance function are discussed in Subsect. 3.2. Given the ongoing shift to electrification in various sectors, battery production systems are being challenged to keep up with rapidly rising demand for batteries, requiring new planning and control methods [2]. Production and control systems from ABB (Sect. 1) are examples of the industrial response to this challenge. Industry 4.0 technologies are increasingly used to create intelligent, smart manufacturing systems with improved interconnectedness and transparency. Smart capabilities have been explored for shopfloor planning and control [22]. For example, internet of things (IoT) may streamline the production flow of batteries, accounting for cell-level and battery-level process variability in process planning [23]. To further increase responsiveness, distributed and collaborative decision-making can be achieved by connecting various shopfloor planning and control systems, such as material requirements planning, enterprise resource planning and manufacturing execution systems. Smart production scheduling requires various forms of integration between different production planning levels and cyber-physical systems or digital twins [24]. Digital twins of production systems support predictive and proactive scheduling decisions in complex environments, e.g. planning across entire process chains to predict quality parameters of battery cell production and optimise process quality and cost-efficiency [23, 25]. Big data analytics and artificial intelligence (AI) can also improve accuracy and performance of demand forecasting and integrate forecasting with inventory and capacity management. AI applications for capacity planning and control integrate multiple control tasks, e.g. order releasing, sequencing, and capacity control. Multi-agent architecture modelling can model the resource configuration process to achieve these characteristics. Cloud manufacturing helps integrate inventory systems with enterprise and execution

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manufacturing systems [26]. For inventory and capacity planning and control, real-time data collection, monitoring, and control are possible through products with productembedded intelligent devices [27]. Regarding material requirements planning, master production scheduling, and sales and operations planning from a tactical and strategic perspective, integrating decisions from different planning levels using digital tools is considered the most critical for realizing intelligent manufacturing. The integration here could be both vertical integration (between intelligent systems at different manufacturing stages) and horizontal integration (between multiple enterprises in a manufacturing value chain) as further discussed in Subsect. 4.3. Overall, the digitalization of the planning and control functions improves manufacturing flexibility, agility, and responsiveness [22]. In addition, new environmental performance indicators are emerging to support green optimization (material and energy efficiency, waste avoidance or reduction) for more sustainable manufacturing and development [22]. 3.2 Maintenance Battery factories are characterized by high levels of automation, digitalization, and equipment intensity, making them complex and sensitive systems [28]. Therefore, maintenance is considered a top priority function to ensure production stability, efficiency, and quality. Maintenance is a fundamental principle of circular manufacturing and needs to be actively involved in all phases of the system’s life cycle, from systems design, through procurement, installation, ramp-up, and full production. Maintenance combines all technical, administrative, and managerial actions to prevent breakdowns, scrap, and waste in the operational phases. Further, the maintenance process and associated material flows serve to optimize equipment life span through cycles of repairs, refurbishment, remanufacturing, and repurposing of machine components. Maintenance of battery factories is especially challenging due to the variety of processes embedded in the same system, combining continuous and discrete processes, chemical and mechanical processing, and complex interactions between the process and the product [6]. However, despite all challenges involved, scientific literature in this area is still scarce. Few existing publications treat maintenance as a minor contribution to overhead costs in analytical models evaluating total battery manufacturing costs [29], which contrasts with established knowledge from other manufacturing areas where maintenance typically represents 15% of the total production cost [30]. To build a foundation for research on maintenance in battery production, we suggest the concept of Smart Maintenance as a framework [31]. Smart Maintenance is “an organizational design for managing maintenance of manufacturing plants in environments with pervasive digital technologies” that consists of four dimensions: (1) datadriven decision-making, (2) human capital resource, (3) internal integration, and (4) external integration. Many of these dimensions overlap with the subtopics related to human-centred production in Sect. 2. In data-driven decision-making, research is necessary to effectively utilize the opportunities provided by green field factory design to capture and analyse the right data for predicting failures and risks. Entire solutions must be designed from start to overcome existing challenges with predictive maintenance [32], reaching beyond traditional

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predictive maintenance towards prescriptive maintenance based on data analytics. By combining and analysing data from various computerized systems, maintenance actions could be automatically planned at optimal times with consideration for productivity and cost, for example. Human capital resource in maintenance is a critical challenge in securing competitive battery production. Collaborative actions for addressing the skill gap, developing competence profiles, and defining roles and responsibilities for maintenance personnel must involve both industry and academia; for example, creating training programs to upskill maintenance personnel as discussed in Subsect. 2.1. Internal integration contributes to effective work procedures and improves information flows between functions (maintenance, production, quality control, etc.) which is necessary to reach prescriptive maintenance planning. Internal integration can also address skill gaps, skill shortages and potentially high employee turnover; for example, rapidly train experts and delegate maintenance tasks to non-experts similar to autonomous maintenance [33]. External integration is crucial in a fast-paced business environment like battery manufacturing. It will be impossible for battery manufacturers to scale without successful integration with external front-edge technology and competence. Building strategic supplier relationships, addressing cultural and traditional differences, and securing mutual benefits are necessary to scale fast. 3.3 Production Quality and Effect on Battery Performance Quality in battery production has a significant impact on battery performance and service life, such as cell and pack consistency [34], along with other quality and maintenance issues previously discussed in Subsect. 3.2. Data-driven battery management provides valuable insights into the performance of various components and help identify critical production steps affecting battery performance. For example, battery management system monitors various key parameters (e.g., voltage, temperature, etc.) and controls of energy storage and transfer which are essential for the quick charging required by electric vehicles [35]. Battery health monitoring and analysis is essential to optimize the lifetime, safety, reliability, and efficiency of battery-powered devices by tracking key parameters, such as temperature, state of charge, and state of health, and making necessary adjustments for cell balancing [36, 37]. The system uses sensors, data acquisition systems, and algorithms to process and analyse battery performance data, allowing for timely interventions to prevent battery failure and extend battery life [36]. In the secondary battery system, including LIB, nickel-metal hydride (Ni-MH) and nickelcadmium batteries (NCB) used in various applications [38], the prioritized performance measure is the energy density. Data-driven approaches for state-of-charge, state-of-health and lifetime prediction, such as machine learning algorithms and statistical models, have been used to improve battery performance and health management systems for electric vehicles. This is essential to enable product durability, maintenance, repairability and other circular strategies discussed in Subsect. 4.1. One of the main challenges posed by these data-driven approaches is the reliance on large amounts of data collected from the battery system to identify patterns and trends [39]. Additionally, there is a lack of standardization in testing and measurement procedures. Industry also needs a better understanding of the

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underlying physical and chemical mechanisms that govern battery degradation. Those challenges need to be addressed for improved accuracy and effectiveness of data-driven techniques. When production quality issues are identified, the system can monitor the performance of affected batteries and identify any degradation or safety issues. It can also help determine the root cause and implement corrective actions to prevent similar issues from occurring in the future [40].

4 Sustainable Manufacturing Value Chains 4.1 Circular Economy Given the growing concerns of natural resource availability, circular economy (CE) is becoming a necessary operating framework for sustainable production and consumption. Battery production supply chains must combine primary (mining) and secondary sourcing (recycling) with other circular strategies to meet the demand for batteries. Product design for CE is essential to ensure the feasibility of remanufacturing and recycling of spent batteries. Supporting business models and circular strategies to extend battery-powered product lifetime are further discussed in Subsect. 4.2. Issues related to production quality affecting battery performance are also discussed in Subsect. 3.3. Battery recycling technologies have long been developed to handle spent batteries with increasing focus on lithium-ion, but also lead-acid, nickel-metal hydride, nickelcadmium, and other types of rechargeable batteries. Recycling processes combining a mechanical pre-treatment, hydro- and pyrometallurgical processes are continuously developed with new challenges related to new cell chemistries (aka next-generation batteries) [41]. Besides recycling, material substitution can also alleviate the pressure on scare minerals. For example, enabling greener and more sustainable electrical energy storage with the development non-toxic alternatives and next-generation batteries, new battery manufacturing processes [42] and new recycling approaches [41], etc. Other circular strategies include remanufacturing and repurposing second-life batteries [43]. Production has an important role to play in a circular battery system, considering both the material inflows and outflows [43]. The batteries from retired electric vehicles are estimated to reach 280 GWh in 2030 [21]. When degraded batteries reach the end of their first life (typically for electric vehicles), they can be repurposed for other applications, such as stationary energy storage to support renewable energy systems. When the quality and state of health of spent batteries is high enough, remanufacturing is possible [44], although challenges still remain to achieve industrial scale considering economic viability, production capacity, and balancing demand and supply [45]. With support from a data-driven battery management system, it is possible to identify batteries with remaining capacity and decide if they can be reused and refurbished for other applications, or remanufactured into new batteries [40]. Further research is needed to develop optimization algorithms and decision-making tools to find circular pathways for different battery types under different conditions and contexts. 4.2 Service-Based Business Models Service-based business models can support battery reuse and life extension, repurposing, remanufacturing and recycling. They can embed CE principles in the value proposition

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and commercialize the use, results or performance of a product, over the retention of ownership. Discussed since the 1970s [46], some of the expected benefits of servitization include: extend a product’s life cycle, promoting competitive advantage, creating more meaningful relationships with customers, and more responsible consumption. The idea of value delivery decoupled from tangible assets and material resource consumption is also known as dematerialization. This business strategy applied to batteries needs to be further explored. Several factors can influence the new business model adoption for battery CE. For instance, market readiness, financial incentives, available technologies and infrastructure, stakeholders’ involvement and relationship, industrial policy and regulations. These influencing factors can be viewed simultaneously as drivers and challenges for CE implementation in product end-of-life management: market uncertainty, unprofitable business cases, low-maturity technology and insufficient supporting infrastructure, lack of trust and willingness to share data, and lax environmental regulations. Literature has mainly explored main business models for electric-vehicle batteries: the take-back and servitization models. End-of-life vehicle take-back is essential to increasing the recovery rate of the automotive batteries [47]. The concept of servitization, in which the manufacturer retains ownership of assets, focuses on access to assets with business models in the form of a leasing or sharing platform [48]. This approach could reduce the electric vehicles’ initial cost, which may then increase the number of returned electric vehicle batteries. The approach can be combined with government incentivizing manufacturers to engage proactively in end-of-life processes instead of using control regulation. Another less explored business model is the exchangeable battery service concepts, where battery swapping was first suggested in the late 19th century as the fast-refuelling service for electric vehicles [49]. An example of their application is that the discharged battery pack of an electric vehicle can be swapped with a fully charged. In terms of structure and ownership, [50] the swapped batteries are often not owned by the electric vehicle users themselves, but rather a leased product based on some service contracts and consumers are charged based on usage or miles driven. This business model can be referred to as battery-as-a-service and hold good promise to overcome the challenge posed by long recharging time during long-distance travels. 4.3 Transparency and Resilience in Supply Chains The value-creation process of LIB production starts from raw material extraction and refining, and goes through cell components, modules and battery pack manufacturing to finished product assembly, useful product life and servicing, and eventually end-of-life management. Such a complex value chain involves a massive global business network. Critical upstream risks can propagate throughout the value network, such as uneven supply of raw materials and blocked export channels. Battery production requires a wide array of elements sourced from countries across multiple continents [51]. Transparency is defined as the extent to which all stakeholders have a common understanding of and access to product-related information without loss, noise, delay, and distortion. In battery value chains, transparency can ensure that raw materials are ethically sourced, workers are fairly treated, and environmental impact is minimized.

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For example, in the cobalt mining supply chain, blockchain technology enables realtime transparency to detect unethical cobalt production and support sustainable business decisions [52]. Blockchain can transform how partners cooperate and share knowledge, and is perceived as crucial for eradicating forced or child labour, pointing out if the mines produced more than expected amounts indicating mineral mixing, assisting in confirming responsible purchase and production, etc. [53]. For transparency at a value chain level, several challenges need to be considered, such as data quality and accuracy, standardization, integration with existing systems, regulation compliance, costs, and scalability. Stricter legislation can also push stakeholders to align their business goals for environmental sustainability [54]. Battery supply chains are vulnerable and dependent on traditional mineral resources. For supply chains to be sustainable, their operations need to be resilient [55]. That is, they need to establish responses to mitigate risks while maintaining the triple bottom line of sustainability. Supply chain resilience is the ability of supply chains to proactively respond to disruptive events and bounce back to normal or even better states of operation after the event has occurred, such as the COVID-19 pandemic and the war in Ukraine which impacted trade relationships among countries with many environmental, social and governance consequences. Supply chains are increasingly digitalised, providing opportunities for resilient value networks that are responsive, deliver quality solutions, and have transparent value creating operating models [56]. Such connected networks require collaboration between value chain partners, creating dependencies and potential vulnerabilities in the supply chain. Digital technologies can integrate, synchronize and manage the information flow across the value chain. From raw material extraction to the point of use, the whole process can be tracked to provide information for timely decision-making. In cell manufacturing, tracking the origin and movement of individual components (such as cells and modules) provides manufacturers with accurate information to identify the source of errors and take immediate corrective action. IoT and blockchain can track and monitor value-creation processes to enable preventive maintenance, reduce resource consumption and associated costs, and improve the production system’s sustainability [53, 54]. Recent developments with digital product passports and digital battery passport [57] are promising solutions for sustainable circular product management.

5 Conclusion The battery manufacturing sector is a key enabler in the ongoing societal green transformation. This paper discussed some of the most pressing issues organised around three themes related to the transformative vision of Industry 5.0 for Europe: human-centred production, smart production management, and sustainable manufacturing value chains. Each subsection suggested directions for further research and can be summarised as follows: • Developing news ways to measure skill gaps, what competences and knowledge should be developed to meet industrial needs; • Developing new ways to address the skill gap and skill shortage, how to create and deliver training, on-the-job-learning and individualised learning paths;

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• Promoting human-centred automation to address skill shortage and safety concerns; • Addressing new ergonomic and cognitive challenges related to the nature of battery and electric vehicle production processes; • Adapting physical and cognitive workload assessments accounting for variable human-robot task distribution (increased automation and flexibility); • Ensuring system integration/interoperability through standardization for higher levels of automation and easing communication between humans and machines; • Using automated systems to capture specialised knowledge and skills, lowering the demand on the human workforce in battery production; • Developing new planning and control methods to keep up with the rapidly rising demand for batteries and increased production systems complexity; • Prioritising production maintenance to ensure stability, efficiency, and quality for continuous production of batteries; • Capitalising on greenfield development to build fully digitalised systems, as opposed to upgrading older equipment and systems (brownfield development); • Developing data-driven decision-making enabling smart planning and control, predictive maintenance (predicting failures and risks) and autonomous maintenance (proactive and preventive measures); • Optimizing key parameters of production quality with the most impact on battery life and performance; • Prioritising the development of battery materials, battery technologies and industrial processes with minimal ecological impacts (e.g., resource depletion, toxicity), maximum potential for extended battery life (maintenance, repair and repurposing) and for value recovery (remanufacturing and recycling); • Assessing battery state of health to extend battery life and identify optimal circular pathways when reaching the product end of life; • Implementing service-based business models to increase value delivery from batterypowered products; • Implementing service-based business models to retain control of critical components and materials to support value retention through circular strategies; • Digitalizing supply chains for increased transparency to support stakeholder collaboration and increased supply chain resilience; • Exploiting IoT and blockchain technologies for more ethical and environmentally responsible battery manufacturing value chains; • Exploiting advanced data analytics to optimize each process in the production value chain accounting for broader aspects, such as material supply constraints, potential supply disruptions, process efficiency, quality, cost, etc. While this list is not exhaustive, it provides pointers to focus and accelerate research and development for sustainable battery production by addressing the most salient industrial challenges identified and discussed in this paper. Acknowledgements. This work was supported by Västra Götaland Regionen Regionutvecklingsnämnden under grant no. RUN 2022-00294 (PreMAXBATT), by Swedish innovation agency Vinnova and the strategic innovation programme Produktion2030 under grant no. 2022-02467 (MATTER) and 2022-01279 (EWASS). The work was carried out within Chalmers’ Area of Advance Production. The support is gratefully acknowledged.

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Assessment of the Main Criticalities in the Automotive Battery Supply Chain: A Professionals’ Perspective Valérie Botta-Genoulaz1(B)

and Giulio Mangano2

1 Univ Lyon, INSA Lyon, Université Claude Bernard Lyon 1, Univ Lumière Lyon 2,

DISP-UR4570, 69621 Villeurbanne, France [email protected] 2 Department of Management and Production Engineering, Politecnico di Torino, 10129 Turin, Italy [email protected]

Abstract. The environmental transition has become a crucial element in the European Commission agenda. In this context, a key role is play by the electrification of the mobility that is viewed as a feasible alternative respect to the traditional fossil fuel paradigm, due to significant energy benefits. However, the scarcity availability of raw materials for producing battery packs and their concentration in few specific areas of the world, is determining high level of uncertainties and vulnerability in the supply chains of European car manufacturers. In particular, they are heavily facing the challenges that this transition is posing, specifically considering the reorganization and the structure of the associated supply chain. Therefore, this paper aims at capturing the perspectives of automotive industry about the different stages of the battery supply chain in the electric vehicle market. To this end, a questionnaire survey has been administrated to a set of identified automotive professionals. The obtained results underline that procurement, production and recycling of batteries are the most critical steps. On the contrary, the transport and the storage of the batteries are seen less crucial. This research is intended to stimulate future studies on innovative supply chains able to better manage batteries, and it is aimed at supporting car producers on designing more accurately their supply chain and to support decision makers in more effectively develop policy in the field of the electric mobility transition. Keywords: Supply Chain · Battery · Electric Vehicle · Questionnaire

1 Introduction Transport that represents the core of all business and social activities at a worldwide level, is one of the most impacting agents for the environmental pollution, as about onefourth of greenhouse emissions can be related to transport vehicles [1]. This is because the transport sector mainly depends on fossil fuels that discharge enormous amounts © IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 536–548, 2023. https://doi.org/10.1007/978-3-031-43688-8_37

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of greenhouse gases that are the primary basis of the climate change. Furthermore, the transport sector is a cause of the urban heat island phenomenon, resulting in emissions of fine particles, i.e. PM2.5, NOx, and SO2, into the natural environment. Air pollution has long been a focus of attention because of its adverse effects not only on the climate and national economies but also on human health. Therefore, according to the European Commission the sustainability of the transport has increasingly become a central theme [2]. In this context, the electrification of the mobility is considered a crucial element in achieving environmental sustainability goals due to significant energy benefits compared with conventional fossil fuel vehicles [3]. Even electric vehicles (EV) still suffer from a limited mileage, an underdeveloped charging network and lengthy charging times, the expected number of EV sold is projected to increase from 4 million in 2018, to 900 million in 2040. This will bring to a dramatic increase the demand for batteries that are currently the main element in powering EVs [4]. Moreover, car manufacturers have announced heavy investments in EVs, and a lot of new electric models are expected to enter the market in the next years [5]. In this context, Europe has been undertaking a strategy of climate neutral economy and EVs might play a crucial role in achieving this goal. Car manufacturers rely on lithium-ion battery for powering the electric engines of the vehicles since this technology is able to offer an acceptable level of efficiency, high powers, good life cycle and high energy density [6]. This business environment is determining strong competition among players in order to have the availability and the steady supply of batteries together with the related raw materials. In particular, car manufactures are dealing with battery manufacturers that have a strong market position in the light of the scarcity of batteries [7]. In addition, the environmental transition in transport is determining relevant uncertainties in the automotive supply chain (SC) because of the massive technology shift related to product design, production process and more in general supply network structure. Other critical aspects might be referred to the limited availability of lithium, its spatial concentration in specific areas of the world and difficulties in implementing a sustainable and economically feasible recycling process. Therefore, these complex relationships are leading vulnerabilities to EV SC, and the battery SC structure and the related organization are important drivers of procurement and cost reduction for a low impact vehicle [8]. In addition, the European Green Deal places European Union at the forefront of the green transition, and one of the main objectives is the decarbonization of the transport sector [9]. Thus, European car industries have embarked on a transition from the production of vehicles with internal combustion engines to the production of electric vehicles, which is leading to the reorganization of the existing automotive industry European sector [10]. Specifically, European Union seek to achieve a more than 80% share of EV by 2030 [11], and there is still an open debate about the decision of prohibiting the sale of traditional engine powered vehicles by 2035. Therefore, there is a strong policy pressure that is forcing the automotive industry to design and produce more environmental vehicles based on electric power. European Union, via the program so called European Battery Alliance has been trying to actually support the battery production within the European

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borders in order to better respond to the increasing investments borne by European car manufactures in the electric arena [12]. As a matter of fact, the cells that are the most relevant component in a battery pack, accounting for the 70% of the cost of a battery and up to the 40% of the cost of an EV are dominated by East-Asian companies with a European market share lower than 4% [13]. As a consequence, car manufacturers are called to carefully manage their SC, by precisely identifying the main critical stages, considering that the number of EVs produced in Europe is expected to increase up to more than 4 million by 2025 [14]. Thus, the perspective of European car manufacturers practitioners becomes particularly interesting to capture the most important challenges that this industry is facing in these uncertain periods. To this end, the results of a questionnaire survey administrated to professionals working in the European automotive industry are here presented to understand the most critical stages of the battery SC in the European car industry. The paper is structured as follows: First an overview of the most relevant literature focused on the critical issues of the EVs markets and the related SC stages is shown. After that, the adopted methodology is described and the results presented. Finally, discussions and conclusions are traced.

2 Literature Review Web of Science database was chosen to locate research works related to battery supply chain, especially in the automotive sector. The rationale for this choice is that this database is the oldest, global and multidisciplinary database that have the highest score in the visibility index global and is one of the world leaders in peer-reviewed journal ranking and evaluation. To identify the trends, the database was asked to search for the following equation in title, abstract, and keywords: (battery/batteries) and (supply chain) from 1975 until 2022. The 879 results include 625 articles, 79 review articles, 179 proceeding papers, 25 early access, 7 editorial materials, 9 book chapters and 2 corrections. Within these results, we search for papers dealing with (vehicle) in title, abstract, and keywords to focus on the automotive sector, with the query (TS = (supply chain)) AND (TS = (batter*)) AND (TS = (vehicle)), and obtained a total of 343 results. We have preferred the word “vehicle” instead of “automotive” because more results were obtained (343 versus 57). As we can see from the distribution of papers over the years on Fig. 1, even if the first paper dates from 1995, researcher’s interest really began in 2018, and in 2020 when we focus on automotive sector. The literature shows many challenges, as the need to cope with the chip shortage and the scarcity of resources, the increasing transportation cost of EVB (electric vehicle battery) as those batteries are now registered as dangerous, the ensuring social welfare as the EVB market expands, and also a major environmental issue in every stage of the supply chain. Without embarking on a systematic analysis of the literature, we have sought to identify the issues associated with the main stages of the supply chain: procurement, production and use (including storage, and transport), and finally recycling in the broadest sense. It has come to light that the procurement of battery raw materials face several issues, due to the availability of the resources (lithium, cobalt, aluminum, copper…) and the

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250 200 150 100

0

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022

50

"batter*" and "supply chain"

"batter*" and "supply chain" and "vehicle"

Fig. 1. Paper distribution in Web of Science database

extraction process. Indeed, the supply of copper and aluminum is the one emitting the most greenhouse gas, especially during the manufacturing of the cathode and anode. The battery market accounts for 22% of the lithium exploitation [15]. These above-mentioned aspects related to both economic importance and supply risk demonstrate the reason why these materials are critical for European companies [16]. [17] investigate the effects of key parameters on the equilibrium capacity allocation decisions and manufacturers’ profits; they also show that to maximize social welfare, the upstream EV manufacturer should not supply batteries to its competitor if the procurement cost from the external supplier is low, which is contrary to the case of profit maximization. Some studies have compared make-or-buy strategies, such as Rafele et al. [8]; the authors draw a parallel between the purchase of complete batteries that implies the highest costs and CO2 emissions; on the contrary, buying single components helps improving these aspects, but it requires a certain level of vertical integration by the car manufacturer together with specific know how. Moreover, it is shown that the production stage of electric vehicles has the biggest environmental impact due to the battery production. Already in 2012, [18] develop a lithium supply chain model that provides a framework with which to investigate the technical, geopolitical, and economic factors that impact the supply of lithium through different life cycle stages. The assessment of batteries’ supply chain is also going to be integrated in low impact vehicles, focusing on location of the associated warehouse [3]. In the use stage, we can see the advantages that electric vehicles have compared to internal combustion engine vehicles, but we must keep in mind that the whole product lifecycle must be considered and thus the impact of the battery production must be reduced. In addition, it is important to underline that Europe accounts for only the 3% of global production of battery at an international level. This is a crucial aspect considering that a relevant number of electric and hybrid vehicle are sold in Europe [19]. Therefore,

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heavy investments are carrying out not only for supporting the production but also to implement reuse and remanufacturing processes, in order to reduce the European dependence from far-Eastern industries [20]. However, it is worth noticing that due to safety and performance reasons, battery should be stored and moved in warehouses and trucks with the control on humidity and temperature [21], with consequent negative impact on cost and on the environment. A recent study show that the conditions of the electric vehicle manufacturer’s product choice strategy depend on two thresholds related to the production cost of batteries, the manufacturing and assembly cost of EVs, the government subsidy and the range anxiety, while the conditions are independent of the EV manufacturer’s market position and battery outsourcing decision [22]. In addition, the issue of recycling is very recurrent. Indeed, for instance in China, Zhao et al. [23] developed pricing strategies and emphasized the fact that the increase of government subsidies is correlated with the development of EVB recycling. Many authors developed studies regarding the EVB life cycle and it is clear that the battery lifetime must be extended as much as possible. According to the EU Battery Directive, the recycling of battery is expected to increase to 65% by 2025 [24]. Re-using batteries for other purposes could also restrict the waste as much as possible, as explained by Picatoste et al. [25]. The re-used batteries could have new lives as fast charging stations or even back-up power supplies. However, it would be important to assess the risk [26]. There are different recycling strategies, but centralized recycling facilities (with high capacity) offer great advantages. Using secondary materials (including scrap and residuals from production processes) could also be beneficial [27]. Overall, an effective framework to control the battery supply chain is required, to get a reliable circular or closed loop EVB supply chain [28–30]. Some authors review the external policy drivers and barriers for CE strategies for lithium-ion batteries and discusses how policy can be further developed. The results from [20] demonstrate that many manufacturers are pursuing CE strategies, mostly focused on repair, refurbishing, and repurposing. [28] studies a three-period electric vehicle battery recycle and reuse closed-loop supply chain consisting of a battery manufacturer and a remanufacturer; their result suggests that, comparing with new battery manufacturing, battery recycling and reusing would contribute to reduce raw material consumption hence reduce environmental impact, but may not gain financial benefits. Using game-theoretic models, [29] analyze process innovation strategies for green product remanufacturing in a closed-loop supply chain consisting of an upstream supplier and a downstream manufacturer; they conclude that (1) process innovation can effectively improve remanufacturing performance while increasing the recovery rate of the manufacturer, (2) although the cooperative mechanism is always beneficial to the supplier, the supply chain and the environment, it may not be favorable by the manufacturer; they also show that government subsidies can incentivize the manufacturer to adopt the cooperative mechanism, thereby achieving a win–win situation. [30] highlight the need for effective policy frameworks to foster a circular EV battery value chain. A life cycle sustainability assessment as well as a Product Sustainability Budget could also be useful tools [31].

3 Methodology The research was carried out according to the following steps.

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• Questionnaire Construction: based on the literature review outcomes, a questionnaire survey was designed so that to seek information and insights from professionals working the automotive industry with regard to the main challenges that the automotive SC is called to face in dealing with the battery for new electric vehicles. Before its administration, a pre-test was conducted with the aim of highlighting criticalities associated with the ambiguity and redundancy of questions, consistency, and typos. After that, the survey was ready to be submitted. • Sample identification: the initial survey sample was made of 80 potential respondents. In particular, they have been identified via a research on LinkedIn. The research was carried out by looking for professionals working on the European Automotive sector. Once the identified profile was selected, he/she was contacted and in turn invited to take part to the study, by sending him the cover letter and the link to the questionnaire. • Survey administration: the survey was developed by means of Google Forms and then sent by e-mail to the potential previously identified respondents. Specifically, they received a mail, with a cover letter aimed a presenting the objective of the research, with the related link to the questionnaire and entered their answers in the Google Form system that saved them accordingly. Finally, the obtained responses were downloaded and coherently organized in an excel file so that to have a dataset suitable to the analysis. The administration period extended over a couple of months according to two different waves, in 2022 in November and December. The first one was the initial invitation to take part to the research, and the second one was the reminder. This last one was conducted, to increase the response rate. The administrated survey was composed of two main sections. The first part asked for demographic aspects of the respondent. In particular, the age, the nationality, the educational level, and the years of working experience were investigated. In the second part, respondents were asked to rate the importance of a set of proposed issues about the management of the battery SC in the automotive industry. For all statements, a Likert scale scoring system was used, where 1 = Not important; 2 = Moderately important; 3 = Important, 4 = Very important; 5 = Extremely important. This scale is largely used in carrying out survey research for assessing questionnaire answers [32]. The first set of questions was focused on assessing the level of criticality of every stage of the battery supply chain. Specifically, the extraction and the procurement that are becoming very important in maintaining stable the SC due to the growing demand of the scarce raw materials [33]. The production, the transportation and the storage were investigated. The recycling was considered too, since it is one of the topmost challenges in the battery SC [34]. The focus was on these aspects as, according to previously discussed in the Literature Review section, they represent the most relevant stages in the battery SC. In fact, the assessment of this SC is crucial to understand the main criticalities that companies are going to face as soon as the market share of EVBs increases [35]. Also, the battery cost was taken into account, considering that is a relevant driver for an EVB [36]. This is still an open question. On the hand it could be expected a decrease on cost thanks to the increased capabilities of companies and thanks to the more exploitable economies of scale related to the higher production volumes. On the other hand, some studies indicate that the cost of raw material is expected to increase further [37]. These

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issues, highlight that there is the necessity of investigating the challenges in EV battery supply chain according to the opinion of experts [1]. Once the responses were collected, the obtained data were ready to be analyzed. In particular, the questions based on a 1–5 Likert Scale were semi-quantitatively analyzed, and the open questions related to the solutions of the critical aspects of the battery SC very carefully analyzed in order to gain meaningful insights.

4 Results The questionnaire achieved 31 responses, with a final response rate equal to about 37%. The results related to the demographic questions are presented in Table 1. Table 1. Demographics Age

Level of Education

Years of Experience

Less than 35

51,61%

35–44

19,35%

44–64

22,58%

More than 65

3,23%

High School

6,45%

University (Bachelor & Master)

70,97%

Ph.D.

19,35%

Less than 5 years

38,71%

Between 5 and 10 years

16,13%

Between 10 and 20 years

19,35%

More than 20 years

22,58%

More than 50% of the respondents are less than 35 years old, and only one is more than 65, meaning that especially young professionals decided to take part to the study. This result is coherent with the years of experience. As a matter of fact, 38% of the respondents had less than 5 years of working experience. 22.5% of them had been working for more than 20 years. Finally, by observing the level of education almost 70% of the respondents had a university degree, 20% of professionals got a Ph.D. and only 6.5% had a high school diploma. This result shows that in the field under study higher levels of education can be observed, and high skill professionals proved to be willing to take part to this study. Table 2 shows the results of the administrated survey. Respondents almost fully agree on considering the battery as the principal cost driver in manufacturing an electric vehicle. This demonstrates that this battery should be deeply studied in the future in order to reduce its cost. Similarly, more than 80% of the respondent see the procurement a critical stage in the battery supply chain. Specifically, 74% strongly agree on viewing this SC stage as a critical one. This is coherent with

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Table 2. Survey Outcomes Item

1 strongly disagree - 5 strongly agree 1

2

3

4

5

Battery as crucial cost driver for EVBs

0,00%

9,68%

12,90%

22,58%

54,84%

To what extend these stages can be critical in battery SC

Procurement

0,00%

6,45%

6,45%

12,90%

74,19%

Production

0,00%

3,23%

19,35%

25,81%

51,61%

Storage

3,23%

22,58%

38,71%

12,90%

22,58%

Transport

9,68%

12,90%

22,58%

22,58%

32,26%

Recycling

0,00%

0,00%

19,35%

19,35%

61,29%

The extraction of raw material in Europe 0,00% can support battery SC

0,00%

12,90%

22,58%

64,52%

the study developed by the European Union about the identification of critical raw materials for strategic sectors and technologies. In particular, it lists lithium, cobalt and graphite as largely needed in battery as critical ones [38]. As a consequence, there is a relevant level of procurement risk in the light that Chinese companies are the main supplier of anode material (graphite) and lithium-cobalt oxide cathode material [39]. By observing the production stage, about 50% strongly agree on its criticality, and about 20% of the respondents are neutral respect to this SC stage. This result might couch on an increasing strategy based on the internal manufacturing process for battery implemented by car manufacturers [40]. As a matter of fact, for batteries more and more companies are trying to pursue a make-strategy instead of a buy-strategy in order to lowering the cost, and internally develop and keep specific competences that will be more and more required in the future. Storage and transport stages are both considered less critical compared with the previous ones. This result is not aligned with the literature outcomes that underlines a certain level of criticality for these stages of the SC. A possible reason could be that companies have already established effective and efficient strategies for the inventory management and for the transport of their products and batteries, thus they are more easily able to deal with battery in these SC stages. Finally, more that 61% of the respondent strongly agree and almost 20% of them agree about the criticality of the recycling. In fact, the recycling of the battery is not a well-established process, and a lot of research has been undertaken in the field. Nowadays it often requires high quantity of reagents, high quantity of energy for separating rare and precious materials [41]. Therefore, it is still a huge challenge for car companies to efficiently manage this process. Finally, respondents were asked to express their level of agreement with the potential support of extracting raw material in Europe. The obtained result is coherent with the opinion related to the procurement stage. There is a high level on agreement about the opportunity of having raw materials in Europe. The shared perspective is that this raw material proximity could become a lever to relieve the dependency from Far East companies and in turn to simplify the level of complexity of battery SC with positive effect for European car manufacturers and for their customers [42].

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In the last part of the survey respondent could mention some relevant solutions and issues that should be undertaken in order to more effectively face the investigated challenges in the battery SC in Europe. The results show that four different areas of interest can be identified. First, European Community should financially support applied research in the field so that to make more autonomous the local automotive sector: “Searching alternative solutions (Hydrogen)”. In fact, the Hydrogen is a quite promising solutions in the next years for powering electric vehicles, and it is expected that it could be more largely adopted from mid 2030s [43]: “Massive investments from governments and EU”. The European Commission already heavily support the EV market for the consumers’ perspective and funds a lot of research projects aimed at developing innovative technological solutions in the field under study [14]. In addition, policy makers are called to simplify the administrative paperwork for the process of extracting raw materials: “Make mining licenses less bureaucratic”; “faster permitting”. Actually, it is worth underling that especially licensing processes require the acquisition of a large amount of data that need to be deeply analyzed in order to accurately evaluate the potential negative environmental impacts [44]. However, policy makers should always pay attention to social aspects associated with mining activities: “mining in human conditions”; “Develop fair and sustainable extraction of these raw materials”. Finally, the fourth aspect refers to the circular economy and to recycling processes: “Battery technology with less rare materials”; “Increase local procurement and production”; “To assemble in house”; “Circular business cycles, battery degradation reuse, research on how to recycle the batteries”. These issues have been gained growing attention from research, industries, and policymakers [45] since they are considered practices that can foster the reduction of the environmental effects and at the same time the maximization of the resource efficiency [46]. These results show a high level of complexity associated with the SC for the batteries, especially in relationship with the procurement and the recycling stages. This complexity is quite difficult to be managed and it requires the involvement of a plethora of stakeholders as the respondents have underlined in the proposed questionnaire. In particular, the procurement can be made less critical via active programs implemented by the public policy maker that might support this process with easier administrative paperwork. On the contrary, research activities aimed at developing innovative technologies for both recycling and reusing should be carried out by automotive companies, together with universities and research centers that are increasingly interested in these topics. Another important aspect might be referred to the fact that the SC under study is not completely well established yet. Specifically, the battery SC is expected to have more companies operating in this industry, at different levels of the SC with a market less concentrated in the Far-East area.

5 Conclusion and Perspectives This paper is aimed at investigating the main stages of the battery supply chain in the electrical vehicle arena. To this end, a survey questionnaire was developed and administrated to an identified sample of experts. In particular, the objective is to capture

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the perception of professionals working in the European car industry about the level of criticality associated with the main stages of the SC at an issue. The results highlight a general relevant level of criticality and concern about the managing the SC in particular considering the procurement, the production and the recycling processes. On the contrary, transport and storage are viewed as less critical. In addition, it is worth mentioning the cost, still seen as a critical driver for electric vehicle. These results might also depend on the high volatility of market price for specific materials (e.g. lithium and cobalt) [23]. The proposed study can be considered as a support for car manufacturers for prioritizing their strategic actions to be aligned with the challenges in the battery SC. However, this support should be implemented in a systemic way, considering all the stages of the SC from the extraction of the raw materials to the final reuse of recycle of the batteries. In this sense, policy makers should include the critical SC aspects in designing their policies in order to support European car industry that is exposed to high level of uncertainties. In particular, attention needs to be given to applied research focused on exploring alternative materials to the lithium so that to reduce the European dependency from other continents. Furthermore, it is crucial to have closer suppliers for rare, critical, and costly materials. This would make the SC more flexible, resilient, and efficient. In fact, several mining companies are emerging in Europe aiming at directly providing raw lithium to European car manufactures [47]. These companies should be effectively supported by European public authorities, not only from a financial perspective, but also in terms of leaner bureaucratic and administrative paperwork. In this way, the production of battery in Europe will be also able to create a relevant number of jobs. Specifically, European car manufacturers are expected to shift from a buy strategy, where batteries are purchased as finished product to a make one, in which industries develop vertical integration that allows to gain autonomy, to limit risks, and to increase the capability of human resources employed in these sectors. Consequently, the issue of dramatic job loss that is expected due to environmental transition could be relieved. This transition should consider both environmental and social impacts of the whole SC, such as the energy required to produce cells, the often-unregulated working condition, and the material extraction stage [48]. Finally, as a final crucial point to make easier the adoption of EVBs, policy makers are called to develop broad charging grids for effectively boost the market. This paper originates both theoretical and practical implications. From a theoretical point of view, this study represents a contribution in the field of the study of the SC in the automotive sector focusing on the management of the battery. This is a very important research field with an increasing number of contributions. Indeed, this paper highlights the need to accurately design this SC, in the light of the massive transformation that this industry is facing, especially in Europe. Thus, it might stimulate further studies focusing on the most critical stages of the SC and on the alternative business models here highlighted. From a practical perspective, the proposed research might increase the awareness about the criticality of the SC of the batteries in the electric transition of mobility. Thus, it can facilitate policy makers in more accurately design the policy related to the support to automotive industry that might be implemented at the different levels of the SC, and by using different drivers (legal, administrative, fiscal, economic). In addition, this paper

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might support car manufacturers in the design of a more reliable and less risky SC for the battery that is the most important component and highest cost element for an electric vehicle. However, this paper suffers from some limitations. In particular, the survey items related to the literature review, considers the SC stage in a very general way, without entering more in detail in the main activities associated with every step. In addition, the sample size was limited to some car manufacturers professionals. Future studies will be addressed to more deeply investigate the processes related to the here considered SC stages, and to enlarge the sample size by including more respondents, and other professionals operating in companies producing batteries and battery’s components. Moreover, other stakeholders will be included, such as public policy makers that in this industrial field have been played a crucial role. Acknowledgement. This work was developed in collaboration with Paul Grieumard, Lea Issa and Valentine Pyt, master students at the Industrial Engineering Department of INSA Lyon.

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Integration of Hydropower and Battery Energy Storage Systems into Manufacturing Systems A Discrete-Event Simulation Carla Susana Agudelo Assuad(B) , Lennart Deike, Zhicheng Liao, and Md Ali Akram Norwegian University of Science and Technology NTNU, 2815 Gjøvik, Norway [email protected]

Abstract. The integration of on-site renewable energy generation into manufacturing systems can contribute to lower CO2 -emissions and reduced energy costs for manufacturing companies. The main challenges for integrating on-site renewables arise from their volatile nature, paired with the variable energy demand of manufacturing processes. Battery Energy Storage Systems (BESS) can be used to synchronize energy generation and demand. This paper investigates the integration of an on-site micro hydropower-plant and a BESS into a chair-manufacturing plant using discrete-event simulation. The simulation model tracks the energy demand of the manufacturing process and models the allocation of the energy between the manufacturing process, the hydro-powerplant, the BESS and the grid. The results show cost reduction potentials of up to 55% and emission reduction potentials of up to 63% by integration of the hydropower-plant. The integration of the BESS can bring no further improvements regarding economic indicators. Further emission reductions could be achieved. A trade-off between economic and ecologic goals can be noted. The results clearly advocate the integration of renewable energies into manufacturing. While no clear recommendation for the integration of BESS can be made, further investigation of the application of BESS in combination with more volatile renewable energies (e.g., PV, Wind) is suggested. Keywords: discrete-event simulation · renewable energy · sustainable manufacturing · energy storage systems

1 Background Over 150 nations signed the Paris Agreement on the international Climate Change Conference in 2015, agreeing to limit global warming to 2 °C. To achieve this, the decarbonization of the energy sector must be driven forward. Norway has a particularly high share of renewables of 95% [1]. However, large quantities of energy are imported from countries with a lesser share of renewables (e.g., Germany), which heightens the CO2 -Intensity of the Norwegian energy mix [2]. The spread of decentralized renewable energy generation can help to increase the share of renewables in the electricity mix. The integration of on-site renewable energy © IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 549–559, 2023. https://doi.org/10.1007/978-3-031-43688-8_38

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generation can also lead to benefits for the manufacturing company. Electricity costs can be reduced due to low levelized costs of electricity (LCOE) of renewables, the possibility to avoid taxes and fees and the option to sell surplus energy to the grid [3, 4]. One major challenge in the successful integration of on-site renewable energy generation into manufacturing system lays in the synchronization of energy generation and consumption [5]. Energy generation from renewable sources is volatile and hard to predict. Energy demand of the manufacturing processes varies as well [6]. Several possibilities to synchronize energy generation and consumption within a microgrid exist. Through energy flexible manufacturing the energy consumption can be synchronized to better fit the energy generation curve [7]. Through the use of energy storage, the generation and usage of energy can be decoupled [8]. Schulz et al. [5] describe a concept to integrate decentralized renewable energies into manufacturing systems. Economic advantages for the manufacturing company and positive externalities for the distribution grid are pointed out. The focus lays on photovoltaic as a renewable energy source. Wind is only scarcely considered, and hydropower is not considered at all. Flexibility is provided through flexible manufacturing. Islam and Sun [9] suggest an approach to find the optimal size for on-site renewable energies in a manufacturing system. PV, Wind turbines and Battery Energy Storage System (BESS) are considered. The approach follows a two-step approach: First, a simulation model is used to assess the energy demand by the factory. The results are than used as input in a mixed integer non-linear programming optimization model to determine the optimal size of the renewable energy system, considering overall energyrelated costs. Schulze et al. [10] conduct a simulation model with the goal to increase energy selfsufficiency of a manufacturing system. Three different strategies are analyzed: demand side management, energy storage and adaption of the size of the energy generators. As renewable energies, wind and PV have been considered. A combination of demand side management and energy storage has been found to be most promising for achieving their goal. Beier [6] suggest a real-time control method to use energy flexible manufacturing to facilitate the integration of on-site renewable energy generation in manufacturing systems. The method is tested in an agent-based simulation and verified in two case studies. As renewable energies, wind and PV are considered. Flexible pricing was included. The author states that energy flexible manufacturing can contribute to the integration of renewables into a manufacturing system and reduce energy costs and CO2 -emissions. Thornton et al. [11] use a mixed integer linear programming optimization to assess the optimal size of an on-site renewable energy system consisting of PV, wind and BESS for the case of a manufacturing plant situated in Australia. The authors state, that the integration of a BESS does not improve the economic indicators. Ecologic factors are not considered. No work considering the integration of hydropower plants in manufacturing systems could be found. Variable energy prices are only scarcely considered. This paper analyzes the integration of an on-site micro hydropower plant into a manufacturing system. Variable energy prices will be integrated. Additionally, a BESS will be installed to provide flexibility to the system and enable a decoupling of energy

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generation and demand. Both economic and ecologic indicators are regarded. The case of a chair-manufacturing factory situated in southern Norway will be considered. The system will be modelled using a discrete-event simulation model. Summarizing, the following research questions will be addressed: • What are the effects in energy costs and CO2 -emissions of adding an on-site microhydropower plant in the power generation mix of the examined chair-manufacturing system? • What is the potential to improve the economic and ecologic indicators by integrating a BESS into the system? The remainder of the work will be structured as follows: In Sect. 2, the methodology and the simulation model will be explained. Section 3 summarizes the results of the simulation. The results are discussed in Sect. 4, and recommendations are deduced. Finally, Sect. 5 summarizes the findings and concludes this paper.

2 Methodology The proposed research questions will be investigated by building an discrete event simulation model using Anylogic software. The following chapter presents the structure of the simulation model in Sect. 2.1, the assumptions in Sect. 2.2 and the testes scenarios in Sect. 2.3. 2.1 Model Structure The model is divided into two sub-models. The first sub-model represents the manufacturing system. The second sub-model represents the energy system, including the hydropower plant, the BESS, and the connection to the electricity grid. In the following, we will refer to the first sub-model as the factory-model, and to the second sub-model as energy-system-model. Factory Model. The factory model assesses the energy demand based on the state of the machines (idle or working) by reproducing the manufacturing process (Fig. 1). It is based on stochastic distributions of the time individual machines need for the work on specific parts. The energy consumption is calculated in a minute-by-minute time step and is cumulated over 15 min to be send to the energy-system-model. An additional fixed energy demand for lighting and heating is added based on energy consumption per m2 . To make connections with the energy system, the energy consumption is set for the four manufacturing procedures as well as the base load for the operation of the factory, such as the heating and lighting. The factory will add up the energy demand every 15 min by a state chart: 1. Start; 2. Get request; 3. Reset energy demand. The energy demand of the entire factory is updated on a minutely basis while the energy system is on every 15 min. When the energy system comes to the “requestFactory” state, it will get the factory’s accumulated demand for the previous 15 min and return it to the state chart in the energy system. After that, the factory energy demand is reset to 0 and reaccumulated

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Fig. 1. Factory sub-model structure

the usage until send it to the energy system in the next 15 min. The reason for setting 15 min as the factory return interval time is that it’s more reasonable to calculate the energy demand based on the manufacturing procedure instead of on every minute, which might bring more difficulties in keeping the model running smoothly, and 15 min is the proper estimation for completing one procedure. Figure 2 shows the energy demand in factory simulation system.

Fig. 2. State chart connection to the Energy System

Energy-System Model. The energy-system-model represents all energy sources, sinks and their interactions. The battery and the electricity grid can act both as an energy source and a sink. The factory acts as a sink. The hydropower plant acts as an energy source. Each energy source or sink can be identified by a unique ID. The allocation of energy between the entities is done in an Energy Management Trading System (EMTS). The EMTS manages the energy allocation between the entities periodically in 15min time steps in accordance with the shortest trading period on the Nordpool market [12]. In each time-step each entities send an energy offer or bid to the EMTS. The bids and offers contain the amount of energy offered or demanded, the energy price and the unique ID of the sending entity.

Integration of Hydropower and Battery Energy Storage Systems

553

The process-steps of the energy-system are explained in detail below and shown in the form of a state chart in Fig. 3. 1. startPoint: The start point is the neutral starting position. Every 15 min a message will trigger the transition to the next state and thereby start the energy allocation for this period. 2. getHydro: The amount of energy generated from hydropower in this period is calculated. It depends on the peak power capacity of the hydropower and the seasonal factor. 3. getGrid: The prices for buying and selling electricity from the grid for this period are updated. This happens by reading in the current prices from an Excel-File. 4. requestFactory: A message is sent to the factory to request the amount of energy used in the factory in this period.

Fig. 3. State chart of the Energy System – Model

To better understand the way energy is exchanged it is important to look more closely at the different entities. The grid can buy and sell unlimited quantities of energy in every time step. To guarantee that the demand of the factory is fulfilled, a sufficiently high number is set as the price for the factory. The price for the hydropower is set to its marginal costs (=0 e/kWh). It could also be assumed the operating cost as minimum price, however in Norway that price is very low for small run-of-river hydropower ( $1/kg), Moderate ($0.5–1/kg), Low (< $0.5/kg)

new ships with electric battery technology can have a high initial investment cost [44]. Furthermore, installing charging stations and developing a reliable charging infrastructure requires collaboration between multiple stakeholders, including ports, governments, and energy providers. Despite these challenges, some companies already invest in such infrastructure, recognizing the potential benefits of reduced emissions, improved air quality, and increased operational efficiency. In the Norwegian context, the government played a pivotal role in facilitating a wide implementation of battery-electric systems in ferries [28]. Additionally, the energy company Equinor adopted battery-electric systems on its offshore supply vessels [1]. As battery technology advances and becomes more affordable, we expect to see further investment in the necessary infrastructure to support its widespread adoption in maritime transportation. According to recent studies, electric vessels could, by 2050, economically serve 40% of today’s sea routes [14]. 2.2

Fuel Cells

Fuel cells are another promising alternative to internal combustion engines in maritime transportation. They offer a plug-and-play zero-emission solution and a higher total energy efficiency than traditional marine diesel engines. Fuel cells eliminate the combustion processes that typically occur in internal combustion engines, resulting in only one step of energy conversion, making them a more efficient option for generating electricity. There are several types of fuel cells, including hydrogen, ammonia, and methanol, each with unique advantages and limitations, which are highlighted in what follows. Hydrogen Fuel Cells. Hydrogen fuel cells generate electricity by combining hydrogen and oxygen, with water as the only byproduct. Hydrogen fuel cells can be retrofitted to existing ships or integrated into new vessels in maritime transportation. Hydrogen fuel cells possess several benefits over other alternative fuels, including their high energy density, making them ideal for long-distance operation [42]. Moreover, hydrogen fuel cells produce no emissions other than water, contributing to reducing greenhouse gas emissions in the maritime sector. Despite these benefits, several hydrogen-related challenges need to be addressed

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before wide-scale adoption, such as the lack of robust and reliable hydrogen fueling infrastructure in coastal areas, high hydrogen production costs, fuel cell technology expenses and maintenance requirements, and technical challenges associated with smaller vessels’ space and fuel cell sensitivity to temperature and humidity [31]. Several initiatives, including the HySeas III project, have been launched to advance the development of hydrogen fuel cell technology for maritime applications. In parallel, as part of the pilot project Hydrogenesis [21]), two small passenger ships-one operating in Bristol and the other, the MF Vagen, in the harbor of Bergen-are being successfully operated, both utilizing hydrogen as their fuel source. Additionally, a noteworthy achievement has taken place in France with the unveiling of Energy observer 2, the world’s first cargo ship powered by liquid hydrogen. This vessel will commence its inaugural voyage in 2025 and it will be operated by the logistics leader CMA-CGM [31]. Ammonia Fuel Cells. Ammonia-based fuel cells are another promising technology for maritime transportation. Ammonia can be liquefied at higher temperatures and lower pressures, making it a more practical fuel source for marine vessels than hydrogen [42]. Furthermore, ammonia is more abundant, less expensive, and has a higher energy density than hydrogen, contributing to increased efficiency. Nevertheless, using ammonia as a fuel has its challenges, such as toxicity, requiring careful handling and storage to ensure safety. Burning ammonia releases nitrogen oxides, contributing to air pollution . The Viking Energy cargo ship is set to become the world’s first vessel to run on green ammonia as a fuel source, with Eidesvik Offshore leading the project [18]. Methanol Fuel Cells. Methanol fuel cells have received significant attention as a potential solution for low-emission marine transport. These fuel cells produce electricity by converting methanol into the water and carbon dioxide as byproducts. Compared to traditional combustion engines, methanol fuel cells operate more efficiently, generate fewer emissions, and operate quietly. Additionally, methanol fuel cells offer economic advantages such as lower operating costs than hydrogen and ammonia [42]. However, the lack of a reliable methanol supply chain presents significant challenges to the widespread adoption of this technology. Moreover, the production and transportation of methanol can generate greenhouse gas emissions, detracting from the technology’s environmental benefits. Lastly, the potential for methanol leakage poses safety risks for passengers and crew, making adopting methanol fuel cells subject to tight legal restrictions and regulations [19]. Nevertheless, several research and development efforts are ongoing. One such pilot project, FellowSHIP, developed the world’s first vessel to use a methanol fuel cell for propulsion, called the “Viking Lady” [13]. 2.3

Wind Propulsion Systems

Wind propulsion systems use the power of the wind to propel a ship, resulting in fuel savings and reduced emissions [17]. They come in various forms and are often used with other fuel sources, such as fossil fuels. Retrofitting existing ships to include wind propulsion systems is currently the most common way to

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implement this technology. However, wind propulsion’s effectiveness varies due to environmental conditions and onboard and commercial factors. Future ships designed with wind propulsion in mind will likely be better adapted to this technology. Unlike other zero-emission energy sources like e-methanol and hydrogen, wind propulsion systems require no new infrastructure. As an illustration, a the Danish container shipping company Maeresk implemented and employed two rotor sails on the Maersk Pelican tanker. This innovative technology resulted in an annual fuel consumption reduction of 8.2%, corresponding to approximately 1,400 tons of carbon dioxide. Furthermore, the utilization of rotor sails led to fuel cost reductions ranging from 5% to 20%, while maintaining the vessel’s operating speed [9]. Particularly noteworthy, the Maersk Pelican achieved even greater fuel savings on specific routes with favorable wind conditions, highlighting the substantial potential of rotor sails in optimizing fuel efficiency. 2.4

Solar Panels

Solar panels are a technology that converts sunlight into electricity, which can be used to power onboard systems such as lights and communication devices. They are made up of photovoltaic cells and are becoming more common in maritime transportation due to their efficiency and cost-effectiveness [35]. Excess energy generated by solar panels can be stored in batteries for later use, providing a reliable power source even when sunlight is unavailable. However, the limited space available on ships and boats can make it challenging to install large solar panel arrays, and their effectiveness can be reduced by environmental factors such as cloud cover and inclement weather. Ongoing research is being conducted to improve the efficiency and durability of solar panel technologies for use in harsh marine environments.

3

Methodology and Results

We conducted a systematic review on the utilization of renewable energies and green technologies in the maritime sector. This review was based on a comprehensive search and analysis of published studies that addressed maritime transportation planning problems in a green setting. To ensure a systematic search, analysis, and categorization of the results from previous studies, we conducted a broad literature search using a combination of search terms such as green/renewable/clean maritime planning, vessel routing, speed optimization, fleet planning, and terms describing the relevant technologies. By reviewing the titles and abstracts of the retrieved publications, we filtered the results according to relevance to the review’s aim. We then classified the publications based on the studied planning problem, the utilized energy technology, and the business sector. Furthermore, we identified articles providing more insights into integrating clean energies at the planning level for deeper analysis. The literature review covered approximately 30 studies. Figure 1 and Table 2 (in Appendix 1) summarize the results of our study. A striking observation is

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that the majority of publications focus on vessels with electric batteries, which may indicate the relative maturity of this technology compared to other green technologies. The remaining studies cover hydrogen and ammonia fuel cells, as well as wind propulsion cases. These studies mainly investigated refueling network design, vessel routing, and speed optimization problems. However, we call for more attention to contributions on the fleet size and mix problem, which can be of paramount importance.

Electric battery

Cargo shipping

67%

Passenger shipping

Ammonia fuel cell

7%

60%

30%

3% Hydrogen fuel cell

23% Military

Wind propulsion

3% 7% Service operations

(a) Green technology

(b) Maritime sector

Vessel deployment Fleet size & mix

13%

10% Vessel routing

47% Fuel network design

33%

Speed optimization

37%

(c) Planning problem

Fig. 1. Distribution of papers on green technologies in maritime transportation planning (Color figure online)

4

Discussions

In this section, we will discuss the most notable differences between green and conventional fossil fuel maritime planning problems, as already presented in the literature. We will also emphasize the impact of these differences on planning approaches and solution methods. 4.1

Fuel Network Design

Fuel network design is an important aspect of maritime transportation, involving the optimization of infrastructure for supplying fuel or power to ships within a

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specific region. This optimization includes determining the location and capacity of fuel storage facilities as well as the distribution of fuel from production sites to storage facilities. However, designing fuel networks for maritime transportation using renewable energy sources presents a significant challenge due to uncertainties related to technology advancements and volatile prices of renewable energy sources such as ammonia and hydrogen. Careful consideration of technological advancements and price uncertainties is necessary to ensure the system remains feasible in the long term and to avoid exorbitant upfront investment costs, as the rapid progress in renewable energy technologies means that the fuel network design using current technology may become obsolete soon [24]. Moreover, demand uncertainties and price differences between ports and countries significantly affect fuel network design in maritime transportation. Fluctuations in fuel demand in specific regions may require adjustments in fuel network infrastructure, such as altering the location of fueling stations or increasing/decreasing storage capacities. Additionally, price differences can influence the decisions of shipping companies regarding the choice of ports and fuels, thus affecting the demand for different types of fuel in different regions [27]. Consequently, planning and optimizing fuel network design may require considering multiple possible scenarios and potential risks associated with each option. The uncertainties involved in fuel network design problems also increase the complexity of modeling, as additional variables and parameters must be considered and incorporated into optimization models. This results in more complex mathematical formulations, longer computation times, and more challenging solution techniques. Furthermore, the modeling approach must be adaptable to accommodate changes in technology and market conditions over time. In contrast to conventional fossil fuel operations, where optimal refueling station locations are determined based on minimizing the sailing distances for vessels to refuel, renewable fuels have higher transportation costs, making it advisable to locate bunkering stations near the production sites [37]. This presents a trade-off between the distance traveled by vessels to refuel and the transportation distance of fuel. To achieve this, fuel production, distribution, and vessel routing decisions must be considered simultaneously. Additionally, renewable vessels have limited ranges, necessitating a higher number of refueling or recharging stations compared to conventional vessels [32]. These stations are necessary to ensure that vessels can operate efficiently and that their limited range does not impede their operational flexibility. 4.2

Fleet-Size and Mix Problems

The fleet size and mix problem in maritime transportation involves determining the optimal number and types of vessels needed to meet shipping and transportation demands while minimizing costs or maximizing profits [20]. Extensive research has been conducted in the scientific literature using various approaches, such as mathematical modeling, simulation, and optimization techniques. However, modeling the fleet size and mix problem under green energy sources requires

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additional considerations beyond the choice of technologies. However, when modeling the fleet size and mix problem under green energy sources, there are several important factors to consider beyond the choice of technologies. According to a recent study by [12], operating zero-emission vessels generally incurs higher costs than operating conventional vessels, and the optimal number of vessels varies between the two settings. This is due to the limited range of zero-emission vessels and the time required for charging, which necessitates a higher number of vessels to maintain the same frequency of service. Other factors influencing the fleet size and mix decisions include the suitability of different vessel types or sizes for specific routes, the availability of green technologies and supporting infrastructure, and the demand for shipping services. For instance, electric or hybrid vessels may be more suitable for short routes with frequent stops, while larger vessels with more fuel-efficient engines may be more appropriate for longer routes with fewer stops [30]. Furthermore, planning for the fleet size under a green setting must account for the intermittency of renewable energy sources, the regulatory environment, and the rapid pace of technological advancement [7]. These factors increase the complexity of the modeling approach and algorithmic design for the fleet size and mix problem. Consequently, sophisticated modeling approaches and algorithms are needed to handle uncertainty, variability, and changing conditions, which makes research in this area more challenging. 4.3

Speed Optimization Problems

The vessel speed optimization problem is an essential aspect of maritime transportation that involves finding the optimal speed of vessels to minimize costs and environmental impact while still meeting demand [23]. However, introducing renewable energy sources has brought new challenges and uncertainties to the speed optimization problem due to their variability and unpredictability caused by various factors such as weather conditions and infrastructure. For instance, wind-powered vessels may need to adjust their speed and route based on the prevailing wind conditions to optimize their energy efficiency [38]. This variability makes it quite challenging to determine the optimal vessel speed along a shipping route. Solution models need to incorporate additional variables such as weather forecasts, energy storage, and the efficiency of wind turbine technologies to optimize vessel speed in the presence of renewable energy sources. Havre et al. (2022) [12] demonstrated that electric-powered vessels must adopt varying speeds along their sailing route. This finding contradicts the common belief that consistent speed results in lower energy consumption and costs. The authors explain that electric vessels must increase their speed to compensate for the transit times of passengers or shipments, which is caused by the lower frequency of green routes and the charging times required for batteries. In other words, using green routes may result in a longer transit time, necessitating electric vessels to speed up to maintain the desired schedule. Finally, optimizing vessel speed in green maritime transportation requires integrating various

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systems and technologies, such as hybrid propulsion and energy storage. However, when these different systems and technologies are combined into a ship’s energy system, their interactions must be considered to achieve optimal vessel speed. This involves balancing the energy requirements of propulsion with the available power supply while also accounting for the limitations of the energy storage system [11,29]. Therefore, a holistic optimization approach that considers the interactions between different components of the ship’s energy system is essential. Overall, the vessel speed optimization problem differs significantly from a modeling perspective when using renewable energies, requiring shipping companies to change the planning and management of their vessel operations. 4.4

Vessel Deployment Problems

The vessel deployment problem entails the allocation of ships in a fleet to specific shipping routes or tasks while considering various constraints and objectives, such as vessel availability, customer demand, and operational costs [8]. However, additional factors must be considered when planning for vessel deployment using renewable energies. One significant challenge is the current cost and limited availability of green fuels at charging or refueling stations. This poses a major obstacle to the widespread adoption of green vessels, as demonstrated in a study by [49], which specifically focused on electric-powered vessels in the shipping industry. Furthermore, it is not economically viable to assign batterybased vessels to long-range routes due to the recurring need for charging [49]). Similarly, [39] demonstrated that battery capacity and the number of existing charging stations influence the number of electric ships deployed on a specific route. Most of the existing contributions on the green vessel deployment problem dealt with the specific case of battery-equipped vessels. Further research is, however, needed for other types of technologies. These technologies have intrinsic characteristics, which are comprehensively detailed in Sect. 2, that would induce different deployment decisions and planning practices. 4.5

Vessel Routing Problems

The vessel routing problem involves finding the most efficient and cost-effective route for a vessel from its origin to its destination while considering various constraints and objectives [8]. In the context of green energy, the routing problem for vessels introduces several key differences from the classic vessel routing problem. One major difference is that optimal sailing routes may differ from traditional routes that rely on non-renewable energy sources. For example, a vessel powered solely by wind energy may need to avoid areas with little or no wind to optimize its route [4,16]). Similarly, if a vessel is powered by a combination of solar and battery power, the optimal route may need to include areas with high levels of sunlight and consider the energy requirements for charging the batteries. Hence, the shortest or fastest route may not necessarily correspond to the optimal route. Furthermore, to minimize the impact of range limitation, vessels may need to stop several times to recharge or refuel, especially if the journey is long or if

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renewable energy sources are scarce along the route [25,33]. This can add a layer of complexity to the vessel routing problem, requiring the model to consider the vessel’s energy requirements during stops and the availability of renewable energy sources at these locations. Another key difference underlined in [26] is that adopting a butterfly (or figure-eight) shaped route structure is optimal for renewable energy-powered vessels. In a typical butterfly route, a vessel would sail in a series of loops, constituting one entire route, while making multiple stops at an intermediate port. This structure allows for using a single charging hub, which reduces the need for costly up-front investment in charging/refueling station installations. This layout is entirely different from the optimal route structure under a conventional energy setting, which may be circular or pendulum-shaped. However, such a structure may not be feasible for existing timetables and schedules, and significant changes must be implemented [26]. In summary, the vessel routing problem in the context of green energy differs from the classic vessel routing problem in several ways. It requires consideration of renewable energy sources, range limitations, and the optimal route structure. Traditional routes may also not be optimal for vessels powered by green energy sources. These differences highlight the need for new planning practices and, by necessity, new models and solution approaches to address the challenges of vessel routing in the era of green energy.

5

Conclusions

This paper investigates how planning problems differ under the green context from the conventional one by reviewing existing research on planning problems in green maritime transportation. Although the research in this field is in its early stages, we have found notable differences between planning for green and conventional maritime transportation. For instance, using green technologies like wind-assisted propulsion, batteries, and fuel cells requires different planning approaches than traditional propulsion systems. Additionally, green maritime transportation involves complex supply chains, multiple stakeholders, and regulatory requirements, further complicating planning processes. Our review highlights the need for integrating planning problems in green maritime transportation. Green technologies may require different planning approaches to reduce technological and price uncertainties. The risks caused by technological uncertainties, such as performance and reliability issues of new technologies, and price uncertainties, such as high investment and maintenance costs, require careful consideration and analysis. These differences affect the mathematical modeling of planning problems, leading to more complex models requiring new solutions and techniques. Overall, our review emphasizes the necessity for further research to advance planning practices in green maritime transportation. Developing mathematical models that can capture the unique complexities of planning problems in this context is vital. Future research should focus on developing new mathematical models and optimization strategies to address the uncertainties and risks associated with green technologies and consider the environmental impact.

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Appendix 1 Table 2. Methodological classification of the papers on green technologies in maritime transportation planning Paper

Year Technology

Sector

Planning problem Fuel network Fleet size & Speed optidesign mix mization *

Vessel deployment

Vessel routing

(Havre et al., 2022) [12]

2023 electric battery

passenger shipping

(Villa et al., 2020) [34]

2023 electric battery

passenger shipping

(Ritari et al., 2021) [25]

2021 electric battery

passenger shipping

(V´elez and Montoya, 2023 electric 2023) [32] battery

passenger shipping

*

*

(Villa et al., 2019) [33]

2019 electric battery

passenger shipping

*

*

(Rødseth et al., 2023) [26]

2023 electric battery

passenger shipping

*

(Duan and Zhang, 2022) [10]

2022 electric battery

service operations

*

(Bellingmo et al., 2021) [3]

2021 electric battery

passenger shipping

*

* *

(Gu et al., 2021) [11] 2021 electric battery

cargo shipping

(Yan et al., 2011) [43]

2021 electric battery

military operations

(Zhen et al., 2020) [49]

2020 electric battery

cargo shipping

(Zhen et al., 2022) [48]

2022 electric battery

cargo shipping

(Yin et al., 2022) [45]

2022 electric battery

cargo shipping

*

(Wang et al., 2022) [39]

2022 electric battery

cargo shipping

*

(Wang et al., 2020) [41]

2022 electric battery

service operations

(Khatami et al., 2023) [15]

2023 electric battery

cargo shipping

(Gu et al., 2021) [11] 2021 electric battery

passenger shipping

(Wang et al., 2023) [40]

2023 electric battery

cargo shipping

(Sundvor et al., 2021) [30]

2021 electric battery

passenger shipping

(Sun et al., 2023) [29]

2023 electric battery

cargo shipping

*

*

*

*

*

*

*

*

* *

* *

*

* *

*

* * *

*

(Wang et al., 2023a) 2023 ammonia [37] fuel cell

cargo shipping

*

(Wang et al., 2023b) 2023 ammonia [36] fuel cell ˆ adlerov´ (St´ a and 2022 hydrogen Sch¨ utz, 2021) [27] fuel cell

cargo shipping

*

cargo shipping

*

(Bentin et al., 2016) [5]

2016 wind propulsion

cargo shipping

*

(Li and Qiao, 2019) [16]

2019 wind propulsion

cargo shipping

*

(Yuankui et al., 2014) [47]

2014 wind propulsion

cargo shipping

*

(Yingjun et al., 2013) [46]

2013 wind propulsion

cargo shipping

*

(Yuankui et al., 2014) [47]

2014 wind propulsion

cargo shipping

(Wang et al., 2022) [38]

2022 wind propulsion

cargo shipping

(Chica et al., 2023) [7]

2023 wind propulsion

cargo shipping

*

* * *

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A Framework for Enabling Manufacturing Flexibility and Optimizing Industrial Demand Response Services Paul Kengfai Wan1(B) , Matteo Ranaboldo2 , Alessandro Burgio3 Chiara Caccamo4 , and Giuseppe Fragapane1

,

1 SINTEF Manufacturing, Trondheim, Norway

[email protected]

2 CITCEA-UPC, Universitat Politècnica de Catalunya, Barcelona, Spain 3 Technological development, Evolvere SpA Benefit Company, 20124 Milan, Italy 4 SINTEF Energy Research, Trondheim, Norway

Abstract. The energy industry is experiencing significant changes in terms of sustainability and competition, primarily driven by the introduction of renewable energy targets and emission limits. Demand response is a potential solution to reduce the critical peak; however, its implementation in industries can be challenging due to their production requirements. Technology enablers such as digital twin technology can enhance energy flexibility and optimize manufacturing and service processes. In this study, we aim to develop a framework that can help the manufacturing industry to optimise industrial demand response services and achieve a seamless interaction of different layers such as the physical, data infrastructure, digital twin, management, and aggregator. A systematic literature review and workshops were conducted to identify key technologies, decision areas and methods to enable both manufacturing and energy flexibility to reach demand response. Based on the results, an energy-flexible framework for manufacturing industries was developed. Keywords: Digital Twin · Energy flexibility · Manufacturing flexibility · Demand response

1 Introduction The EU Green Deal sets the EU on the path to a green transition which aims to reach climate neutrality by 2050 [1]. Along with the shortage of natural resources [2], this exerts pressure on the manufacturing industries, particularly on the high energy intensity industries like glass and steel, to reduce carbon emissions. This is because most of them still heavily depend on fossil fuel-based energy sources. For example, the industrial sector in Germany consumes 44% of the total electricity [3]. Therefore, greener energy transition efforts are increasingly necessary to reduce negative environmental impacts [4]. © IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 634–649, 2023. https://doi.org/10.1007/978-3-031-43688-8_44

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In energy demand response, customers dynamically change their electricity consumption behaviour in response to time-of-use electricity price signals or real-time dispatching instructions to reduce critical-peak demand [5]. However, this approach can be challenging for industries like steel production, as it requires to heat up and maintain production at a high temperature. At present, the research on demand response mainly focuses on the traditional demand response in power systems, while the research in the analytical technique and evaluation method is not comprehensive enough and fail to consider from an industrial perspective [5]. Digital twin (DT) can provide ideas for solving the above problem through forming a one-to-one mapping between the physical and virtual layers and then optimizing manufacturing and service processes [6, 7]. The increasing application of Internet of Things (IoT) utilized in the manufacturing sector which generated a massive amount of data [8], which are useful for product lifecycle monitoring and maintenance, which are crucial tasks in manufacturing, aim to detect production exceptions and ensure normal task execution [9]. These technologies can enhance energy and production flexibility, but the actual implementation in the industries still faces problems and barriers like data integration [10] and the lack of industrial knowledge. A common challenge with the existing framework is inability to provide better decision support for industries when it comes to energy management. Therefore, there is a need for a holistic framework which can not only improve the decision-making process but a to reach the long-term goals of energy efficiency. In this work, we aim to develop a framework which considered various key requirements and enabling technologies which can help the manufacturing industry to better implement them into their systems. In order to develop a feasible and adequate framework, we combine both systematic literature review to obtain a clearer overview of the current state of the art of energy systems and workshops with industry experts to provide actual industry practice to make our framework more robust. The following research questions will guide this study: 1. What are the key enabling technologies, decision areas, and methods for implementing industrial demand response in a manufacturing environment? 2. How can industrial demand response be applied and optimized in a manufacturing environment? The structure of this paper is as follows: Sect. 2 presents a theoretical background of industrial demand response. The research methodology for the systematic literature review (SLR), workshops, and case study is outlined in Sect. 3. The results of the SLR are presented in Sect. 4, followed by the introduction of a novel framework in Sect. 5. Section 6 discusses the results and limitations of industrial demand response and concludes the paper in Sect. 7.

2 Industrial Demand Response Flexibility on the demand side is an important resource to address the flexibility gap in the power grid caused by the rise of variable renewable energy sources [11, 12]. Demandside flexibility involves strategies aimed at adjusting end-user electricity consumption,

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typically achieved through energy efficiency measures and demand response programs. [11]. Demand response is defined as “changes in electric usage by end-use customers from their normal consumption patterns in response to changes in the price of electricity over time or incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized” [13]. Demand response strategies are gaining attention in power system operations as they can reduce peak load, defer infrastructure costs, enable active participation of consumers in grid operations, and enhance the efficiency, reliability, and safety of the power system. [14, 15]. As the industrial sector holds a significant portion of the cost-effective demand response potential [16], industrial Demand Response stands out as a highly promising solution for unlocking the potential of demand-side flexibility [17]. Successful implementation examples have been documented across various industrial sectors. However, barriers such as regulations, information and technology infrastructure requirements, production disruption risks, limited knowledge, and social acceptance still hinder the adoption of demand response programs and strategies in industries [18–20]. Different resources are currently available to support the effective implementation of industrial demand response, such as tools for defining energy-aware scheduling and planning of manufacturing systems, as well as aggregators and the use of DT [21–26]. Flexibility market regulations are also evolving rapidly to facilitate demand response adoption. However, additional research is necessary to overcome the remaining challenges [18] and can provide valuable insights into the implementation and outcomes of various industrial demand response programs.

3 Methods To develop a manufacturing and energy flexible framework for industrial demand response, a two-step research methodology is proposed. Firstly, a systematic literature review will be conducted to investigate the current state-of-the-art at the intersection of manufacturing, energy, and digitalization. This will assist in creating an initial version of the framework. Secondly, inputs from industrial experts will be gathered through workshops to refine the framework and make it more applicable to an industrial setting. 3.1 Systematic Literature Review The goal of a systematic literature review is to facilitate theory development, align existing research, and discover areas where additional research is needed [27]. The systematic literature review was conducted using Scopus and Web of Science to provide wide coverage of published literature. The reporting of this review was guided by PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-analysis extension for Scoping Reviews) [28]. To identify relevant literature, the search was performed on “Title, abstract and keywords” with Terms listed in Table 1. In our search, we focused on peer-reviewed articles and conference proceedings to provide a wider overview of current digital tools used to achieve industrial demand response of energy systems. Only publications in English were considered. No-full paper

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Table 1. Keywords used in the SLR Manufacturing

Energy

Digitalization

Production

Aggregator

Digital twin

Manufacturing

Demand response

Cloud computing

Industry

Smart grid

Digitalization

Fig. 1. Systematic literature review process flow.

and posters are excluded in our search. A total of 25 articles were included in the final review. The SLR process flow is summarized in Fig. 1 and the results are presented in Sect. 4. 3.2 Workshops To enhance the systematic literature review and address its limitations, several workshops were conducted. These workshops involved participants from both academia and industry, with diverse roles including researchers, consultants, IT experts, production leaders, and engineers. During the initial workshop, the results of the literature review were presented and discussed among the experts. Based on these discussions, the main

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layers and connections of the manufacturing and energy flexible framework for industrial demand response were determined. The participants were then divided into smaller focus groups to delve deeper into the main functions, activities, and decisions required at each layer. A total of five workshops, including one for the main structure and one for each layer, were conducted to develop an industrial-ready framework for demand response based on the literature review. The final framework is presented in Sect. 5.

4 Literature Review Results The following section presents an overview of key enabling technologies and applied decision areas that are utilized to enable and operate demand response. A summary of the collected papers is provided in Table 2. From the collected papers, smart grid systems with communication technology have been highlighted as a key enabler for industrial demand response which can provide stable, efficient, scalable, and cleaner electrical energy system [29, 37, 48, 53, 54]. Abdulsalam et al., [29] concluded that iMax is the most suitable for advancing metering in smart grids, followed by Zigbee; while Power Line Communication is the least suitable. Smart grids can generate different types of data, from energy generation to consumption, and can move from silo systems to integrated networks for data analysis to improve operational efficiency [30]. Moreover, the DT depends on communication technologies to efficiently manage devices in the system [41]. With the adoption of different data communication tools, cybersecurity of the energy system must be considered to prevent any malicious activities such as hacking. Lei et al., [40] proposed a chain of defence concept using reinforcement learning framework to empower the system operator to incorporate existing cyber protections and strategy in a more dynamic, adaptive, and flexible ways to enhance cyber-resilience. Chen et al., [53] proposed a privacy protection strategy via edge computing, data prediction strategy, and pre-processing to overcome the drawbacks of the current cloud computing system. Blockchain can also be adopted in the energy system as an additional security layer. The data-storage structure of blockchain enables energy tracing and prevents data tampering [32, 55]. This is because any form of data tampering can alter the data analysis for forecasting. Forecasting and predicting are the main decision areas in the demand response to improve energy efficiency by flattening the daily energy demand level [56]. By constructing digital twins of an integrated energy system, the manufacturing industry can benefit from its capabilities to improve coordination among various energy converters, hence enhancing energy efficiency, cost savings and carbon emission reduction [47]. For example, Ye et al., [31] demonstrate that digital twin forecasts of the renewal energy and load of both wind and solar energy were closely matched to the actual values. [47] trained a deep neural network to make statistical cost-saving scheduling by historical forecasting errors and day-ahead forecasts, and the proposed methods can reduce operating costs by 65%. Forecasting the demand for energy usage is critical for better energy management where the industry can better coordinate with the production schedule [35]. In our search, most of the collected work only focuses on the energy perspective. Tomat et al., [57]

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Table 2. Summary of the collected papers Author Key enabling technology

Decision areas

Methods

[29]

WiMAX communication technology is the most preferred

Communication

ZigBee, Multi-criteria decision making

[30]

Highlighted operational data generated during the building life cycle are essential for realizing the energy-efficient operation

Data processing and information sharing

Data-driven deep learning, physical model-driven

[31]

Traditional equipment efficiency correction models only consider the historical load factors and variations in the environmental factors

Energy load prediction and visualization

Digital twin models, visualization models, polynomial regression, back propagation neural network

[32]

Framework using blockchain as an addition of a security layer

Cybersecurity

Copula model

[33]

Reinforced Learning algorithms select the optimum battery planning measures based on forecasts of wind power and photovoltaic availability

Data visualization, forecasting

Multi-criteria decisions via an individual user

[34]

Deep learning layout that uses generative adversarial networks (GAN) to forecast the hourly power generation

Forecasting

Reinforced learning

[35]

Machine learning algorithms for forecasting, storage optimisation, energy management systems, power stability and quality, security, and energy transactions

Optimising energy scheduling

Machine learning

(continued)

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Author Key enabling technology

Decision areas

Methods

[36]

Integrated digital twin and Data processing and big data provides key integration, prediction technologies for data acquisition (such as sensor, Bluetooth, and WIFI for data communication) in energy-intensive production environments

Data mining algorithm, Big data analyses

[37]

The importance of the Decentralized energy transformation from a system traditional centralized energy system to a decentralized one using IoT, smart grid, blockchain, fog computing

Decentralized decision making

[38]

AI has been applied for network provision, forecasting (weather and energy demand), routing, maintenance and security, and network quality management

Forecasting

ANN, fuzzy logic, SVMS and genetic algorithms

[39]

Energy systems are no longer passive and uni-directional but active and bi-directional with end-users taking active roles in the operation and management of the energy system

Energy demand based on user behaviour

Distributed Energy optimization method

[40]

Developed a novel Cybersecurity approach to identify critical branches to strengthen and shield the smart-grid power system threats

[41]

Developed uncertainty modelling approaches for optimization problems under uncertainty for circumventing the impact of ambiguous parameters

Markov Decision Process Model

Operation and technical Deterministic model uncertainties in the energy grid

(continued)

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Table 2. (continued) Author Key enabling technology

Decision areas

Methods

[42]

The architecture can be used to reproduce any functional plant with minimal cost and which is scalable

Communication

Cybersecurity testing, research, and education

[43]

Proposed a unified Hypervision scheme based on structured decision-making concepts, providing operators with proactive, collaborative, and effective decision support

Data management, security

Human-centered design approaches

[44]

Proposed an energy behaviour simulation in equipment digital twin model

Energy demand management

Data-driven hybrid Petri-net, Gaussian kernel extreme learning machine

[45]

Highlighted digital technologies can make modern power systems more effective, reliable, secure, and cost-effective

Energy demand management

Markov model and clustering algorithms, SVM-based technique

[46]

Concluded AI-initiated Energy demand learning processes by using management digital twins as training environments can enhance buildings’ adaptability

Building-integrated AI, reinforcement learning

[47]

The proposed DT-based method can reduce the operating cost of IES by 63.5%, compared to the existing forecast-based scheduling methods

Scheduling, forecasting

Deep neural network, multi-vector energy system

[48]

Investigated how blockchain and IoT together can improve existing smart grid ecosystem toward facilitation of better monitoring services

Energy management and load control

Energy load control

(continued)

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Author Key enabling technology

Decision areas

Methods

[49]

Presented new empirical Energy demand evidence to validate management data-driven twin technologies as novel ways of implementing consumer-oriented demand-side management

DNN, ordinary differential equation, linear autoregression, Linear regression, Naïve model, predictive analytics model

[50]

Highlighted that government should invest in the development of AI

Various AI models

[51]

Proposed the use of the Contracting and Services Open Automated Demand Response standard protocol in combination with a Decentralized Permissioned Market Place based on Blockchain

Simulation modelling

[52]

Formulated a lumped model for forecasting the rate at which electricity is consumed with inadequate real-time energy data

Forecasting

Lumped model for forecasting

[53]

Propose an IoT-based privacy protection strategy via edge computing, data prediction strategy

Cybersecurity and prediction

Numerical simulations, edge computing system

Energy demand management

highlighted that user behaviour can have a critical impact on demand response effectiveness. Lee and Yim [58] concluded that having a clear understanding of on-demand behaviour can enable an efficient operation of energy supply. Similarly, for the manufacturing sector, a better integration of production scheduling and planning management with energy management is important to enable optimizing industrial demand response services. For example, steel production requires a high amount of energy, often fossil fuel to bring the heat up to and must continue even though the cost of energy has gone up during operation. From the literature review, the published frameworks identified mainly focus on the interaction between the smart grid and aggregator layers or have a strong energy demand management focus in industrial cases. However, a holistic integration of the methods and technology integration is still missing [38].

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5 Framework for Industrial Demand Response Services To enable demand response services, consistent and seamless interaction between the physical, data infrastructure, DT, management, and aggregator layers is essential. Through the use of an SLR and workshops with experts, the crucial activities and communication structure required for industrial demand response services have been identified and integrated into a framework. Throughout the workshops with experts from the aggregator, energy, digitalization, and manufacturing sides, all agreed that the current frameworks in literature lack providing a comprehensive framework that allows for implementation in industrial companies and for layers to be connected and communicated from the physical to the aggregator layer. The SLR results (Sect. 4) emphasize the importance of smart grid systems in enabling industrial demand response, which can help create a stable, efficient, scalable, and cleaner electrical energy system. As a result, this framework focuses specifically on the key activities relevant to aggregators and industrial manufacturing companies. Both the literature and expert group have recognized the general DT framework as suitable for representing the entire communication line with critical activities for industrial demand response services. This framework is visualized in Fig. 2 and described in the following. Manufacturing companies can find interruptions or significant reductions in production difficult to manage. To provide demand response capabilities that are attractive, it is advantageous to have local renewable energy and storage systems. These systems can be highly effective in allowing production to continue while simultaneously providing demand services to reduce energy peaks in the grid. However, these systems must be properly managed to interact with production and the aggregator at the right time. Therefore, it is essential to establish an end-to-end data infrastructure. Data plays a critical role in identifying high-energy consumers in production and understanding energy reduction capabilities. Appropriate sensors and meters need to be selected and applied to identify high-energy consumers in manufacturing, and machine data needs to be extracted. In the digital layer, which includes the data infrastructure layer, it is important to define data collection, interfaces, data structure, and data storage to ensure consistency, interoperability, and system robustness. The data must be processed (e.g., data cleaning, fusion, etc.) to enable data-driven simulation and optimizations. In the DT layer, various DTs need to be established and interact to reflect both manufacturing and energy flow processes. Their detailed representation allows for simulating and optimizing complex manufacturing processes, with a focus on energy factors, to provide a baseline and different scenarios for energy consumption profiles. The results of simulation and optimization must be presented and visualized for decision-making, which occurs in the management layer. The management layer focuses on establishing a manufacturing and energy system for flexible and adaptive consumption profiles. Decision-making in this layer ranges from strategic to operational levels. Production needs to identify its manufacturing flexibility in times of production scheduling, while energy needs to integrate renewable energy and storage systems and drive effective demand-side management. The aggregator agent that provides demand response services connects many different manufacturing companies, usually through a platform. The aggregator agent performs forecasting of energy demands and supplies to identify potential energy gaps and provide flexibility

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Fig. 2. Framework for industrial demand response services

to the grid. By communicating with manufacturing companies and exchanging possible consumption profiles, the aggregator can optimize the cluster and provide incentives back to the manufacturing companies to encourage them to provide energy flexibility to the grid.

6 Discussion The introduced framework aims for a consistent and seamless interaction between the physical, data infrastructure, DT, management, and aggregator layers. This is essential for all types of manufacturing industries, particularly high energy intensive industry, to enable a more flexible demand response. The central part of the framework for industrial demand response is the identification, optimization, and adaption of energy consumption profiles of manufacturing processes. The data-driven models with real-time and historical production data enable the identification of different energy consumption down to the product, machine, and process levels. This allows moving away from aggregated energy consumption data and supports the reduction of complexity in the interplay between manufacturing processes and energy consumption. The interplay between manufacturing

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and energy management and the aggregator agent is crucial to develop schedules that meet demand and orders and, on the other hand, improve energy consumption to reduce energy prices and CO2 emissions. Real-time communication with manufacturers and aggregator needs to enable cluster optimization and allow the manufacturer to change their manufacturing schedules in time. The energy industry is going through significant changes in terms of sustainability and competition, with the introduction of renewable energy targets and emission limits. One potential solution to balance the power supply during periods of over- and undersupply from high levels of uncertain, renewable generation is through demand response. The framework can help balance fluctuating power supply, but it requires accurate control and market frameworks to optimize the use of this geographically distributed resource. Moreover, it can support the replacement of the traditional model of large and centralized generators operating within a monopoly with vertically integrated systems that enable competitive marketplaces. However, the development of complex models of electrical demand is necessary at both the component and system levels to accurately represent the highly diverse, dynamic, and uncertain nature of demand, as well as the complexities of end-user interaction with the system. The presented framework serves as a useful guideline for industries seeking to improve the flexibility of their industrial demand response. However, it is important to note that the framework has limitations. While it primarily focuses on production scheduling and energy strategy, it is important to consider deeper level components such as changes in the human workforce, disruption of the supply chain, and geopolitical factors to build a more robust and resilient energy system for the industry. Furthermore, the framework has only been tested in the steel industry and large-scale companies. To implement this framework successfully, a high level of digital infrastructure, particularly the data infrastructure layer, is required. The availability and accessibility of different types of data are crucial for the digital twin process to simulate and optimize a process or production system, which requires sensors to be installed at desired locations. Therefore, while larger manufacturing firms may find this framework more readily applicable due to their greater resources, SMEs may face more challenges in its implementation.

7 Conclusion This study aimed to address the current limitations and enhance the understanding of the interplay between manufacturing and energy industries and reduce the complexity for demand response. The developed framework creates an end-to-end communication and data sharing between the management of the manufacturing sites and the aggregator providing demand response services. Data-driven models and simulations based on DT can help manufacturing industry to identify a variety of possible energy consumption profiles for different manufacturing schedules to meet demand and orders. This framework can service a guideline for manufacturing sector to cope with the changes in the energy sector but the level of digital infrastructure the manufacturer can be the main limitation of the for a successful implantation. Future research should focus on expanding the framework and identifying additional key decision areas and decision-making methods. Furthermore, it should be tested in

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various industries beyond steel and small and medium enterprises, and their feedback should be used to improve the framework. Acknowledgment. The research described in this paper is supported by funding from the FLEX4FACT (Grant agreement ID: 101058657). The authors would like to thank the European Commission and the companies’ respondents that made it possible to carry out this study.

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Discrete Event Simulation for Improving the Performance of Manufacturing Systems: A Case Study for Renewable Energy Sources Production Panagiotis Mavrothalassitis, Nikolaos Nikolakis, and Kosmas Alexopoulos(B) Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, 26504 Patras, Greece [email protected]

Abstract. Nowadays manufacturers have highly sophisticated and reliable inhouse logistics systems that support production lines, with a primary focus on increasing system efficiency. For decision-makers to get the desired efficiency level, tools that help identify the improved set of operations are necessary. The use of simulation to evaluate a manufacturing system design and performance can explore this need. In this work, a Discrete Event Simulation (DES) model is presented for evaluating the current production layout and identifying potential areas for improvement for a renewable energy sources production system. Production scenarios with different shop-floor setups were explored and simulation runs were realized. To evaluate the efficiency of each of the shop-floor setup variation, resulted KPIs, such as makespan and resources utilization for different workloads, were detailed. Results showed that small labor and machines adjustments, as well as a more flexible process plan, lead to significant productivity enhancement. Keywords: Simulation of Production & Supply Chain Systems · Production Design Optimization · Discrete Event Simulation · Renewable Energy Sources

1 Introduction Developing strategies and addressing challenges and opportunities associated with digital transformation are necessary for the strategic plan of almost all companies. Since this transformation is global and for the companies to adapt to this new industrial era, a fundamental upgrade to their operations, as well as the exploration for new opportunities that will enhance their productivity and performance, are essential [1]. Especially for SMEs related with renewable energy sources production, the digital transformation is inevitable, if they want to compete with the big companies. Such production systems want to enhance productivity by upgrading the production environment, since until recent years, the renewable energy sources sector was not a thing. As more and more attention is gained, the use of technology will enable businesses to meet changing client needs © IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 650–665, 2023. https://doi.org/10.1007/978-3-031-43688-8_45

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and survive competition [2]. Hence, companies need proper management of the digital turnaround, to have the opportunity to access operational and productive advantages and, as a result, it allows them to compete with better efficiency. One of the most significant first steps in a company’s transformation path might be considered a test-before-invest safe and cost-effective method, which includes analysis, development, test, and validation of the manufacturing processes utilizing digital simulation tools [3]. Discrete Event Simulation (DES) is a widely used techniques for analyzing and understanding the dynamics of manufacturing systems [4]. However, the use of DES for modelling a production system has to follow a concrete information structure [5]. If there is no such structure, a use of a flexible methodology is mandatory. In [6] authors proposed a method for simulation with the use of fuzzy parameters. Nevertheless, since then there is a gap in addressing well defined methodology steps for the use of DES to test and verify a manufacturing system. Additionally, in the era of automation, a predecessor methodology for the design of manufacturing systems towards their manufacturing upgrade, would support the efforts for an automated production design process, with the use of Industry4.0 techniques. DES is a highly flexible tool which enables companies to evaluate different alternatives of system configurations and operating strategies to support decision making in the manufacturing context. The general performance of manufacturing systems is affected by several critical aspects, including facility design and production plan optimization. The arrangement of machines and departments inside a facility is referred to as facility layout design, and it may significantly affect how productive and efficient manufacturing activities are. Several researchers have applied simulation in various production optimization and facility layout design problems [7]. The development of a simulation model for in-house logistics operations for an automotive plant was carried out by authors in past years [8]. Different scenarios were studied to analyse the potential for production support improvement. Results have shown that through intelligent carrier robots’ efficiency can be enhanced with less resources needed. Resources utilization, buffers capacity and production makespan are the main KPIs for addressing challenges and pain points in a production system. Simulation can be applied in the industrial setting and allows for the learning and testing of the system’s behaviour. DES offers advantages that may be attained with a variety of system configurations in addition to being a secure and quick examination tool. Simulation can be used to study and compare alternative designs or to troubleshoot existing systems. Modelling provides a safe way to test and explore different “what-if” scenarios [9, 10]. The effect of changing staffing levels in a plant may be seen without putting production at risk. This gives the ability to make the right decision before making real-world changes. With simulation models, how an existing system might perform if altered could be explored, or how a new system might behave before the prototype is even completed [11]. Most organizations that simulate manufacturing systems use a simulation software, rather than a general-purpose programming language. By using a simulation software, the programming time is significantly reduced as the software provides greater flexibility towards tasks like generating random numbers from a probability distribution, advancing simulation time, determining the next event, collecting and analyzing data, reporting the results etc. The two most common criteria for selecting simulation method are modelling

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flexibility (ability to model any system regardless of its complexity or uniqueness) and ease of use. For this work, Discrete Event Simulation has been selected to represent a renewable energy production shop-floor and processes to produce hydrogen reactors. Most of the renewable energy production systems have not yet automated their operations. Currently, the majority of the operation performed in such systems is manual. This means that technicians carry out the whole process plan. Hence, the automation factor is not much present in such systems. With the identification of pain points in a production system with the use of simulation is an indirect method leading to sustainability. Purposeful changes can make the difference and lead to a more sustainable environment, that meets the standards. The objective of the paper is to propose a methodology for the design of a manufacturing system using DES and its practical implementation upon a realistic industrial case to support decision making. As a use case, the system under study is from the renewable energy sources production. Such a production system includes many manual operations and the effective coordination of the processes operators, as well as the upgrade of certain production resources, is the main goal of such production systems to enhance productivity. To address potential shop-floor or process plan adjustments, research and evaluation of the options that are accessible for a system that produces renewable energy sources, by utilizing a DES model, through a well-defined methodology for the implementation of simulation in this test-before-invest approach is necessary. Moreover, the methodology proposed can be implemented in various manufacturing systems, since the key to simulating a production environment is the correct and proper modelling of the production entities. In many production systems the know how for different processes and the plan on its whole is based on the production manager. Thus, to develop a representative simulation model can be tricky and most of the times inaccurate. Through a coherent collaboration with the production manager and the study of files that contain the whole production system key information for the development of the model, investigation of potential improvements is carried out in relation to critical factors, such as production makespan, time waste, and resources and equipment efficiency, through the targeted manufacturing simulation study.

2 Methodology The performance and flexibility of a production system can be evaluated either by analysing data captured from the actual, physical system or from its simulated counterpart. However, evaluating possible facility layouts and measuring their performance be a time consuming and costly procedure. This problem can be addressed with the use of simulation. In order to use a simulation approach to extract KPIs, there should be a certain methodology with discrete steps that must be followed (Fig. 1). The methodology proposed combines ideas from previous research works and aims at providing a concrete roadmap for the design of a manufacturing system with the use of a DES software. In [12], authors proposed a methodology that uses DES done by hand for an example scenario. The technique proposed includes modelling concepts that need to be realized for the correct development of the simulation model. However, some of the concepts described in the methodology may be optional for the design of a manufacturing system,

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since this process needs some degree of freedom. The design of a manufacturing system is a process that may lead in a non-expected but desired performance, which may not be possible if all system variables and model parameters are defined in a stricter manner. Hence, there should be a correct balance between the flexibility of the simulation model and the correct formulation of the problem, which is a twofold objective. In [13] authors propose an input data management method for realizing a DES model. The activities conducted to gather the required data is one of the most important points in the design of a system. The methodology proposed, though, does not provide a use case implementation. Thus, the present work identifies the positive impact and methodology steps proposed in the aforementioned works to propose an overall methodology and implementation for a renewable energy production system, while it goes one step further, with an analysis example that could support the decision making process. As a first step, the identification of the shop-floor view could provide an initial idea of the simulation model that will be developed. The machines, warehouses, buffers and operators are typical resources in a production system. After that, a discussion with the production manager is mandatory to address the process plan and the materials used. The process plan can also be addressed in different file formats. Nevertheless, the use of a file format depends on the simulation software that will be used. Thus, before the use of a file with this information included, a more generic process plan schema should be developed, as an output of the discussion with the manager. Next is to create the input for the simulation, as well as the simulation model itself. The input could either be parameterized from the existing form to the one utilized by the simulation, or to be created from scratch, after the discussion with the production manager and the process plan schema creation.

Fig. 1. Methodology for the design of a production system with a simulation approach

When all the steps have been executed, there should be simulation model created for validating the proper operation of the production systems in its current state. The validation of the model structure and performance should be performed in collaboration with the production manager. With the current simulation model as a base, realistic production scenarios should be clearly defined and then tested in the simulation environment. These scenarios could be identified and defined in collaboration with the production manager. The simulation runs for each scenario will then be executed and production KPIs could then be gathered and used for a comparative analysis.

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3 Case Study The industrial use case under investigation is from the field of renewable energy sources production and is referring to the production of a reactor. The selected industrial environment is not fully exploited, thus a test-before-invest solution such as the DES approach seems almost inevitable, for a company’s digital transformation journey, towards a more resilient, productive and efficient workplace. 3.1 Methodology Implementation with the Use of DES To produce the reactor certain working steps must be followed. Generally, the sequence of operations is static. However, by integrating DES Software [14] one can discover more flexible and dynamic ways to produce the reactor. Specifying the constraints, such as dispatching rules, pre and post conditions, operators’ allocation, is one of the most important steps for the functionality of the model to correspond to the real production steps of the reactor. Although the functions are well defined, the simulation model can perform even under different circumstances and scenarios. This is achieved by implementing such a framework, where the simulation is running with respect to the user’s input, regarding information about processing times, operators, tasks sequence, machines suitability etc. This offers the opportunity to interact with the model, check various combinations to produce the reactor and observe performance. In addition, the user can not only experiment with the model’s input, but he or she can experiment with the simulation’s elements themselves. As it emerges, further adjustability of the simulation model to the needs of the resulted production system can be recognized by the fact, that the user can easily change the number or certain characteristics of the simulation’s elements (resources, buffers, operators) and perform simulations runs to observe the performance. Regarding the input, this file contains almost all the necessary information for the reactor’s production. Every single part is encoded with a unique “product id”. Other than this id, another number is assigned to each part, the “group id”, referring to the part that will be produced, after all the parts of unique group are assembled or welded. Also, this file contains a table of procedures and their corresponding processing times. As we already mentioned, these are some fields the user can input a value. For example, the manager wants to check how the whole production will be influenced by varying specific processing times. Additionally, another table is included in this file, containing all the information about process and resource suitability, operators’ allocation, preand post-conditions and working steps for each individual process. This file contains the essential info about the reactor’s production procedures. If one wants to change the above-mentioned parameter to run the simulation for a different scenario, he or she can do it manually by inserting the desired value in the appropriate table’s cell in the input file. Lastly, the user can insert the desired volume of reactors he wants the simulation to run for, which is an aspect that adds one more level of flexibility to the simulation model. 3.2 Renewable Energy Sources Production Specification The processes to produce the reactor are typical manufacturing processes such as cutting, drilling, welding and assembling. Sub-assemblies – mostly referring to metal parts, e.g.,

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pipes - get through certain processes before in-between welding and assembly of the parts that in the last working steps, operators weld and assemble into the final product. More precisely, some of the parts are raw materials that are subject to processing from the shop-floor resources immediately and some others arrive in the warehouse from external sources, due to the absence of purpose-built equipment. Machines that support every individual manufacturing process on the shop-floor are grouped in workstations, where a buffer is installed next to each one, to stock the parts that have just finished relevant processing. This structure, with separated workstations, is based on the physical characteristics of each process. The current facility design is shown in Fig. 2. Eight workstations are included in this simulation model. As mentioned, each one can be matched to a manufacturing process. The workstations are used for cleaning, cutting, prefabrication, machining, drilling, quality check, welding and assembly. There are two storage spaces, the warehouse where the outsourcing material is stored (e.g., laser cutting) and the storage where the final product is stored. Every workstation contains a certain number of machines, and every machine can perform a specific type of task. A part is inserted into a machine, with respect to the production working steps regarding material processing, and when a process of its sequence is completed, the part is stocked in the corresponding buffer. Nevertheless, currently the shop-floor has no fully automated machines, hence, in most cases an operator is necessary to operate the machine. There are two types of operators, experienced and assistant technicians. For the purposes of the reactor production, processes such as cutting, drilling, machining and welding are taking place. For these types of operations, a combination of tools and machines is used, such as angle grinders for cutting machines, drilling machines for drillings etc. However, these machines are not autonomous, hence, a human must be allocated for a process to complete. Operators are responsible for carrying out the production’s tasks, by using the available equipment e.g., angle grinders, drilling machines, lathe etc. Each operator is qualified for executing specific manufacturing processes. The well-structured cooperation of experienced technicians and assistant technicians is a key factor for reaching high performance standards. The necessary manufacturing processes for the preparation of the individual parts are sheet metals and tubes cutting, drilling, cleaning and final cutting and drilling. The next step is a pre-assembly of the main body parts during which custom-made matrices and support bases are used for parts alignment and fine-tuning placement before spot welding, followed by the full welding assembly process of the main body and of the peripherals. Finally, a leak test is performed with pressurized air. Currently, the manufacturing of the reactor involves a lot of manual tasks and the usage of some basic mechanical equipment for the completion of the mentioned actions.

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Fig. 2. Facility layout design

4 Experiments In order to formulate the experiments and address the system’s potential performance, a discussion with the production manager regarding the potential production flexibility options was held. The output of this was the shop-floor and process plan variables that could be tested with different values in the simulation model. These can be seen in Table 1. The process plan to produce the reactor consists of a three-level procedure. The first level refers to the sub-assemblies that need to be produced and come to their final form, before the final assembly. The second level refers to the components of the subassemblies that need to be processed before the sub-assemblies’ final form. The third level refers to the actual processing task of a material, part or sub-assembly. In order to experiment with the different viable variations of the process plan, without violating the production procedures and constraints, a process plan input was used, with no stricter pre and post conditions regarding the sub-processes of a level. The shop-floor, as already mentioned, consists of machines and operators. In the current production system, machines require the corresponding operator to complete a production processing task. Thus, a variety of shop floor machines and operators’ volume can be another variable. The last entity that will serve as a variable is the workload volume. The workload is considered as the number of reactors to be produced. An experiment scenario consists of a simulation model for specific shop floor layout and process plan. For this workplace layout, different workloads are simulated, to evaluate production efficiency. This is considered as one experiment and the current facility design is evaluated. The scenarios defined and tested are described in the following section.

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Table 1. Production parameters to formulate experiments. Production entity

Variable description

Process plan

No restrictions in a process plan level

Machines

Quantity and processing time

Workers

Quantity

Workload

Quantity

4.1 Scenarios In order to formulate the production scenarios, the parameters’ values should be defined. Regarding the process plan a more flexible plan was formulated, with respect to the current process plan and production constraints. More specifically, second level tasks can start before finishing the third level tasks, without violating the production constraints (Fig. 3). The shop floor variations (Figs. 4 and 5) refer to the different available machines. The operators (experienced and assistant) volumes are variables as well. Table 2. Scenarios and simulation runs parameters values Scenario id

Shop-floor variation

Operators (quantity) Experienced

Assistant

Workload (reactors to be produced)

1

VAR 1

1

1

1–4

2

VAR 1

1

2

1–4

3

VAR 1

2

1

1–4

4

VAR 1

2

2

1–4

5

VAR 2

1

2

1–4

6

VAR 2

2

1

1–4

7

VAR 2

2

2

1–4

8

VAR 2

3

2

1–4

The workload volume variations refer to the number of reactors to be produced in a single production run, with the above shop-floor variations utilized. The specific simulation runs scenarios are shown in Table 2.

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Fig. 3. Reactor production process plan

Fig. 4. Shop floor setup variation 1

4.2 Results and Discussion To evaluate the production scenarios and shop floor variations, the results of the simulation runs should be translated into KPIs. This means that for each entity on the simulated shop-floor the corresponding measurements should be studied. In Table 3 the KPIs that should be studied and used for the comparative analysis are presented. The DES software gives the opportunity to address more KPIs than the ones presented in Table 3. However, the KPIs that are studied in this work are the ones that are more likely to provide a better understanding of each production variation scenario. In addition, since there are many machines and buffers on the shop-floor, only the KPIs of the key production entities will be presented. The key production entities are both operators’ categories (Table 2), the machine types that had different units in the shop floor variations

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Fig. 5. Shop floor setup variation 2

Table 3. Shop-floor KPIs under investigation Production entity

KPI

Machines

Utilization (%)

Operators

Utilization (%)

Buffers

Waiting time (mins)

General

Production makespan (days)

Cycle wait labour (%)

Max capacity (volume) Average resource utilization (%)

and the corresponding buffers of the workstations and machines. Lastly, each production scenario will be evaluated with two general KPIs namely production makespan (Fig. 6) and machines utilization (Fig. 7). Table 4. Cleaning buffer items waiting time and capacity allocation KPI

WORKLOAD SCEN SCEN SCEN SCEN SCEN SCEN SCEN SCEN 1 2 3 4 5 6 7 8

Waiting time (mins)

1

1494

1452

1208

1143

1433

1084

1000

1001

2

2787

2822

1832

1815

2712

1421

1403

1155

3

4083

4186

2529

2578

4001

1989

2009

1518

4

5376

5540

3124

3286

5359

2491

2674

1942

Capacity 1 (items) 2

27

27

27

27

27

27

27

27

49

49

47

47

49

47

47

47

3

69

72

64

67

72

64

69

67

4

92

93

81

87

98

82

95

94

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As shown in Table 4 and Table 5 the waiting time of an item in the buffer (the cleaning and cutting buffers were chosen for results presentation, since most of the bottlenecks were identified in those workstation) is increased as the workload increases. The same thing happens with the capacity of the buffers. However, the waiting time for an item can be decreased when both the assistant operators and the cleaning machines volume is increased. The results show that if the number of assistant operators increases, without the increase in the cleaning machines, the waiting time could be increased as well. This happens due to the constraints in the process plan, which can be different in real-time, if some second level processes are executed first. Additionally, there is a decrease in waiting time when the number of experienced operators increases. Even though the assistant operator is responsible for the cleaning tasks, the completion of the experienced operators’ tasks in a shorter time gives the ability to the assistant operator to finish his or her tasks sooner. Lastly, the lowest waiting times can be seen in scenarios 6,7 and 8. This is because increased operators with increased machines can be more productive and this can be enhanced more, if the workload is increased as well. As a result, the efficiency of the personnel and shop-floor resources can be more beneficial for bigger workloads. Table 5. Cutting buffer items waiting time and capacity allocation KPI

WORKLOAD SCEN SCEN SCEN SCEN SCEN SCEN SCEN SCEN 1 2 3 4 5 6 7 8

Waiting time (mins)

1

1146

1190

943

963

1187

732

737

732

2

2326

2431

1483

1598

2277

1038

1122

909

3

3581

3746

2050

2366

3404

1562

1639

1223

4

4861

5074

2609

2928

4708

2007

2310

1604

Capacity 1 (items) 2

28

29

28

29

28

28

29

29

56

59

56

57

58

57

59

56

3

84

88

81

85

86

82

85

84

4

111

118

103

111

116

103

117

112

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As shown in Table 6 the utilization of the cutting workstation machines (cutting machines were chosen for presentation, since most of the tasks were performed in this workstation) is low. The cutting machines are busy for 3% to 4% of the production time. This happens due to two reasons: 1) low processing time of cutting tasks and 2) stricter constraints (these tasks should be performed in the early stage of the process plan). One can see that among the different shop-floor layouts and workload variations, there is not much difference in the utilization of the machines. Nevertheless, this is not the same with waiting labor time. The waiting time of an item in a machine indicates the time that this item was ready for processing, but there was no operator available. In both shop-floor variations the waiting labor time increases as the workload increases. However, this can be better limited in variation 1. As a conclusion Table 6 shows that the machines should not be increased without the increase of operators as well and even if this happens, the utilization of the resources is almost stable. Table 6. Cutting machines utilization and wait labor time for the different workloads and shop floor variations. WORKLOAD

1

2

BUSY 3 (%) 2

3

3

3

3

3

3

3

3

4

4

4

3 4 4

4

VAR2 M1

2

2

2

2

3

3

3

2

3

3

3

2 3 4

4

M2

1

2

1

2

1

2

2

3

2

3

3

3

2 3 3

4 9

VAR1 M1

VAR1 M1 VAR2 M1 M2

WAIT (%)

3

4

1

2

3

4

1

2

3

4

1 2 3

4

11 10 19 29 6

14 20 25 10 15 16 18 3 8 8

3

3

5

5

8

17 23 27 2

4

8

5

2 5 11 11

3

4

5

4

10 17 24 29 3

5

8

5

2 5 12 12

The utilization of machines though is proportional to the lead time. As shown in Fig. 6 the lead time for a workload volume varies in relation to the scenario. Except for the production of one reactor, all other workload volumes indicate the performance of the different shop-floor layouts. The results show that the increase in the number of assistant operators only (scenarios 2 and 5) will not lead to lead-time decrease. On the other hand, the increase of the number of experienced operators can only lead to 30% lead time decrease approximately. When the number of both type of operators is increased, the lead time decrease can reach more than 50%. This approach is also more efficient when the workload increases as well. Moreover, average machines utilization graph shown in Fig. 7 shows the same behavior and strengthens the argument that when experienced operators are increased in parallel with the machines in the shop-floor, the performance of the production system can be dramatically enhanced. Figure 7 shows also that this will also help the decrease of the waiting labor time (waiting labor time is 20% of the machines utilization approximately in scenarios 4 and 8).

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Lastly, Fig. 8 shows that the increase of the experienced operators leads to better production performance, because the utilization of such an operator class is approximately five times bigger than the ones of the assistant operators. This means that the experienced operators’ tasks are more time-consuming and, thus the process plan can be executed faster.

Time (days)

LEAD TIME 40 20 0 1

2

1 reactor

3

4

5

6

SCENARIO ID

2 reactors

3 reactors

7

8

4 reactors

Fig. 6. Production lead time for each scenario and reactors workload

SCENARIO ID

MACHINES UTILIZATION 7 5 3 1 0

5

10

PERCENTAGE (%) WAIT

BUSY

Fig. 7. Average machines utilization for each production scenario

15

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OPERATORS UTILIZATION exp operator avg ulizaon 100 8 7

ast operator avg ulizaon

1 2

50

3

0 6

4 5

Fig. 8. Operators utilization for each production scenario

5 Conclusion In this work a simulation approach was chosen for the extraction of production KPIs that will lead to better shop-floor understanding and design for various production scenarios. Discrete Event Simulation (DES) method was used, due to its flexibility in setting-up and evaluating different scenarios. Companies can assess various system configuration and operating strategy alternatives using DES to enhance decision-making in the manufacturing sector. Thus, a methodology for implementing such an approach is proposed and its practical use is presemted. To validate the impact of such a method, a renewable energy sources production system to produce a reactor was utilized as the case study. This type of manufacturing system was chosen due to its complex and stricter process plan and shop-floor diversity. After the simulation runs for the various production scenarios, KPIs for the different production entities, as well as for the global production itself, were studied. Results showed that with specific shop-floor adjustments and operators volume increase, the lead time can be drastically condensed even to less than 50%. In addition, production efficiency can be better identified when the system is utilized to produce more than one reactor. This is a key point, since the production projections affects the system design. Moreover, the analysis showed that there should be buffer capacity increase, if there is a workload increase as well, and this is independent of the resources increase. This happens due to the fact of manual operations leading the process plan and there are specific restrictions that can not be avoided in some cases. Another useful conclusion was that the utilization of the experienced operators was much bigger than the one of the assistant operators. Since the process plan has specific constraints, for the majority of the production time the assistant operators are less utilized. The resources that the experienced operators handle are less utilized as well. Nevertheless, a more effective process plan may increase the utilization of the whole production system. The main result is that an efficient and sustainable production system for similar environments has great potential only if the reactors production would be increased. As results indicated, in low volumes the adjustments made in the shop floor do not affect much the KPIs of the

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system. All the resources are better utilized and prove their value with higher workload volumes. Simulation approaches for a test before invest method are useful for companies. In some cases, as the one presented in this work, is the only way to address challenges and pain points in the production environment. When there is lack of automation in the production system, deviations affect a lot the performance of the system. A simulation gives the ability to identify production measurements with higher accuracy and observe a potential production system operation, before it is actually established. More automated simulation approach techniques could be studied in future, to give the ability to semiqualified company personnel to perform such methods. The methodology proposed in this work could be enhanced with the use of Artificial Intelligence and smart systems and result to an intelligent automated self-adaptive system, capable of performing multiple simulation runs for many production scenarios under different circumstances and uncertainties. This would be a very powerful and supporting tool for manufacturing companies, especially in the early stage of their digital transformation journey. Acknowledgement. This work was co-funded from the EIT Manufacturing Regional Innovation Scheme (RIS). This paper preparation and completion were possible with the generous staff allowance from HELBIO S.A. Hydrogen and Energy production. The authors are grateful to Neoklis Tzinias, Alexandros Safakas and George Nomikos for supporting this research.

References 1. Zangiacomi, A., Pessot, E., Fornasiero, R., Bertetti, M., Sacco, M.: Moving towards digitalization: a multiple case study in manufacturing. Prod. Plan. Cont. 31(2–3), 143–157 (2020) 2. Alexopoulos, K., Nikolakis, N., Xanthakis, E.: Digital transformation of production planning and control in manufacturing SMEs-the mold shop case. Appl. Sci. 12(21), 10788 (2022) 3. Florescu, A., Barabas, S.A.: Modeling and simulation of a flexible manufacturing system—A basic component of industry 4.0. Applied sciences 10(22), 8300 (2020) 4. Gajsek, B., Marolt, J., Rupnik, B., Lerher, T., Sternad, M.: Using maturity model and discrete-event simulation for industry 4.0 implementation. International Journal of Simulation Modelling 18(3), 488–499 5. Johansson, B., Johnsson, J., Kinnander, A.: Information structure to support discrete event simulation in manufacturing systems. Proceedings of the 2003 Winter Simulation Conference, vol.2, pp. 1290–1295. New Orleans, LA, USA (2003). https://doi.org/10.1109/WSC.2003.126 1564 6. Gien, D., Jacqmart, S.: Design and simulation of manufacturing systems facing imperfectly defined information. Simul. Model. Pract. Theory 13(6), 465–485 (2005) 7. Mourtzis, D., Papakostas, N., Mavrikios, D., Makris, S., Alexopoulos, K.: The role of simulation in digital manufacturing: applications and outlook. Int. J. Comput. Integr. Manuf. 28(1), 3–24 (2015) 8. Coelho, F., Relvas, S., Barbosa-Póvoa, A.P.: Simulation-based decision support tool for inhouse logistics: the basis for a digital twin. Comput. Ind. Eng. 153, 107094 (2021) 9. Siddaiah, R., Saini, R.P.: A review on planning, configurations, modeling and optimization techniques of hybrid renewable energy systems for off grid applications. Renew. Sustain. Energy Rev. 58, 376–396 (2016)

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10. Caputo, G., Gallo, M., Guizzi, G.: Optimization of production plan through simulation techniques. WSEAS Trans. Inf. Sci. Appl. 6(3), 352–362 (2009) 11. Wang, Q., Chatwin, C.R.: Key issues and developments in modelling and simulation-based methodologies for manufacturing systems analysis, design and performance evaluation. The Int. J. Adv. Manuf. Technol. 25, 1254–1265 (2005) 12. Banks, J.: Introduction to simulation. In: Proceedings of the 31st conference on Winter simulation: Simulation—a bridge to the future, Vol. 1, pp. 7–13 (1999, December) 13. Skoogh, A., Johansson, B.: A methodology for input data management in discrete event simulation projects. In: 2008 Winter Simulation Conference, pp. 1727–1735. IEEE (2008, December) 14. Lanner: https://www.lanner.com/en-gb/technology/witness-simulation-software.html, accessed online April 2023

Analysing Barriers to Achieving SDG 7. Managing Green Product Development in the Wind Energy Sector Rakel García-Alonso1 , Beñat Landeta-Manzano2(B) , German Arana-Landín3 and Rubén Jiménez-Redal4

,

1 Tecnalia Research and Innovation, 48013 San Sebastian, Spain

[email protected]

2 Business Management Department, Faculty of Engineering, University of the Basque Country,

48013 Bilbao, Spain [email protected] 3 Business Management, Department Faculty of Engineering, University of the Basque Country, 20018 San Sebastián, Spain [email protected] 4 Business Management, Department Faculty of Engineering, University of the Basque Country, 48013 Bilbao, Spain [email protected]

Abstract. Wind energy is seen as a promising option for sustainable energy, but its implementation also has environmental impacts. The environmental impact of wind energy systems, particularly on-shore and off-shore wind turbines, has been extensively researched using methodologies such as Life Cycle Assessment. However, creating more sustainable wind turbines involves rethinking their creation, production, and consumption. To reduce environmental impact, companies must overcome barriers to implementing eco-design and develop innovative solutions. Understanding these barriers is crucial for designing effective solution strategies in the wind energy sector. A case study was carried out on one of the top five original equipment manufacturers in the wind energy sector. Eco-design has become one of the most effective ways of incorporating environmental considerations into design activities, but also has led to increased innovation in wind turbine design and development, as well as in associated processes such as manufacturing, transportation, installation, operation, and maintenance. However, despite experience in environmental management systems and eco-design in particular, obstacles, both external and internal to the company, still need to be overcome to succeed in product eco-design. Among other barriers, technicians and managers often lack specialised ecodesign skills, which can hinder the process, and the labour market does not adequately respond to the demand for professionals with specific knowledge in this field. Top management should also develop more effective methods or improve existing ones to meet the expectations of product design and development technicians. Keywords: Eco-design · Sustainable products · Wind energy · Barriers © IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 666–682, 2023. https://doi.org/10.1007/978-3-031-43688-8_46

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1 Introduction Energy is the main contributor to climate change, accounting for around 60% of all global greenhouse gas emissions [1]. Therefore, one of the targets for 2030 under the United Nations Sustainable Development Goals (SDGs) goal 7 on clean and sustainable energy is to significantly increase the share of renewable energy in the energy mix. In this context, numerous energy generation systems have been developed, with mixed success [2], and different growth expectations for various reasons [3]. One of the systems that has experienced the greatest expansion in the world has been wind energy. The keys have been the availability of wind, a seemingly inexhaustible resource; a mature technology; a relatively low environmental, social and Levelised Cost of Electricity impact or cost compared to other systems [4]. It is the system with the greatest prospects for growth and is even seen as one of the keys to the future energy system in many regions, as is the case in the European Union [3]. However, as a renewable energy, its remarkable growth and expansion around the world inevitably implies that the impacts associated with the implementation of wind power generation systems will increase [5]. But among the impacts inherent to any human activity, those that compromise environmental sustainability are also compromising for the future of humanity [6]. Throughout the entire life cycle of 1kWh of wind energy, there are impacts on a global scale on biodiversity, climate, the ozone layer, and acid rain, and on a local scale on the consumption of natural resources, emissions, waste, dumping and visual landscape impact [6]. Therefore, given its relevance, there is an extensive literature analysing the potential environmental impact of different wind generation systems. These are mainly on-shore, although the rapid development of off-shore technology has also aroused the interest of researchers and stakeholders interested in understanding its environmental impact [7]. Research has mainly focused on estimating the potential impacts of wind turbines (WT) [8]. However, from a business perspective, the main challenge in creating more environmentally sustainable products may not be to choose the most appropriate methodology to assess the environmental impacts of their WTs, but to rethink the ways in which they are created, produced, and consumed [9, 10]. New concepts, technologies and innovative actors must be developed to address the complexity of today’s sustainability challenges [9]. Therefore, given the relevance of the wind energy sector in the current and future context, it is necessary to start from the beginning. Thus, it must be addressed the analysis of the main barriers that limit the implementation of eco-design within companies in the wind industry that allow the subsequent design of possible solution strategies to overcome them. For this purpose, the following two central research questions (RQ) are posed: RQ 1: What are the main barriers that limit the implementation of ecodesign in companies in the wind energy sector? RQ 2: What strategies can be effective in overcoming the barriers to ecodesign by companies in the wind energy sector?

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The following section reviews the existing literature. Section 3 then presents the research methodology used. In Sect. 4, the research results are analysed and, in Sect. 5, discussion and conclusions of interest to industry practitioners and academic specialists are presented. Finally, in Sect. 6, the main references used in this research are shown.

2 Background and General Concepts Eco-design can be referred to as the most appropriate framework for designing and developing a product with the environment in mind [11]. Karlsson and Luttropp [12] stated that “eco-design is an aspect of design, a new intelligent design for the future in line with the Bruntland report”. Ecodesign projects require an organisational vision where it is necessary that all the agents involved in the project collaborate. To achieve the proposed objectives, changes in the way projects are undertaken and a series of guidelines and tools to support the activities are necessary. There are several methodologies developed with the aim of successfully tackling the keys to the integration of ecodesign in a single tool. These include environmental management standards based on processes of continuous improvement of ecological and economic indicators of a product and based on the systematic integration of eco-design into company strategies and practices throughout the life cycle [13]. An example of this is the ISO 14006 eco-design management standard that allows developing schemes for product design and development [14]. On the other hand, for a more modest first approach, many companies have opted for the use of manuals and guides that develop in a practical way basic procedures aimed at improving specific product parameters [15]. However, some authors such as [16] emphasise that success in improving product environmental performance lies in the development of a systematic approach based on a management standard such as ISO 14001, which must be sufficiently developed and established within the company. This contributes to a better understanding of which material and energy flows are most important. This basis with the inclusion in the new version of the standard of a more product-oriented life cycle analysis [16], should be complemented with the support of ecodesign tools according to the process stage [15]. Most of these eco-design tools and methodologies are mostly adaptations of traditional methods and tools used in the product development process, such as Quality Function Deployment (QFD) or Failure Mode and Effect Analysis (FMEA) [17]. Methodologies differ in the scope of application, the quality of the results and the time needed to apply them [17, 18], but their application is scarce, and, in many cases, only theoretical examples are available, without the support of practical application by a company [18]. Even when they are applied, most of the time they are not applied systematically in companies due to their complexity, the time required for their implementation and the lack of knowledge on environmental issues [15, 18]. The academic literature reveals several common barriers and difficulties faced by companies when adopting eco-design practices. Economic constraints and technical limitations are among the commonly cited barriers. Sanyé-Mengual et al. [19] found that economic investment and technical constraints are common challenges faced by companies. These constraints may include the costs associated with implementing ecodesign practices and the availability of appropriate technologies. Similarly, Veshagh

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et al. [20] identified high implementation costs as a major barrier, along with weak justification for investment, lack of stakeholder awareness, and weak customer demand. The lack of legal incentives is highlighted by Jabbour et al. [21] as the most influential barrier to eco-design adoption. Companies may prioritize factors such as performance, cost, and aesthetics over environmental considerations due to the absence of legal requirements or incentives. Rossi et al. [22] also emphasize that companies tend to optimize aspects directly affected by legislation and prioritize other drivers, which limits the implementation of eco-design practices. Inadequate tools and methods for eco-design implementation pose significant challenges. Rizzi et al. [23] mention that the tools and methods available may not align with industry needs, making it difficult for companies to select the most suitable options. The time, effort, and resources required for implementation are additional barriers mentioned by Rossi et al. [24] and Rizzi et al. [23]. Stakeholder participation and awareness are critical for successful eco-design adoption. Bonou et al. [24] propose a participatory approach involving cross-functional employees to identify improvement opportunities and build support for sustainable innovations. Resistance to change and conflicting goals within organizations are common barriers mentioned by several authors. Dahmani et al. [11] highlights the resistance to change from employees or management, along with conflicting goals or priorities, as barriers to eco-design implementation. Medeiros et al. [25] emphasize the challenges faced by managers and team members, including resistance from team members and the risk of failure associated with eco-design projects. Lack of knowledge and awareness about eco-design is another significant barrier. Sing and Sarkar [26] emphasize the importance of specific knowledge about eco-design tools and methods and limited awareness of environmental issues and sustainability. Jabbour et al. [21] list insufficient knowledge as one of the barriers to eco-design adoption. Financial constraints and lack of resources also hinder the adoption of eco-design practices. Buzuku and Kässi [27] rank lack of financial resources for implementation as one of the top barriers, along with uncertainty in product demand and lack of knowledge and skills for eco-design implementation. In general, the studies that address the issue provide us with an approach that companies are facing, and this can be a reference for wind turbine manufacturers when they wish to successfully adopt eco-design practices. However, we have not found any study that addresses the issue specifically in the wind sector. Perhaps this is because the academic literature considers the results of existing studies to be perfectly applicable to the sector and considers that the sector has no particularities that would indicate otherwise. In this respect, Landeta et al. [14] or Stewart et al. [28], among others, pointed to sectoral differences. Landeta et al. [14], for example, in their analysis of the adoption process of eco-design management systems in four industrial sectors, highlighted appreciable differences between sectors. Trying to come up with another possible explanation, the wind turbine manufacturing sector has experienced consolidation in recent years. This consolidation has resulted in

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a concentration of market share among a few dominant manufacturers. Limitations on access to meaningful sources of evidence may also have been a deterrent. However, the issue must be addressed. The remarkable growth and expansion of the global wind sector inevitably means that the impacts associated with the deployment of wind generation systems are increasing and therefore relevant [29]. When it comes to creating more environmentally sustainable products, the primary challenge for businesses in the wind turbine industry may not lie in selecting the most suitable methodology for assessing the environmental impact of their turbines. To achieve their goals of improving the environmental performance of their products, companies need to integrate ecodesign activities into their strategic management and daily operations. This integration should be viewed as a dynamic process of continuous improvement, rather than isolated improvements with limited impact [15]. Therefore, given the crucial role of the wind energy sector in the current and future environmental landscape, it becomes imperative to address the initial stages. Therefore, it is essential to examine the primary obstacles that hinder the incorporation of eco-design practices within wind energy companies. This examination will enable the subsequent formulation of viable strategies to overcome these barriers and pave the way for sustainable solutions.

3 Research Methodology The research process began with a review of the literature in the first quarter of 2022. We obtained a first approach to the research problem that enabled us to adequately formulate the central research questions and the methodological design. Given the nature of our objective, a single case study has been carried out, because of its importance and significance it is considered critical and sufficiently valid to draw conclusions, we wish to study a very specific situation [30]. Following Yin [31], the selection of the case to study was based on a theoretical and logical, non-random sampling. It was chosen a case that offered a greater opportunity for learning, greater accessibility to the information required by this research, and an adequate disposition on the part of the people most directly involved in the environmental management of the company. The company chosen for the research is one of the top five original equipment manufacturers (OEMs) by sales volume and cumulative installed capacity, with more than 120 GW installed worldwide by December 2022. In terms of environmental management, the OEM has a long history of working to improve the environmental performance of its activities and products. The OEM has a multi-centric EMS per ISO 14001 reference standard, covering almost 100% of its production capacity worldwide and has set a target of achieving carbon neutrality by 2025. In addition, the company annually verifies GHG emissions according to the ISO 14064 series and produces environmental product declarations for several multi-megawatt models in its portfolio, based on the ISO 14025 and CEN 15804:2012 + A1 standards. But the company is a true industry pioneer in product eco-design and the integration of eco-design into its environmental management system. The case study allows to analyse the phenomenon in its real context, considering all aspects of the problem, and using multiple sources of evidence, quantitative and/or

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qualitative simultaneously and to generate a very important level of realism in the conclusions of the research [32]. Cambra [33] defines the case study as part of an inductive research perspective, in which the empirical details that constitute the object under study are interpreted in the context in which they occur. This gives depth and dimension to the explanations generated by this methodology. Moreover, as the phenomenon under study is underpinned by difficult to imitate, socially complex organisational factors, the use of qualitative methods is necessary to obtain evidence of interest. Certainly, the use of qualitative methods allows for a considerable increase in knowledge about the behaviour of organisations and, among all of them, the case study allows for a very important level of realism in the conclusions of the research [33]. In the second phase of the research process, the case study protocol was designed to clarify certain ethical aspects, as Huberman et al. [34] point out as necessary. The informants involved were informed about the expectations, commitments and rights related to the study and which specifically refer to the effort and approximate time they had to dedicate to this research, what activities their collaboration would involve, or the treatment of the information obtained, among other aspects. The sending of this documentation and subsequent telephone contacts with the company served to certify their collaboration in the investigation and to clarify any doubts that arose among the participants in the investigation. After this phase, the field procedure began with the phase of collecting data from multiple sources of evidence. External sources were used, such as annual sustainability reports, eco-design procedures, product LCAs, product sales data, internal reports, and communications, and other relevant environmental information, some about product from suppliers. In addition, semi-structured and in-depth interviews were carried out - exceptionally in groups - with open and closed questions with people who had directly carried out management tasks in the design of environmental strategies and objectives in the period analysed, in the implementation phase of actions, and in the control and monitoring processes through indicators. The fieldwork was completed with direct observation by visiting some of the company’s facilities to gather more precise contextual aspects. All this work was carried out with the use of physical, technological, and cultural artefacts, including image files and recordings of the interviews conducted, as support for the confirmation of data previously collected by other methods (reports, reports, eco-design procedures…) [14]. All the information was recorded, classified, and combined to create a database of evidence. Subsequently, data analysis was performed and a preliminary report describing the results was produced and passed on for review to three key informants, an academic expert in Business Innovation and Technology Management; an eco-design management consultant; and a Global Business Development Market Manager from one of the other four major OEMs in the wind sector. All experts from outside the research. They discussed whether the results were in order, understandable and well documented, and whether the research team had sufficient mechanisms in place to avoid bias [31]. Nevertheless, a revised report with suggested changes was reworked and provided again to the same people for sign-off.

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4 Case Study 4.1 Introduction Continuous improvement is present in all the OEM’s activities and the path towards excellence is a commitment that reaches all the processes related to the design, manufacture, assembly, field assembly, commissioning and after-sales service of WTs and their components. Since 1997, the OEM has had a quality management system certified in accordance with the ISO 9001 standard, and since October 2002 it has had environmental certification in accordance with the ISO 14001 standard. Subsequently, in accordance with its commitment to continuous improvement and collaboration in achieving sustainable development, as set out in its Integrated Health and Safety at Work, Environment and Quality Policy, in 2010 the OEM obtained the AENOR Health and Safety at Work certificate. It became the first Spanish company to certify its worldwide network of centres according to OHSAS 18001 standard by the certification body. In 2011, the Systems were integrated to form a single system of global scope, including all OEM’s headquarters and production centres around the world (30 worldwide). The existence of this integrated system ensures that practically 100% of its production capacity in the world is certified according to these standards. At the same time as making progress in the implementation of an Integrated Management System for the entire company, in 2003 the OEM started the project for the next generation of WTs. With the aim of creating environmentally friendly products, the OEM decided to promote the “Life Cycle Analysis and application of the eco-design concept to a 2MW onshore WT” project, becoming the first company in the sector to carry out a LCA of a WT. The main objective of this study was to calculate the environmental impact associated with a WT throughout its entire life cycle. This was a necessary step to eliminate or minimise the environmental impacts associated with a WT. The OEM was finalising preparations for certification in UNE 150301, but in view of the forthcoming publication of the international eco-design standard, ISO 14006, the OEM agreed to wait for the publication of this standard, and thus obtain the international label that would certify its work in eco-design. In 2011, the OEM completed the eco-design project, inventorying 99.8% of the WT. In December of the same year, the OEM obtained ISO 14006 environmental certification for the world’s first eco-designed model. Currently, in the period of analysis of this research, the company adopt continuous improvement as a strategy and maintains systematic approaches to identify the environmental and energy aspects. 4.2 Results In relation to the WTs developed by the company, more than 85% of the environmental impact is concentrated in the first two phases of the life cycle: “Acquisition of raw materials and manufacture of WTs”, and “Construction of WTs”. Between 49% and 74% of the impact is generated in the phase “Procurement of raw materials and manufacture of WTs”. The consumption of electrical and electronic components in the cabinets and the converter, and the consumption of electricity and natural gas during the manufacture of

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the blades are critical sources. The reduction of raw materials used has a very significant impact on all manufacturing and logistics activities. The “wind farm construction” phase accounts for between 21% and 41% of the impact in all four categories. Transport, steel and concrete foundations, metals in the transmission grid, transformer oil and burnt fuel are the most polluting elements. The “Operation and maintenance” phase, although with significantly lower impacts than other phases of the life cycle of WTs (between 3.5% and 27% of the total), is growing with each new launch. The replacement of blades, which are getting larger with each new launch, is the most critical element today for environmental and product development technicians and managers. Finally, in the EoL phase, the environmental impact generated is low, largely because the recyclability of the machines is over 90%, with environmental impact values varying between 0.3% and 4.0% of the total. The recyclability of the blades remains a problem even today, without significant improvements. Based on this environmental profile, the development of new WTs is conditioned by various aspects that hinder the adoption of eco-design improvement actions and the results obtained. The main eco-design strategies (see Table 1) and measures applied in the latest WT developments carried out by the OEM are shown below: Considering the environmental profile and the strategies designed and measures taken to improve environmental performance, the company still faces several other internal and external barriers. The following Table 2 show a list of difficulties in eco-design processes identified in the literature [14]. The assessment provided summarises the rating of each aspect as a conditioning factor in terms of level as a Likert-type item, from very low or no influence (◯), to very high (●●●●●), considering: the opinions expressed by the people interviewed the OEM, the analysis of available documentation, internal communications… And visits. Likewise, to reinforce the validity of the construction and as a reliability test, the three key informants mentioned in the previous Sect. 4 were supervised in the preparation of these tables. One of the main challenges faced by the OEM is to perform the life cycle inventory. “The number of components of a wind turbine is enormous and obtaining the necessary information from suppliers to be able to assess the environmental impact is really difficult”, said the first eco-design project leader. The technical staff and those responsible for product design and development in OEM needed informative talks and specific training courses on eco-design, mainly given by their own staff, occasionally supported by external personnel (auditors, consultants, and public environmental organisations). The use of LCA tools is crucial for technicians, but still unknown to most of them, even though it allows them to focus on the weak points of the product, to improve the integral perspective of the product and to check the environmental influence of their decisions throughout its life cycle. “I would say it provides a new perspective on how to approach the design of new products,” says one of the product design and development technicians interviewed.

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Table 1. Main eco-design strategies and measures considered for the environmental improvement of new WTs. Product life cycle phase

Strategy

Implemented measure

Procurement of materials and Reduce material use components

Redesign of components and systems to reduce the size and weight of the nacelle

Production

1/ Reduction of steps and required energy consumption

Reduction of polluting substances

2/ Elimination or reduction of harmful or hazardous substances 3/ Reduction of VOCs 4/ Optimisation of the carbon fibre burr cutting processes and the painting system in the manufacture of blades Assembly

Reduction of emissions

1/ Reduction of civil works and land occupation, as well as the number of installations, assembly and lifting of blades 2/ Reduction in the use of heavy cranes in the assembly of WT

Transport and distribution

Select environ. Efficient forms Reuse of all packaging used of distribution between its suppliers and the OEM Reduction of waste during the useful life of the WT, maintenance costs, spare parts, etc. Reduced consumption of lubricating and hydraulic oils

Use

Reduce maintenance needs

Substitution of lubricating oil for grease

End of life

Optimising the life cycle

Recycling of practically all metals (ferrous and non-ferrous), plastics and cables (recyclable percentage minimum: 90%)

However, the training received sometimes lacked answers to problems of practical experience of technicians and difficulties in handling support tools, LCA and others. In this respect, the labour market has not responded to the company’s demand in this area.

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There is a manifest lack of training and knowledge in eco-design among technicians and those responsible for the product design and development process, which hinders the adoption of environmental criteria in the design and development phase, in accordance with life cycle perspective. The same design and development managers and technicians point out the need to have an LCA tool synchronised with the management software and other database managers used in the company, to be able to always know the cost associated with a given environmental impact and thus be able to act to try to reduce it. In the case, a direct consequence of the application of eco-design is a slight increase in machine costs, mainly due to the use of more efficient but more expensive equipment, which seems to hold back the level of commitment of top management (see Table 2). Table 2. Difficulties faced by the OEM. Internal

External

Tools:

Market - customer:

Lack of criteria for selecting the most appropriate tools.

●●●●

Difficult to implement new tools within the development process.

●●●●

Need for new tools.

●●●●

Difficulty in identifying the advantages/disadvantages related to the implementation strategies for products.

●●●

Difficult to use the results of an ●●●● LCA to inform the decision-making process. Completing an LCA requires ●●●●● too many resources in terms of data, time, and expertise. Lack of support for data exchange between different tools.

●●●●

High competitiveness.

●●●●

It is difficult to achieve the behavioural and cultural changes needed to support the implementation of the tool.

●●●

Lack of marketing studies.

●●●●

Benefits are not tangible.

●●●●

It is difficult to find the right ●●●● balance between simplification of the LCA approach and possible loss of accuracy/reliability/quality.

(continued)

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Internal The environmental tools we tested did not fit our product development.

External ●●●●

Lack of knowledge of the benefits

●●●●◔

Expertise: Requires a great deal of ●●●●● knowledge development both in the design department and in the supporting business functions Staff do not have the level of environmental knowledge required for effective environmental improvement.

●●●●

Difficulty in interpreting customer ●●◑ perception

Perception of no market demand

●●◕

Difficult to create external awareness for the implementation of environmental improvement

●●●◑

Lack of involvement of sales and marketing departments

●●◑

When easy environmental ●●●◑ improvements were carried out it became too difficult to continue to the next level. The trade-off is too difficult to ●●◕ balance (e.g. less chemical use leads to more energy use). It was difficult for us to identify targets for our improvements

●●

Difficult to find information on ●●●● environmental impact. Difficult to find alternative materials/components that have a lower environmental impact.

●●●◕

Difficult to find alternative ●●●◕ manufacturing process alternatives with lower impact. Large amount of data required. ●●●●● High quality of data required.

●●●●●

Investment: Perception of high cost of tools.

Legislation: ●●●

No mandatory requirements

●●◑ (continued)

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Table 2. (continued) Internal

External

Lack of awareness of the benefits that can be achieved.

●●◑

High cost of certifications/verfications.

●●◑

Lack of accessible financial support.

●

Time needed to conduct a complementary environmental analysis.

●●●●

Time to implement tools in the ●●●● company’s system. Time for training sessions.

Perception of bureaucracy

●●●◑

Lack of a clear legislative framework

●●

Specficity for product categories

●●●●

No accountability for producers

●●●●

●●●

Strategy: Lack of commitment / support from top management.

●●

Difficulties in maintaining momentum for the implementation of environmental improvement.

●●●

Not enough legislative incentives

●●●●

Lack of identification of motivational factors.

●●

No support in regulation and enforcement of legislation

●●●●

Lack of environmental vision.

●●

Poor integration into corporate ●●◑ management and strategy. Lack of cooperation between departments.

●●◕

External collaboration:

Lack of standardisation in the product development process. Lack of environmental objectives.

●●●

Identification of value chain stakeholders to include in the eco-design effort.

Complexity of the product development process.

●●●●◑

Conflict in functional/environmental requirements.

●●●●

●●●

(continued)

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R. García-Alonso et al. Table 2. (continued)

Internal

External

Management: Difficult to integrate environmental improvement activities into the product development process

●●◕

Difficult to manage expectations within development projects

●●

Lack of a systematic approach to implementing environmental improvement across the business

●●◕

Lack of deployment of roadmap for continuous improvement

●●◔

Difficulty in integrating environmental issues very early in the development process

●●●◑

Suppliers lack willingness to cooperate

●●◕

Difficulty in obtaining detailed data from suppliers

●●●●

Lack of quality data from suppliers ●●●●◕

Lack of environmental impacts ●● as an overall objective Lack of motivation/resistance from internal stakeholders to implement environmental sustainability improvement aspects or practices

●●●

Difficulty in retrieving data from the entire supply chain

●●●●

Problems related to process timescales

●●◔

Transparency of environmental data in the value chain.

●●●●◔

Environmental improvements resulted in an undesired or compromised product.

●●

At this point, the company’s environmental manager and the technician point out that it is necessary for public administrations to promote measures that favour the development of green markets. Among other measures, in line with the three key informants, they propose the development of environmental legislation. They proposed that “the product exhibiting the highest level of environmental efficiency be duly rewarded, while the poorest performer be subjected to taxation, thereby fostering a scenario of equitable competition”. Product cost is precisely one of the most decisive purchasing parameters of a customer in most markets today, in line with other academic work [14].

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Another challenge for the improvement of WT’s environmental performance was the extension of the eco-design strategy to the supply chain. Even today, there is still a great lack of awareness of the eco-design concept and its implications among suppliers, including situations of mistrust and difficulties in obtaining the required environmental information. In this sense, certain suppliers were reluctant to collaborate in the environmental improvement of the product system. In this sense, the OEM prepares the Environmental Product Declaration type III (standardised under the ISO 14025 standard) to promote the exchange of environmental information on components and materials, use and end of life. It is necessary to carry out more precise LCAs to improve the environmental performance of the product. In contrast to some suppliers, certain customers interested in having a more environmentally sustainable product expressed a more cooperative attitude.

5 Discussion and Conclusions Wind energy has witnessed significant global growth as an energy generation system. However, limited research has focused on developing sustainable products specifically for companies in this sector. Analysing the key barriers to implementing eco-design in the wind energy industry is crucial for designing effective strategies to overcome these obstacles. The implementation of eco-design principles in product development has fostered greater innovativeness in design and associated processes, which has been seen in previous work in other sectors [14] (covering supply chain, manufacturing, transport, installation, operation, and maintenance). As a result, companies have achieved a stronger competitive advantage, a phenomenon previously observed in various sectors [9]. There are different forms of environmental component in eco-design activities. One of the most effective ways of integrating it is by adopting environmental management at the organisational level [14, 15], integrated into the company’s management system (EMS), and with the main premise of any advanced management system, continuous improvement, as its banner [14]. To achieve this objective, it is desirable that the company adopts a product life cycle perspective and that the impacts inherent to the product are considered in each phase of its life cycle, as pointed out by Rodrigues et al. [15] and Charter et al. [16]. It should include the phase or phases in which the company’s own activities are carried out, such as the construction of wind farms or the installation of WTs and/or their operation and maintenance, in addition to the turbine manufacturing phase. This is how it should be, according to the analysis carried out and to the confessions of the technicians and experts consulted. Despite the work carried out and a long experience in environmental management and eco-design, the effort must be persevered with and obstacles external to the company, but also internal, must be overcome. Even today, the lack of mastery of technicians and managers in the field of eco-design still prevails, creating situations of doubt and lack of knowledge that make the process difficult, something that has already been pointed out by Landeta et al. [14], Sing and Sarkar [26] and Chiappetta et al. [27]. Field. This is a problem that the OEM is trying to remedy by promoting collaborative projects for the training of technical specialists. However, internally, the company should also

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complement the training of personnel (technicians and managers), not having done so may be due to resistance to change on the part of employees or management, a barrier highlighted in the literature [11, 25]. On the other hand, the OEM should make greater efforts to create more effective methods or improve existing ones in those aspects that do not meet the expectations of product design and development technicians. Rizzi et al. [23] also pointed in the same direction. Due to the lack of accurate data, eco-design’s potential for reducing manufacturing costs in the value chain is often overlooked. The focus on environmental impacts primarily during the manufacturing phase may explain the limited involvement and support from top management. This trend extends beyond the wind energy sector, as demonstrated by Landeta et al. [14], among others. Continuing with the cost chapter, neither economic [10–20], nor financial investments and lack of resources [28] seem to hinder the adoption of eco-design practices in the OEM. This can be explained by the OEM’s determination to show stakeholders its environmental commitment and its increased ability to have the necessary resources to do so. This same determination at the strategic level would also explain that the lack of legal incentives is not a barrier for the OEM, unlike in other sectors [21]. Finally, as in any research work, the present study has certain limitations, the main limitations referring to methodological issues, typical of a case study. Being a single case study, the results do not allow for statistical generalisations. The issue of generalisability of qualitative studies (including, therefore, the case study) does not lie in a probability sample drawn from a population to which the results can be extended, but in the development of a theory that can be transferred to other cases. The case study conducted is sufficiently important and significant as to be valid enough to draw conclusions and to predict similar results (literal replication), or to find contradictory results due to predicted reasons (theoretical replication). To this end, the necessary measures have been taken to ensure the internal validity (establishment of a chain of evidence and use of triangulation techniques: multiple sources of evidence and data collection methods applied during the data collection and analysis phase of the research data) and external validity (justification of case selection, contextual factors, unit of analysis, scope…) and reliability of the case study. Further case studies are essential in establishing cause-effect relationships between strategies and measures aimed at enhancing environmental performance in the wind turbine sector. This research will facilitate the design and implementation of more effective strategies, leading to reduced environmental impacts through improved processes, updated models, and eco-design management. Neglected aspects, such as the visual impact on installation sites and noise emissions, should also receive greater attention. Acknowledgments. We acknowledge support provided by Centre de Documentation et de Recherches Européennes (Université de Pau et des pays de l’Adour, France), the Environmental Sustainability and Health Institute (Technological University of Dublin, Ireland), and the Euskampus Foundation Programme within the projects ZIRKBOTICS and SOFIA. The researchers are part of the IT1691–22 research team of the Basque University System.

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Resilience Management in Supply Chains

Derivation of the Data Attributes for Identification of Incorrect Events in Supply Chain Event Management Jokim Janßen(B) , Tobias Schröer, Günther Schuh, and Wolfgang Boos Institute for Industrial Management (FIR) at RWTH Aachen, Aachen, Germany [email protected]

Abstract. Based on the increasingly complex value creation networks, more and more event-based systems are being used for decision support. One example of a category of event-based systems is supply chain event management. The aim is to enable the best possible reaction to critical exceptional events based on event data. The central element is the event, which represents the information basis for mapping and matching the process flows in the event-based systems. However, since the data quality is insufficient in numerous application cases and the identification of incorrect data in supply chain event management is considered in the literature, this paper deals with the theoretical derivation of the necessary data attributes for the identification of incorrect event data. In particular, the types of errors that require complex identification strategies are considered. Accordingly, the relevant existing error types of event data are specified in subtypes in this paper. Subsequently, the necessary information requirements and information available regarding identification are considered using a GAP analysis. Based on this gap, the necessary data attributes can then be derived. Finally, an approach is presented that enables the generation of the complete data set. This serves as a basis for the recognition and filtering out of erroneous events in contrast to standard and exception events. Keywords: Anomaly Detection · Data Set · Deviation identification strategies · Incorrect Data · Supply Chain Event Management · EPCIS

1 Introduction Due to increasing globalization, the field of supply chain management has become more complex and dynamic, leading to more unstable framework conditions and a higher probability of unplanned processes [1]. As a result, companies have to expend more effort to control processes and manage a greater number of interfaces and processes [2]. In addition, due to increased interconnectedness and connectivity, companies process and consume larger amounts of data that need to be filtered to extract the relevant information that requires the attention of decision-makers [3]. To achieve this, event-based systems such as tracking and tracing systems and supply chain event management are used, © IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 685–698, 2023. https://doi.org/10.1007/978-3-031-43688-8_47

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which operate on the concept of management by exception [3]. These systems use events as an informational basis containing data in a standardized format that originates from planned or unplanned events [4]. For the information to be useful and reliable for subsequent processes and decision-making, it must first be available and of high quality [5]. Otherwise, serious consequences can occur, as the following example shows [6, 7]: A supplier delivers a product to the manufacturer, but for various reasons, the corresponding event message is not transmitted to the manufacturer. Due to the absence of this message, the manufacturer starts to reschedule production to avoid a production stop. The message is finally transmitted with a delay, which causes the manufacturer to postpone production again, resulting in unnecessary additional costs. This example illustrates the possible consequences of event data that does not accurately reflect reality. To avoid this and to increase the functionality of event-based systems, incorrect event data must be identified and removed from the event repository before processing [5].

2 Theoretical Background To identify and filter out erroneous event data, it is essential to have a comprehensive understanding of the term “event” and the data structure of event standards. In event-based systems, there is no universal definition of the term “event” in the literature. Some authors describe events as activities that occur in the real world or a computer system [8]. According to Bensel et al. [9], the term can be described as the associated data object for the occurrence of a state with essential significance for logistical processes. In contrast, Heusler et al. [10], for example, argue that events are only considered deviations from a planned state. The different definitions can be categorized as “event in the sense of a status report” or “event in the sense of a deviation.” This paper follows the understanding of “event in the sense of a status report” since the focus is on relevant data objects and associated standards of events. Different standards exist for exchanging events, which ensure that the sender and receiver use compatible formats to read the events without any loss in the receiver system [11]. Through literature research, three common standards were identified: Tracefish, TraceCore XML, and EPCIS, with EPCIS being the most widely used. This standard is universal and can be used in various industries, while TraceCore XML was designed for data exchange in the food industry with a focus on food traceability, and Tracefish’s standard is specific to the fish industry. [7] Given that the EPCIS standard is independent of the industry and widely used [12]. In 2022, a new EPCIS standard (version 2.0) was published [13]. In practice, however, it is mainly the previous version that is used, so this paper focuses only on the EPCIS standard version 1.2. According to the EPCIS standard (version 1.2), events always have a basic structure and contain information from the four dimensions of what, when, where, and why using specific data attributes (see Fig. 1). The “dimension what” specifies which objects or object classes are involved in the event, and the “dimension when” records the date and time of the event’s creation and recording in an EPCIS repository. The “dimension where” specifies the exact capture point where the event was generated, and the “dimension why” specifies the reason why the event was created. The entire data is stored in an XML structure with a defined syntax, and the vocabulary is specified by the CBV. The four

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dimensions can be used to describe the content of each event that occurs in a physical or virtual object. All events can be categorized into different types of EPCIS events, such as ObjectEvent, AggregationEvent, TransformationEvent, and TransactionEvent [14].

Fig. 1. Required and optional data attributes of the event standard EPCIS (version 1.2) [14]

Based on the understanding of the event data, the state of the science can now be considered. In general, dealing with incorrect events and insufficient data quality in the context of supply chain event management is not considered in the literature. Regarding the sub-aspects (description of event data with insufficient data quality, handling of insufficient data quality, identification of deviations), individual papers were found using an abstract-based, partially systematised literature search. The following criteria were applied to the databases ScienceDirect, IEEE Xplore, Google Scholar, and RWTH Aachen University Library Catalogue: language (German and English), availability (full text), document type (journal articles, monographs, collected works), content transferability (dealing with erroneous event data in supply chain event management). The event and supply chain event management literature only deals with correct data. Konovalenko and Ludwig [7] note in their comprehensive systematic literature review that this aspect should be considered in the context of supply chain event management. In their work, Janßen et al. [6], create the only approach for the description and typification of incorrect event data. For this purpose, they identify and categorize the typical causes during data acquisition (examples: [15–17]) and data transmission (examples: [18–23] based on a detailed literature analysis (Consideration of more than 110 papers). In conclusion, the authors transfer these causes into specific occurrences of incorrect event data, which are subsequently typified. The definition of data quality typically refers to data that is appropriate for use by the data consumer [24], based on the widely accepted definition from quality management [25]. In the context of decision-making that is based on data, the data consumer refers to the model that utilizes the data as input. Hence, fitness for use is determined by the ability of the data to be suitable for the model to function as intended. Despite the definition emphasizing that data quality can only be evaluated based on the intended application, many studies follow a generic approach that focuses only on the data and not its specific application. Based on this knowledge, Schröer et al. [5] have developed

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a systematization approach to address inadequate data quality. They have identified four primary strategies (improving data sources, cleaning up the database, improving the database, and working with insufficient data quality) based on the data value chain from gathering to using. These strategies have been further elaborated to address specific challenges. Specifically, in the context of supply chain event management, the methods of cleaning up and improving the database can be particularly useful. However, these methods require the identification of incorrect event data. The identification of deviations is a well-established research area, and various methods have been developed that can be classified into several categories. One category includes graphical methods, where humans identify deviations in the visual representation [26]. Examples of such methods include frequency distributions [27] and box-andwhisker plots [28]. The second category is comprised of static methods, which involve various statistical tests (such as the Dixon test, Grubbs test, and Nalimov test [29, 30]). The third category includes all approaches that rely on predefined business rules. Based on these rules, which specify the formal representation and processing of business processes [31], data is often checked for external and internal formal correctness, as well as content plausibility [32]. The detection of deviations through machine learning represents the fourth category that has gained significant importance in recent years. To detect anomalies in larger datasets, several subcategories can be identified [33]: statistical methods, density-based methods, distance-based methods, clustering methods, ensemble-based methods, and learning-based methods. Some studies have taken different approaches to individual data acquisition technologies (especially RFID). However, as the works focus on technical aspects [34, 35] or do not consider deviations based on insufficient data quality [36], they can only be partially transferred to the systematic and generally valid consideration of EPCIS data with insufficient data quality. It is crucial for these methods that the data sets contain the corresponding data attributes required to identify the deviations. In some cases, data enrichment may be necessary before utilizing machine learning approaches. The analysis of the state of the art shows a research gap in the systematic consideration of necessary data attributes for the identification of event data with insufficient data quality (cf. Figure 1). This paper attempts to close this gap by building on existing descriptions of event data with insufficient data quality.

3 Research Methodology Based on the introduction and the theoretical background, the challenge in the use of supply chain event management is the handling of data of insufficient quality. The challenge arises primarily from the need to distinguish between standard events, exceptional events, and events with insufficient data quality during the evaluation. To facilitate this, this paper lays the foundations for the identification of event data with insufficient data quality, which apply to all parts of the supply chain in which two actors apply supply chain event management. Specifically, this leads to the following research question: Which data attributes are necessary for the identification of incorrect event data in comparison to standard events and exception events?

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Accordingly, the paper aims to analyse which data attributes can already be used to identify event data of insufficient data quality and which information is still missing. The resulting gap enables the derivation of further necessary data attributes. This objective aligns with the methodology of gap analysis. A gap analysis aims to identify discrepancies between the current state and the desired state, and subsequently formulate strategies to bridge the identified gap [37]. Gap analyses find applications in various research domains, such as service engineering [38] and technology planning [39]. The initial step in conducting a gap analysis involves defining the target state, followed by assessing the current state. Once the identified gap is established, appropriate measures can be derived. [40].

4 Gap Analysis for Derivation of the Data Attributes The gap analysis carried out in this paper was carried out identically, although the event data considered were narrowed down and specified beforehand. This results in a four-step procedure. In the first step, the event data types with insufficient quality are subdivided and limited based on the complexity of identification. The event data considered further are specified in subtypes. In the second step, strategies for identifying the subtypes are described. The third step includes the current data attributes of the event standard and their restriction in the subtypes. The last step presents the analysis of the gap and shows which total data attributes are necessary and how these can be supplemented, for example. 4.1 Subdivision and Concretisation of Event Data Types Janßen et al. [6] classify forms of occurrences of event data with insufficient data quality into different categories such as missing, redundant, unnecessary, outdated, wrong and inconsistent data and describe them by using concrete examples. These error types can either affect specific attributes or the entire data set. When determining the necessary attributes, the focus should be on error types that cannot be identified using strict rules. Such errors can be detected using existing attributes of the event standard. In concrete terms, this means: if a data attribute is missing, it can be easily identified by checking if the event is complete, without needing complex rules. However, if an event is missing, it may require complex logic to detect. But since the fundamental challenge of supply chain event management is the correct evaluation of received event data, the type of missing event will also not be considered further. To store only relevant data, redundant events (events recorded twice) must be detected by comparing the incoming event with the corresponding database. According to this, there is no need for complex logic. Unnecessary events (which don’t add value or help in decision-making) and outdated events (which are not useful for real-time supply chain event management) are also excluded from consideration. The remaining occurrence of incorrect event data wrong and inconsistent can be specified. Wrong event data are data records that can also be identified as incorrect on their own. The reason for this can be a non-existent location, a time in the future or similar. Inconsistent event data, on the other hand, appear error-free when viewed individually

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and can only be identified as incorrect when compared with other events. For example, an event with location A is not noticeable when viewed individually. When compared to the supply chain upstream and downstream events that all take place at location B, or when compared to historical data of the same step in the supply chain, inconsistency can be identified. It should be noted at this point that wrong and inconsistent are not necessarily free of overlap. For example, a data attribute can contain fictitious data in all events and is therefore clearly wrong, or it is filled with possible data that does not correspond to reality and is therefore inconsistent. If it now contains isolated fictitious data, it can be assigned to the type wrong as well as to the type inconsistent. Since there are already additional existing studies on forms of occurrence of incorrect data and their causes in other contexts [41–44], these findings can be transferred to the event data. Concretely this means under consideration of the already excluded types that the following error occurrences are to be considered: • • • •

Incorrect character string (reference object does not intend). Incorrect character string (another reference object). Missing capture (data attribute contains too few entries (e.g., list). Additional objects capture (data attribute contains too many entries (e.g., list)).

By assigning the characteristics to the error types (inconsistent, wrong), sub-error types can be formed. These are shown in Fig. 2. The inconsistent data related to events can be categorized into three subtypes. Type A comprises all errors that arise when an incorrect character string is utilized to describe another existing object. The error subtype inconsistency type B transpires when not all the intended objects are recorded in a data attribute (missing capture). If additional objects are captured, the error subtype inconsistency type C is referred to subsequently. Wrong event data occurs when an incorrect character string is used to describe a nonexisting object. This can also happen when certain characters are missing. Such features can also emerge in other data attributes of an EPCIS event that illustrate the values or states of the correct object, like the event time and bizStep attributes (cf. Figure 1). Hence, the wrong error type can be segregated into two subtypes. Type A arises when an event relates to a non-existent object, while type B arises when an incorrect, non-existent state or value is assigned to the correct object.

Fig. 2. Overview of the sub-error types

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4.2 Consideration of Identification Strategies (Target Data Attributes) To determine the target data attributes, different identification strategies for the previously described sub-fault types are considered. The data attributes are therefore derived from the necessary information requirements of the strategies. In principle, different strategies can be used to identify the various incorrect data attributes, which are described and subsequently evaluated in the following (cf. Figure 3). The first strategy is matching against a predefined catalogue, which is based on a set of fixed rules. A catalogue is created in advance that includes all possible entries for a data attribute, and the event data is compared with this catalogue. If there is a deviation from the specifications, it is considered an incorrect data attribute. For instance, the EPCIS standard’s CBV is a catalogue that demonstrates this strategy. The effectiveness of this strategy depends on the granularity of the catalogue. Creating a single catalogue for the entire supply chain is not very effective because it needs to cover all possible data attribute entries, and only a few errors can be detected. However, if a catalogue is created for each event-generating step in the supply chain, the possible data attribute entries can be narrowed down, making it easier to detect errors. This strategy is primarily useful for identifying incorrect event data since an incorrect entry cannot be listed in the catalogue. However, creating a catalogue for each data attribute can be time-consuming and expensive. As the complexity of the supply chain increases, listing all possible EPCs becomes more expensive, making it challenging to detect wrong event data type A. But, since the CBV already includes all possible entries, this strategy can be effectively implemented to identify the wrong event data type B since these entries are not dependent on the object. However, listing all entries in a general catalogue makes it difficult to identify inconsistencies during comparison. Creating a step-specific catalogue with further restrictions, such as specifying the expected number of entries in the childEPCs data attribute for an aggregation event, can help identify inconsistent event data types B and C. Nevertheless, if there is more than one possibility, it is not always possible to identify all inconsistencies reliably. Another strategy for identifying incorrect data is to compare different data attributes within an event, with discrepancies indicating an anomaly. However, this method can only detect a limited number of errors without additional external information. For instance, in the EPCIS standard, comparing the childEPCs and childQuantityList attributes can detect inconsistent event data type A and type B. With additional information, such as geolocation data, it is possible to verify whether the readPoint and biz-Location attributes are located in the same geographical area, which can also be used to validate the eventTimeZoneOffset attribute. To detect incorrect data in other attributes, additional data attributes must be introduced, which requires significant effort to implement effectively along the entire supply chain. This identification strategy is too specific and limited in its current form to be effective on its own, but it may be useful as a supplement to other strategies depending on the required effort, as it allows for the identification of errors in individual events without additional information. A further method to identify incorrect event data is to compare them with process step-specific events. In this context, process-step-specific refers to events that occurred in the same process step of the supply chain. These events relate to different objects but are associated with the same process step. For instance, events that occurred at

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a specific station are compared with each other. By doing so, a standard event can be generated, representing the average data attribute entries of the process step. New events can be compared with this standard, and any deviation indicates an error. This strategy can detect inconsistent event data types B and C, such as a consistent number of registered childEPCs at specific stations. It can also detect inconsistent event data type A, for example, if the expected object type bizStep and disposition are not in line with the previous events. However, identifying errors at stations where different data attribute entries are possible can be challenging. Additionally, wrong event data type A can only be identified if the incorrect EPC deviates from the standard due to its number of characters. Incorrect EPCs that conform to the standard cannot be distinguished from correct EPCs that occur irregularly at the station under consideration. A catalogue like the CBV can be created, allowing for the identification of wrong event data type B with a sufficient number of recorded events. However, station-specific errors, which occur continuously at a particular station and become the expected entry, cannot be identified using this strategy. An example of such an error is the output of an incorrect time zone due to a system error. Since EPCIS events are assigned to a specific station with the data attribute readPoint, incoming events can be sorted, and this identification strategy can be applied without additional information. The final strategy discussed in this section involves matching object-specific events. Unlike the previously discussed station-specific events, this approach considers all events that can be linked to a particular object. This means that the object is tracked throughout the entire supply chain and all related events are gathered and analyzed as a single “case”. Within this case, the events are compared and checked for any anomalies. Inconsistent error data type A can be identified using this method, such as an EPC that appears in one event but not in the preceding or following events. Expectations for individual event attributes, such as eventTime or bizstep, can be determined by examining the linked events. For example, some events must follow a chronological order or a packing step must be followed by an unpacking step. This approach can also identify inconsistent error types B and C. An object that is captured but not recaptured in subsequent events or an object that is unrecaptured but appears again later in the case can both be identified. Wrong EPCs can also be detected due to their unique occurrence in the case. An error in the dimension when of an EPCIS event, such as entering the year 1900 in eventTime, can be detected using this approach because a wrong event cannot be chronologically classified. However, a comparison with the Common Business Vocabulary (CBV) is necessary to reliably detect errors in attributes such as bizStep or disposition since a single case does not contain enough data to generate a comprehensive catalogue for comparison. The suitability of the different strategies depends on the sub-error types. The following Fig. 4 shows this clearly. The evaluation shows that identification strategies are necessary for all analysis values to recognise the various subtypes of erroneous event data. Accordingly, the following information can be derived in summary as information requirements: Event data attributes (strategy: (2)), predefined catalogue of values for individual data fields (strategy: (1)), information about process step-related events (strategy: (3)), information about object-related events (strategy: (4)).

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Fig. 3. Suitability of the identification strategies for the sub-error types

4.3 Analysis of the Sub-Error Types (Current Data Attributes) The existing information availabilities (current data attributes) are represented by the EPCIS standard (version 1.2) in combination with the predefined and clear vocabulary, the Core Business Vocabulary (CBV) [14]. According to the EPCIS standard, the following data attributes form the basis of the event types considered (only the obligatory attributes) [45]: eventID, eventtype, epcList, parentID, childEPCs, inputEpcList, outputEpcList, eventTime, recordTime, eventTimeZoneOffset, readPoint, bizLocation, bizStep, disposition and action. Furthermore, in the considered event types object event, aggregation event and transformation event, only the data attributes eventtype, eventTime, eventTimeZoneoffset and action are not optional (cf. Figure 1). Figure 4 provides an overview of the data attributes according to the EPCIS standard, including descriptions. 4.4 Deriving the Essential Data Attributes (Gap Analysis) The comparison of target data attributes and current data attributes reveals a corresponding gap in information needs and availability. When examining the identification strategies, it becomes apparent that the first two strategies (strategy (1); strategy (2)) does not require any expansion of the data attributes beyond the EPCIS standard (version 1.2) and the corresponding CVB. However, to identify all types of incorrect events effectively, the other two strategies are also required. This means that for the identification of all considered faulty event data the following two data attributes must be added besides the EPCIS standard (see Fig. 1): • Case-ID 1: All events related to an object (including aggregated and transformed objects) are assigned the same Case-ID 1 (strategy (4)). • Case-ID 2: All events with the same process flow within Case-ID 1 are assigned the same Case-ID 2 (strategy (3)).

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Fig. 4. Data Attributes of the EPCIS standard [45]

Gerke [46] proposed a method for generating Case-ID 2 in the context of process mining, which can be modified for this case. The EPCIS2MXML algorithm, which is modified for this purpose, is based on graph theory and involves the creation of an overall graph showing the relationships between events and EPCs. Nodes are created for each event and each EPC in the case of an ObjectEvent. Edges are created between the nodes based on the action value of the event. For example, an edge is created between the event node and the EPC node if the action value is “add”, while an edge is created between an existing EPC node and the corresponding event if the action value is “observe” or “delete”. Similarly, in the case of an AggregationEvent with an action value of “add”, nodes are created for the parentID and all childIDs. Nodes are created for the parentID and all childEPCs and edges are drawn between them. In the case of TransformationEvents, nodes are created for each EPC of the outputEPCList and edges are drawn to connect them to inputEPCs and the event node. Figure 5 shows an exemplary simplified graph, which can be produced accordingly. After the graph is created, all subgraphs are formed in the next step. Subgraphs are all connected nodes of the graph that are detached from the rest of the graph or the other subgraphs. Since new serial numbers are used for each run of a supply chain, in the error-free case a subgraph represents an entire run of the supply chain. In faulty supply chains, where graph formation is affected by faulty EPCs, for example, subgraphs are generated that are structurally different from each other. Thus, different Case-IDs 2 are assigned for the different subgraphs, so that all events of a subgraph receive the same Case-ID 2. In addition to distinguishing whether two runs are identical or different, structurally identical runs also get distinguished from each other. All runs with the same Case-ID 2 also receive a Case-ID 1.

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Fig. 5. Exemplary graph, which was created by a modified EPCIS2MXML algorithm.

5 Conclusion and Outlook The idea of Supply Chain Event Management offers businesses the prospect of streamlined and expedited decision-making, facilitated by timely notifications of significant events within the supply chain. In practice, this presents a valuable opportunity for companies, particularly given the rising expectations of customers and unpredictable market circumstances, to leverage the capabilities of SCEM and enhance their competitive standing over the long term. However, companies rely heavily on a dependable database that furnishes the requisite data promptly and with an acceptable level of data quality. This paper adds value to the conceptual and practical considerations of supply chain event management by examining erroneous event data. For the further development of event-based systems, it is essential to develop methods for filtering out incorrect event data. Through the GAP analysis and the systematic consideration of identification strategies of event data with insufficient data quality, the necessary attributes of event data could be derived. This also made it possible to answer the research question of which data attributes are necessary for the identification of incorrect event data. Based on the findings of the GAP analysis and the proposal for generating the two missing data attributes, the practical development of algorithms for generating Case-ID 1 and Case-ID 2 can now be advanced. In the long term, this can be used to develop an automatic filter system upstream of the core functions of simulation, control, and measurement to avoid incorrect reactions due to incorrect event data. For this long-term goal of automatic filtering, however, there is a need for further research. The limitations of the work carried out lie in the concretisation and restriction before the GAP analysis as well as the use of the EPCIS standard version 1.2. Accordingly, in addition to the use of the EPCIS standard 2.0, it should be examined how the findings can be transferred to all error types. In addition, the event data of insufficient data quality should be compared not only with standard events but also with the most diverse exception events, so that the basis for an automatic classification is developed.

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In addition, the influence of adding data attributes to the various automatic deviation detection procedures could be explicitly evaluated.

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Resilience Configurator for Procurement Maria Spiß(B)

, Tobias Schröer, and Günther Schuh

Institute for Industrial Management (FIR), RWTH Aachen University, 52074 Aachen, Germany [email protected]

Abstract. The complexity and volatility of companies’ environment increase the relevance of disruption preparation. Resilience enables companies to deal with disruptions, reduce their impact and ensure competitiveness. Especially in the context of procurement, disruptions can cause major challenges while resilience contributes to ensuring material availability. Even though past disruptions have posed various challenges and companies have recognized the need to increase resilience, resilience is often not designed systematically. One major challenge is the number of potential measures to increase resilience. The systematic design of resilience thus requires a detailed understanding of domain-specific measures. This also includes an understanding of the contribution of these measures to different resilience components and their interdependencies. This paper proposes a systematic approach for configuring resilience in procurement which enables the evaluation and selection of resilience measures. Based on a resilience framework, a resilience configurator is developed. The basis of the configurator are resilience potentials that have been characterized and clustered. Overarching approaches to design resilience and indicators to evaluate resilience are presented. Moreover, a procedure is proposed to ensure practical applicability. To evaluate the results two case studies are conducted. The results enable companies to systematically design their resilience in procurement. Keywords: Resilience · Procurement · Configuration · Disruptions · Resilience Principles

1 Introduction The environment in which companies operate is characterized by increasing complexity and volatility. In such an environment, the ability to deal with disruptions is a critical success factor [1, 2]. Past disruptions like the COVID-19 pandemic or the Ukraine war have posed major challenges to companies and their supply chains [3]. The negative impacts that these disruptions have on companies reveal an insufficient preparation to deal with disruptions [4, 5]. Even though disruptions impact different company operations, effects in the area of procurement are especially critical [3, 6]. Taking into account the interdependencies between different supply chain actors due to low vertical integration, globalization and complex supply chains, the importance of dealing with disruptions in procurement increases [3]. © IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 699–713, 2023. https://doi.org/10.1007/978-3-031-43688-8_48

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In this context, building resilience is a means of dealing with disruptions. Companies with high resilience can better cope with even unexpected disruptions. Thus, resilience becomes a strategic success factor [7, 8]. Many companies recognize the growing significance of building resilience [7]. However, resilience is often not designed systematically [1, 5]. The systematic design of resilience requires a detailed understanding of specific design measures and practical support for selecting and combining these measures [9]. This paper, therefore, proposes a systematic approach for configuring resilience in procurement. The remainder of this paper is organized as follows: Sect. 2 reviews the literature on the design of resilience. Section 3 presents the developed approach for the configuration of resilience in procurement and Sect. 4 discusses the validation of the configurator in practice. Section 5 gives an outlook on the application of the configurator.

2 Conceptual Background and State of the Art This section presents the conceptual background on resilience and procurement as well as existing approaches to design resilience. Moreover, the research need and requirements for an approach to design resilience are described. 2.1 Resilience and Procurement Resilience is a multidimensional concept with no common definition in the context of supply chains and organizational resilience [7]. It is strongly linked to disruptions which are temporary impacts on the performance caused by disruptive events [10]. Resilience both aims at decreasing the impact of disruptions through preventive actions as well as enabling fast reactions and recoveries after a disruption [11]. It thus influences different disruption phases. Additionally, resilience includes different strategies (reactive, proactive and concurrent) and abilities (anticipation, adaption, response, recovery and learning ability) [7]. According to Ali et al. [7], resilience definitions can obtain a narrow view referring to single phases, strategies or abilities or a broad view. Using a broad view, this work understands resilience as “the ability of a company to prepare for potential disruptions, react and adapt to disruptions as well as the ability to return to the original state or achieve a better state after the disruption” [12]. Designing resilience, therefore, aims at minimizing “the impact of disruptions through preventive measures” and returning “to the original state as quickly and cost-effectively as possible” [12]. Based on this definition and related works like the disruption profile [13] and the resilience triangle [14], the authors have proposed a framework for structuring resilience which details the individual components of resilience (see Fig. 1) [12]. The framework contains eight resilience components in three component categories (timerelated, performance-related and curve-related components). Moreover, it describes nine resilience principles as target dimensions for building resilience [12]. Time-related resilience principles include the extension of the buffer time as well as the reduction of the decision time, the response lead time and the recovery time. Additionally, the early start of the response represents a time-related resilience principle. Performance-related resilience principles refer to the damping of the maximum and the

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long-term performance reduction. Curve-related resilience principles are the reduction of the performance loss rate and the increase of the recovery rate [12].

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The generic framework can be applied to different contexts by defining the performance value, the objects under consideration and their interdependencies. Procurement covers all activities that ensure the availability of raw materials and articles that are required but not produced in-house [15]. It thus aims at securing long-term supply security [16]. For procurement, the relevant performance value considered in this work is material availability. A high material availability refers to the supply of the right material, at the right time, in the right quality and quantity, and at the right place [17]. The main object under consideration is the goods receipt. This is influenced by external input actors like suppliers and logistical service providers. The material availability in the goods receipt influences production. The interdependencies are described with the time needed for transport and the time needed for internal provision [12]. 2.2 Approaches to Design Resilience Approaches to design resilience in the literature include simulation or mathematical models as well as design frameworks. Several authors evaluate individual resilience-increasing measures using simulation or mathematical models. These approaches often use cost or service level as target values and distinguish between different scenarios with varying disruption frequencies or impacts. Kamalahmadi and Parast [18] use a two-stage mixed-integer programming model to evaluate the effectiveness of three redundancy measures (safety stock, backup supplier and supplier protection). Carbonara and Pellegrino [19] apply real options theory and simulation to analyze the trade-off between investment before a disruption and savings during a disruption for flexibility strategies. Namdar et al. [20] compare different procurement strategies and resilience measures with a mathematical model and take

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into account the risk attitude of a decision-maker. Johnson et al. [21] use optimization and discrete-event simulation to compare robustness and resilience measures regarding revenue and fulfilling customer needs. Design frameworks often contain a catalogue of resilience measures. In some cases, they are linked to resilience capacities. Examples can be found in [7, 22, 23]. In their often-cited framework, Christopher and Peck [24] identify four principles and relevant aspects within these principles as the foundation for resilient supply chains. Pettit et al. [25] propose an assessment tool. The comparison of vulnerabilities and resilience measures provides the basis for the design of resilience. Burnard et al. [26] develop a resilience configuration matrix which contains four configurations. The configurations are based on a combination of rigid or agile adaption and reactive or proactive preparation. Regarding the contribution of measures to resilience components, Benfer et al. [23] identify five mechanisms and link them to different measures while Dormady et al. [27] use production theory to analyze the contribution to resilience. Some authors especially focus on resilience in procurement. Pereira et al. [6] propose a framework that links procurement activities with enablers of resilience. Heß and Kleinlein [28] propose a maturity model and analysis concept to design resilience in procurement. The analysis refers to critical materials or risk clusters and includes the evaluation of stability and flexibility. Kähkönen and Patrucco [3] classify resilience measures of supply resilience regarding existing or new supply chain relationships (bridging vs. buffering) and short-term or long-term orientation (temporary vs. permanent). 2.3 Research Needs and Requirements for the Design of Resilience Resilience is widely discussed in the literature. Simulation-based approaches and mathematical models enable a detailed analysis of single resilience measures. However, a comparison of a large number of measures is not possible. Additionally, simulations require assumptions and simplifications. These assumptions impair the transferability. Frameworks with a catalogue of resilience measures provide a broader range of design options. However, they mostly do not include further evaluation of these measures. Furthermore, the identified measures are often too generic and not directly applicable in individual domains like procurement. The existing approaches do in most cases not contain a comprehensive characterization of resilience measures. Their contribution to specific resilience components is not considered. Moreover, interdependencies between different resilience components are not sufficiently taken into account. Thus, the existing approaches do not sufficiently support the selection and combination of resilience measures. For the practical configuration of resilience in procurement, the consideration of specific design measures is required. These measures must be detailed enough to be implementable in procurement. Moreover, the approach needs to enable the selection and combination of design measures. To do so, different design measures need to be compared and evaluated. Thereby, the contribution to resilience components should be considered as this enables a holistic design of resilience. To ensure practical applicability, a systematic procedure to derivate design recommendations is required.

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3 Development of a Resilience Configurator for Procurement In this section, the development of a resilience configurator for procurement is presented. The development is based on an applied research approach which focuses on a practical problem and uses case studies to evaluate the results. First, the structure of the resilience configurator is derived. Moreover, the potentials to design resilience in procurement are described. This serves as the foundation for the approach that ensures the practical applicability of the resilience configurator. 3.1 Structure of the Resilience Configurator The analysis of existing approaches demonstrates the research need and requirements for the design of resilience in procurement. Especially, an approach that supports companies with the systematic design of resilience is currently missing. To deal with the described requirements, a resilience configurator for procurement is developed. Configuration describes the composition of an object or process from partial elements under consideration of restrictions. A configurator thus structures the different elements that are relevant to design resilience. Moreover, it allows a company-specific design of resilience as several compositions can be derived. Configuration in the context of resilience refers to the selection and combination of design measures. As described above, different authors identify design measures for resilience on various levels. While a lot of existing works focus on generic success factors or capabilities, the resilience configurator presented in the work deals with specific design measures that can be implemented in procurement. In general, resilience can be increased through proactive measures that are taken before the occurrence of a disruption or through reactive measures that are taken once a disruption has occurred. Additionally, there are reactive measures that have to be prepared before a disruption. The specific design measures considered in this work focus on measures that are built in strategically and thus support companies to prepare for disruptions. They can either directly contribute to increased resilience or create scope for actions in the case of a disruption. Thus, the specific design measures are called resilience potentials. The resilience potentials present a key dimension of the resilience configurator. Furthermore, the design of resilience must refer to the different aspects of the multidimensional resilience concept. The resilience principles of the described framework, therefore, represent the second key dimension of the resilience configurator. By linking these dimensions the resilience configuration takes into account the various design measures and the different components and target directions for the design of resilience. The configuration of resilience requires the selection and combination of resilience potentials. To do so, different resilience potentials need to be compared and evaluated based on their contribution to resilience. A detailed understanding of the contribution of different resilience potentials to the resilience principles thus serves as the foundation for developing the resilience configurator. Resilience potentials can be implemented for specific purchased articles or article groups. Thus, the configuration of resilience in procurement refers to purchased articles or article groups.

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The structure of the resilience configurator and the key dimensions are displayed in Fig. 2.

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The set of implemented resilience potentials for a purchased article is called a resilience portfolio. The resilience configurator enables the analysis and evaluation of the resilience portfolio for the purchased article under consideration. The resilience configurator also supports the derivation of design recommendations for the adaption of the resilience portfolio. Before describing the approach for applying the resilience configurator in practice, resilience potentials in procurement are presented. 3.2 Resilience Potentials in Procurement To develop the resilience configurator, a catalogue of resilience potentials in procurement is derived based on existing literature. The literature analysis both took into account concrete resilience measures as well as generic resilience measures that can be specified for application in procurement. Resilience potentials that are considered within the resilience configurator need to focus on procurement, be practically applicable, contribute to a long-term design of resilience, refer to the resilience on a company level and display a process reference. Resilience potentials that focus on the management systems or the company culture are not considered within the resilience configurator. Dimensions of procurement strategies and the underlying parameters are used to structure the potentials and specify generic potentials for the application in procurement. Melzer-Ridinger [29] identifies four strategic dimensions: procurement program policy, supplier policy, contract policy and storage policy. Each dimension contains several parameters that determine the procurement strategy. The procurement program policy defines the manufacturing depth, opportunities for standardization and substitution of articles and quality requirements. The supplier policy shapes the supplier base and structure as well as the cooperation with suppliers. Contractual terms between buyers

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and suppliers are determined within the contract policy. The storage policy determines the need for warehouses and the supply method. Resilience potentials in the dimension of procurement program policy include using a make-and-buy strategy [19] and the identification of substitutes [27]. In the context of the supplier policy the identified resilience potentials concern suppliers and transportation. Regarding suppliers, the potentials refer to the number of suppliers (e. g. multiple sourcing, identification and / or qualification of alternative suppliers), the geographical design of the sourcing network (e. g. geographical diversification, regionalization of the supply chain), the collaboration with suppliers (e. g. information sharing, collaborative planning) and the supplier selection [7, 22–24, 28]. Resilience potentials related to transportation include using alternative transport options, identification of alternative transport options, holding additional transport capacities and real-time monitoring of the transport [7, 22, 30]. Resilience potentials within the dimension of contract policy are flexible contracts, backup contracts and examining procurement on the spot market [18, 20, 25]. Related to the storage policy resilience potentials concern corporate sourcing, a high order frequency, planning buffer times between demand dates of procurement and production and safety stock [19, 21]. Besides this structure, resilience potentials can be characterized by its effect on resilience. A resilience potential can directly contribute to an increase in resilience, create scope for action after a disruption or both. Moreover, resilience potentials differ in their scope of impact. While some resilience potentials are only useful in case of disruptions from certain areas like suppliers or transportation, other resilience potentials are independent of the disruption area. Beyond that, the effect on resilience can be characterized by analyzing the impact on the different resilience principles. For each resilience principle, the characterization differs between a direct influence, an indirect influence and no influence. Based on this characterization a cluster analysis was performed to identify resilience potential categories. First, the characterization features were scaled and transformed into numerical values. Then, the clustering was performed using a hierarchical cluster algorithm (weighted average linkage method) and manhattan distance. The analysis resulted in five categories which differ in their contribution to resilience: The first category secure alternatives contains potentials which provide a scope for action and result in fast reaction and recovery. The category potential alternatives also provides a scope for action but the implementation and contribution to resilience depend on other supply chain actors. The third category is labelled transparency. This category enables early responses as well as a targeted selection and design of responses due to high information availability. The category decoupling contains resilience potentials that help to ensure that disruptive effects from other objects in the supply chain do not have a direct impact on the company under consideration. The last category is labelled direct compensation and leads to an extended buffer time. 3.3 Approach for Practical Application The approach for applying the resilience configurator in practice enables companies to systematically design their resilience in procurement. Based on the current status of the

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resilience portfolio the approach helps a company to derive improvement possibilities. The approach consists of three superordinated steps (see Fig. 3). Definition of the scope of analysis

Analysis of the existing resilience portfolio

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Fig. 3. Superordinate steps for applying the resilience configurator

The application of the resilience configurator starts with defining the scope of analysis. This is followed by an analysis of the existing resilience portfolio. The last step deals with the derivation of design recommendations. Each step is described in detail in the following. Definition of the Scope of Analysis As described above, the resilience configurator refers to a purchased article or an article group. Thus, the analysis object needs to be defined in the first step. Systematically designing the resilience portfolio can either be triggered by a specific disruption that has caused a lack of material availability or by the general request for an increase of resilience in the area of procurement without a particular disruption reference. In the first case, purchased articles that have experienced a lack of material availability in the past can be chosen for further analysis. In the second case, a relevant purchased article has to be chosen. A company should thereby focus on articles that are critical input materials. If the analysis should consider a group of purchased articles, the individual purchased articles need to follow similar procurement strategies. If a company already works with an article classification, this can be used to identify the analysis object. Otherwise, methods such as ABC- or XYZ-analysis can be used to determine purchased article groups. Besides the identification of an analysis object, the user of the approach needs to identify the relevant goals for procurement. These influence the company-specific design recommendations. The overarching goal of procurement is to secure the long-term supply [16]. Despite that, strategic procurement aims at reducing costs, creating flexibility, ensuring material quality, shortening the time-to-market and maintaining the autonomy of the company [15, 29]. As these are conflicting goals it is important to understand which of the goals is given more weight. Analysis of the Existing Resilience Portfolio The analysis of the existing resilience portfolio creates transparency of its current status. It starts with identifying the resilience potentials that are already implemented for the object under consideration. This step is done using the structured resilience potential catalogue that has been described above. If the company wants to consider resilience potentials that are not yet part of the resilience catalogue, these potentials have to be characterized and assigned to one of the categories first. The next step is the evaluation of the resilience portfolio. To do so, the authors propose indicators that characterize the resilience portfolio. Based on existing approaches to design resilience as well as design principles from strategic corporate planning,

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project portfolio planning and product development, the authors have defined overarching approaches for resilience design. These serve as the foundation for defining indicators that enable the evaluation of a resilience portfolio and for deriving design recommendations. The design of resilience is highly influenced by a company’s specific context and general conditions. While certain measures to increase resilience may work well for one company, they may not fit for another company. Individual measures can thus not be classified as right or wrong but must be consistent with the strategy and considered in the overall context. One key aspect of designing resilience in a company is therefore its fit to the overall goals and strategy of the company. In the context of procurement, the consideration of the procurement strategy is especially important. In the context of product portfolio planning, creating a balanced and diversified portfolio presents a major goal. Applied to the design of resilience, balance in a resilience portfolio refers to the different resilience principles. On a high level, a resilience portfolio should contribute to both robustness and agility [13]. The second overarching design approach is therefore defined as follows: When designing resilience all principles and their balance should be taken into account. When designing products or technical systems, reliability is an essential aspect. Reliability refers to the extent to which functions or components of a system do not fail during operation. It depends on the design of the system as well as on the operational influences [31]. Transferred to the design of the resilience portfolio, it should be considered how reliable the individual components of the portfolio contribute to improved resilience. The third design approach is therefore defined as taking into account the reliability and availability of the resilience portfolio in case of disruptions. Designing resilience is strongly related to limited resources which require trade-offs. The limitations especially refer to financial resources that need to be invested to establish resilience-increasing measures. In the design, it must thus be decided in which measure the limited resources will be invested. Analyzing resilience measures must consider the related costs. Closely related to the fourth overarching design approach, the last approach refers to the consideration of synergies. Synergies exist both between different resilience principles as well as between resilience potentials. The indicators for analyzing the resilience portfolio refer to the two key dimensions of the resilience configurator. Moreover, they are strongly linked to the second and third overarching design approaches. On the one hand, the indicators enable an assessment of the balance of the resilience portfolio concerning the resilience principles and the resilience potentials. On the other hand, the indicators are used to determine the reliability of the resilience portfolio. Regarding the balance of the resilience portfolio, three indicators are defined. The first indicator is called contribution rate. This indicator illustrates for each resilience principle what percentage of the total contribution to resilience in the portfolio takes place in the resilience principle. The contribution rate indicates whether certain resilience principles are not impacted or are impacted very little by the resilience portfolio. For the applicability of the resilience potentials in the event of a disruption, it is critical whether the activated resilience potentials are universally or restrictedly applicable to

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specific application scenarios. For each contribution rate, the universally applicable contribution is thus defined as the second indicator. The third indicator refers to the balance within the resilience potential categories. The distribution rate is defined as the proportion of activated resilience potentials in one category to the number of activated resilience potentials in total. Regarding the reliability of the resilience portfolio, two indicators are defined. The first indicator refers to the resilience principles. For each resilience principle, the indicator number of contributing potentials determines how many resilience potentials contribute to the principle. The second indicator refers to the resilience potential categories. The number of activated potentials determines for each category how many potentials are activated. The more potentials from a category are activated, the more likely it is that the category will be available in the event of a fault. Within the developed resilience configurator, these indicators are calculated. Based on the results the resilience portfolio is evaluated. With the help of the indicator contribution rate, the relatively strong and weak resilience principles are determined. Additionally, the resilience principles that are not addressed are identified. The range between the minimum and the maximum value of the contribution rate provides information on the balance of the portfolio. For each resilience principle with a low value of the universally applicable contribution, the underlying application scenarios have to be analyzed. The distribution rate provides information on strongly and weakly implemented potential categories. The indicators number of contributing potentials and number of activated potentials allow the determination of resilience principles and potential categories with limited reliability. Derivation of Design Recommendations The derivation of design recommendations comprises the identification of improvement potentials and the selection of resilience potentials. Improvement potentials exist in the case of weakly developed resilience principles and weakly implemented resilience potential categories, as well as in the case of limited reliability. As there are interdependencies between resilience principles, strongly implemented resilience principles can compensate for weakly implemented resilience principles. For example, the maximum performance reduction is interrelated with the decision and the response lead time. Faster reactions cause the end of the performance reduction to be reached faster. Thus, short decision and response lead time lead to damping of the maximum performance reduction becoming less important. As other examples, a long buffer time reduces the importance of a short decision time and a high damping of the maximum performance reductions reduces the importance of a short recovery time. Improvement potential exists for weakly developed resilience principles that are not compensated by strongly developed resilience principles that can be applied universally. Moreover, there is room for improvement in weakly or not implemented resilience potential categories. In cases where there is a strong focus on the categories decoupling and transparency this remains another improvement potential as these categories are insufficient when implemented alone. Further areas for improvement are those resilience principles and resilience potential categories with limited reliability. An exception applies to the category direct compensation, as this only includes a resilience potential.

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As described above, each resilience potential category is characterized by its effect on the resilience principles. Before specific resilience potentials are selected, the resilience principles that have been identified as improvement potentials are translated into resilience potential categories. This step is done, as resilience potentials influence several principles and the selection is thus easier within the potential categories. The selection of resilience potentials is highly company specific. However, there are selection criteria that are generally applicable and linked to the overarching design approaches. The selection of resilience potentials needs to take into account the underlying procurement strategy. Moreover, a broad resilience portfolio can be achieved if no direct substitutes are selected. In addition, the focus should be on universally applicable resilience potentials. If limited resilience potentials are used, attention should be paid to a combination of possible application scenarios. As a last selection criterion, the approach includes an overview of costs related to resilience potentials. An overview of different cost types such as logistics costs and procurement costs with its subtypes acquisition costs, order processing costs and warehousing costs and the linkage to the resilience potentials is provided for the user of the approach. For each potential, it is distinguished which costs occur before a disruption for establishing and maintaining the resilience potential and which costs occur for the use of the potential in the event of a disruption. Based on these criteria the user can then determine resilience potentials that can improve the existing portfolio. After selecting further resilience potentials, the indicators for analyzing the resilience portfolio can be calculated again. This enables the comparison of the existing and the adapted resilience portfolio.

4 Evaluation of the Results To evaluate the results two case studies with German companies have been performed. Case studies enable the comprehensive presentation of social reality and the existing interrelationships as well as the derivation of practically relevant conclusions [32, 33]. As the approach is primarily aimed at manufacturing companies, two manufacturing companies were chosen. To evaluate whether the approach is suitable for companies of different sizes, the evaluation was carried out with a medium-sized and a large company. Both companies had experienced challenges in the material availability of purchased goods in the past. The first case study was performed with a medium-sized manufacturing company from the medical technology industry, while the second case study was carried out with a large company which develops and produces industrial automation technology. Within the case studies, the three superordinate steps for applying the resilience configurator (see Fig. 3) were carried out with examples from the companies. To apply the resilience configurator, the results were implemented in an Excel tool. After identifying the implemented resilience potentials, the indicators are calculated in the Excel tool. For the analysis of the indicators and the derivation of design recommendations, the authors provided templates to support the application. The application of the resilience configurator and the results were discussed with company experts from procurement and risk management. After applying the resilience configurator the company experts were asked to evaluate the content and formal requirements. The analysis object in the first case study was stainless steel bars with which the company had experienced challenges regarding material availability in the past. Already

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implemented resilience potentials included flexible contracts, safety stock at a central location, identification and qualification of alternative suppliers, examining procurement on the spot market, collaboration with suppliers, a high order frequency and planning buffer times between demand dates of procurement and production. The analysis of the existing resilience portfolio revealed that the portfolio is not well balanced regarding the resilience principles and strongly focused on restrictedly applicable potentials. Additionally, the category direct compensation is not implemented. The reliability of the resilience portfolio is restricted for the resilience principle buffer time extension. The design recommendations thus focused on the resilience principles buffer time extension and performance loss rate reduction and the resilience potential category direct compensation. As a result, the implementation of safety stock and resilience potentials from the category decoupling were discussed. The second case study focused on the procurement product group electronic standard components which are used in a variety of series products. For this product group, 11 resilience potentials are already implemented which cover all resilience potential categories. The analysis of the portfolio demonstrates that a major focus lies on the resilience principle decision time reduction. Apart from that, the portfolio is well-balanced regarding the resilience principles. Even though there is at least one resilience potential from each category implemented, the portfolio displays a strong focus on the category potential alternatives. Additionally, the reliability is restricted for the performance loss rate reduction and secure alternatives. The strongly implemented resilience principles decision time reduction and damping of the maximum performance reduction can compensate the weaker implemented resilience principles response start shortening and buffer time extension. However, there are improvement possibilities for the category secure alternatives and the principle performance loss rate reduction. As a possible resilience potential for the extension of the portfolio, the supplier selection was identified. Additionally, the dimensioning of the already implemented potential flexible contracts was discussed to strengthen the category secure alternatives. All experts considered the approach to be comprehensible and understandable and the results were rated as consistent and free of contradictions. The experts from both case studies emphasized that the resilience configurator significantly contributes to a systematic design of resilience. Especially the structured resilience potential catalogue adds value to the design process. The integration of the results in a software-based tool was stated as a major improvement possibility. Additionally, the results need to be integrated into company-specific processes.

5 Summary and Outlook The systematic design of resilience requires a detailed understanding of domain-specific design measures. It also needs to take into account the contribution of measures to different resilience components. This sets the foundation for selecting and combining resilience measures. Existing approaches do not sufficiently support the systematic design of resilience in procurement. Therefore, a resilience configurator has been developed. Resilience potentials and resilience principles are the key dimensions of the configurator. To evaluate a resilience portfolio, overarching approaches for resilience

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design and indicators are defined. Additionally, a systematic procedure is proposed to ensure practical applicability. The configurator thus enables the evaluation and design of company-specific resilience portfolios in procurement. To validate the proposed resilience configurator in practice case studies with manufacturing companies were performed. The evaluation results demonstrate that the developed approach is suitable for the systematic design of resilience and can significantly support companies in this process. A limitation is the restricted number of case studies. Moreover, the approach only allows a qualitative analysis of the company’s resilience and it is only applicable in the procurement domain. To further validate the results, more case studies can be conducted. Further case studies should focus on the long-term effects of changes in the resilience portfolio and consider a long-term evaluation. Moreover, further research is needed to integrate the proposed configurator into a software-based tool and extend the content to other company domains. Acknowledgements. Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC-2023 Internet of Production – 390621612.

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A Proposal of Resilient Supply Chain Network Planning Method with Supplier Selection and Inventory Levels Determination Using Two-Stage Stochastic Programming Hibiki Kobayashi1(B) , Toshiya Kaihara1 , Daisuke Kokuryo1 , Rina Tanaka2 , Masashi Hara2 , Yuto Miyachi2 , and Puchit Sariddichainunta2 1 Graduate School of System Informatics, Kobe University, 1-1 Rokkodai-Cho, Nada,

Kobe 657-8501, Hyogo, Japan [email protected], [email protected], [email protected] 2 Lion Corporation, 1-3-28 Kuramae, Taito 111-8644, Tokyo, Japan {r-tanaka,h-masashi,puchit}@lion.co.jp

Abstract. The importance of risk management has been pointed out in supply chain management which stably supplies products with considering economic efficiency. Supply chain network plan usually consists of two-stage decision process in business environment. Two-stage stochastic programming is appropriate for decision making under uncertainly in business environment where two steps of decision processes are involved. Therefore, we propose a planning method of resilient supply chain networks using two-stage stochastic programming. To improve computational efficiency while taking uncertain future events into account, we also propose a risk optimization method to design a resilient supply chain with reducing the number of scenarios by scenario sampling. In this paper, we attempt to plan a supply chain network consisting of suppliers, manufacturers, and wholesalers by selecting material suppliers and determining appropriate inventory levels in consideration of risk. Its optimality and resilience are evaluated by computer experiments. Furthermore, we evaluate the effectiveness of the proposed method in terms of computational efficiency as well as the optimality of the solution with scenario sampling by computer experiments. Keywords: Supply Chain Network · Stochastic programming · Resilience · Inventory control · Scenario reduction

1 Introduction Recently, the influence of supply chain risks has increased in many manufacturing industries [1]. There are two types of supply chain risks: operational risks and disruption risks. The former is a risk that can occur in normal production activities such as fluctuations in demand and production timing. The latter refers to the risks caused by unexpected events © IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 714–729, 2023. https://doi.org/10.1007/978-3-031-43688-8_49

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with irregular timing and scale such as earthquakes, tsunamis, and pandemics. The latter is called supply chain disruption because the supply chain network is temporarily disrupted by various influences. Even large companies may be eliminated by unexpected risks and risk management that takes into account the entire supply chain management is necessary for survival [2]. The 2011 Japan earthquake and tsunami reduced Toyota’s production, resulting in the huge losses in profits [3]. The COVID-19 pandemic has disrupted all parts of the supply chains due to unprecedented global responses to control the virus, including border closure [2]. In risk management against supply chain disruption, our group has started research on constructing a supply chain network that can deal appropriately with supply chain disruptions. Aiming for a resilient supply chain that can withstand disruptions of varying length, impact, and probability is essential to ensure the functioning and success of the supply chain [4]. A resilient supply chain should be able to prepare, respond and recover from disturbances and afterwards maintain a positive steady state operation in an acceptable cost and time [5]. Emphasizing supply chain efficiency exacerbates disruption propagation, while emphasizing supply chain stability undermines efficiency. It is important to design, operate, and manage supply chains based on the tradeoff between economic optimality and stability, while taking future events into account [4]. Supply chain network planning usually consider two stage of decision process in a business environment [6]. The first stage considers the design of the supply chain, such as to determine facility locations and their capacity, and the second stage considers supply chain operations, such as production quantity and delivery routing. Decisions in each stage may affect the other stages and cannot be determined independently. In other words, the design and operational decisions of the supply chain must be considered simultaneously to consider the optimality of operations in a disruption risk environment. There are two approaches to planning a resilient supply chain [7]. One uses stochastic programming, and reference [6] proposes two-stage stochastic program to determine facility placement for supply chain network design under facility disruptions. However, the model assumes that risk mitigation is only by opening and closing facilities, and do not take into account inventory, which is important as a lubricant in the supply chain. The other approach uses simulation techniques, and reference [8] optimizes supply chain design considering disruptions by holding reserve inventories. However, because operational decisions in the supply chain are determined subordinately, the optimality of decision making after disruptions is not guaranteed. In this paper, a method for planning resilient supply chain networks with material supplier selection and inventory level determination is proposed for a supply chain consisting of suppliers, manufacturers and wholesalers. In order to determine appropriate supplier selection and inventory levels considering future disruption risks, a two-stage stochastic programming [6] is applied. In order to obtain a solution with appropriate computational time and accuracy even if the problem scale is increased, we also propose a computational method that introduces Latin hypercube sampling [9], which extracts scenarios uniformly from the entire scenario space, to the proposed method. In computational experiments, the proposed method is compared with the conventional exact solution method in terms of solution accuracy and computation time for selecting material suppliers and appropriate inventory levels.

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2 Target Model and Risks This chapter provides an overview of the targeted supply chain and disruption risks. 2.1 Target Supply Chain This paper focuses on a supply chain network consisting of multiple suppliers, one manufacturer including multiple factories and distribution centers, and wholesalers, respectively from the perspective of a manufacturing company. Figure 1 shows an overview of target supply chain network. The target is determined through discussions with the collaboration company that mass-produces daily necessities.

Fig. 1. Targeted Supply Chain Network

The target supply chain network has the following characteristics. • Stock replenishment at each location follows fixed order period system. • When wholesalers place orders to the distribution center, the quantity that does not meet demand is considered out-of-stock. • The manufacturer selects material suppliers and determines appropriate inventory levels, taking into account of economic feasibility in response to disruptions. 2.2 Disruption Risks In this paper, we consider a multi-period scenario of future events, assuming disruptions that occur at suppliers, factories, and distribution centers. The disruptions and scenarios are established based on reference [10]. The disruptions and scenarios are as follows: • • • • •

The facility where the disruption occurs will be deactivated. The disruption scale is expressed by the length of the stoppage period. The probability of occurrence of disruption is smaller for longer stoppage periods. Disruptions of any scale will be completed within the planning period. There are two scenarios: a normal scenario in which the disruption does not occur, and a scenario in which the disruption occurs only once during the planning period, with the length of the stoppage period varying according to the probability of occurrence set by the disruption.

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3 A Proposal of Material Supplier Selection and Appropriate Inventory Level Determination Method with Risk Resilience This chapter describes a method for selecting material suppliers and determining appropriate inventory levels using two-stage stochastic programming for planning a resilient supply chain network. 3.1 Notation The definition of the characters used in the formulation is as follows: Sets • • • • • •

S: set of suppliers (s = 1, 2, . . . , S) M: set of factories (m = 1, 2, . . . , M ) W: set of distribution centers (w = 1, 2, . . . , W ) C: set of Wholesalers (c = 1, 2, . . . , C) K: set of scenarios (k = 1, 2, . . . , K) T: set of planning periods (t = 1, 2, . . . , T )

Parameters • • • • • • • • • • • • • • • • • • •

Capss : capacity at supplier s Capmm : material inventory capacity at factory m Cappm : products inventory capacity at factory m Capww : inventory capacity at distribution center w PMm : production capacity at factory m Dktc : demand for products at wholesaler c in scenario k, period t CSsm : contract costs between supplier s and factory m CBsm : purchasing cost per unit of material from supplier s by factory m CTSsm : transportation cost per unit from supplier s by factory m CTMmw : transportation cost per unit from factory m by distribution center w CTWwc : transportation cost per unit from distribution center w by wholesaler c CPm : production cost per unit of product at factory m CLc : stockout loss cost at wholesaler c HMm : storage cost per unit of material at factory m HPm : storage cost per unit of product at factory m HWw : storage cost per unit of product at distribution center w LSMsm : transportation period from supplier s by factory m LMWmw : transportation period from factory m by distribution center w of scenario k pk : probability  1 : if supplier s operates in scenario k, period t • αkts : 0 : if a disruption occurs at supplier s in scenario k, period t  1 : if factory m operates in scenario k, period t • βktm : 0 : if a disruption occurs at facory m in scenario k, period t

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 • γktw :

1 : if distribution center w operates in scenario k, period t 0 : if a disruption occurs at distribution center w in scenario k, period t

Decision Variables  1 : if supplier s is contracted by factory m • xsm : 0 : otherwise • lmm : inventory level of materials at factory m • lpm : inventory level of products at factory m • lww : inventory level of products at distribution center w • ppktm : production quantity of factory m in scenario k at period t • bktsm : quantity of materials purchased from supplier s by factory m in scenario k at period t • qwktmw : quantity of products ordered from distribution center w to factory m in scenario k at period t • qcktwc : quantity of products ordered from wholesaler c to distribution center w in scenario k at period t Dependent Variables • amktm : quantity of materials arrived of factory m in scenario k at period t • awktw : quantity of products arrived at distribution center w in scenario k at period t • acktc : quantity of products arrived at wholesaler c in scenario k at period t • imktm : inventory quantity of materials of factory m in scenario k at period t • ipktm : inventory quantity of products of factory m in scenario k at period t • iwktw : inventory quantity of products of distribution center w in scenario k at period t • ld ktc : stockout quantity of wholesaler c in scenario k at period t 3.2 Method of Selection of Material Suppliers and Determination of Appropriate Inventory Levels with Risk Resilience This section outlines the proposed method for planning resilient supply chain networks with material supplier selection and inventory level determination for a supply chain consisting of suppliers, manufacturers and wholesalers. In order to determine appropriate supplier selection and inventory levels considering future disruption risks, two-stage stochastic programming is applied [6]. Furthermore, in order to obtain a solution with appropriate computational time and accuracy even if the problem scale is increased, we also propose a computational method that introduces Latin hypercube sampling [9], which extracts scenarios uniformly from the entire scenario space, to the proposed method. This study proposes a method for selecting material suppliers and determining appropriate inventory levels with the aim of planning a resilient supply chain network. As discussed in Sect. 1, optimizing operations in a disruption risk environment requires simultaneous consideration of supply chain design and operational decision making. Compared

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to other methods such as deterministic model or Mixed Integer Program (MIP), twostage stochastic programming is more appropriate for decision making under uncertainty in business environment where two steps of decision processes are involved. Two-stage stochastic programming allows decisions in each stage to occur simultaneously and fits to business decision process. Two-stage stochastic programming also allows to consider the disruption risks environments by using scenarios. Therefore, the proposed method uses a two-stage stochastic programming method. Figure 2 shows a conceptual diagram of the proposed method. The disruption risks described in Sect. 2.2 are assumed in Fig. 2. A disruption scenario is assumed in the form of a branch from the no disruption scenario to another scenario in the period when the disruption occurs. Under these possible future scenarios, two stage of decision process will be considered to plan a supply chain network that minimizes the total expected future costs. First, the first stage of the decision process involves selecting material suppliers and determining appropriate inventory levels, taking into account of all possible scenarios as shown in Fig. 2. Therefore, the existence of a contract between supplier s and factory m (xsm ) and the inventory levels at each facility (lmm , lpm and lww ) are established as first-stage decision variables, which are common variables for all scenarios. Next, the second stage of the decision process, the production, purchase, and order quantities are determined for each scenario. We define the production quantity of factory m in scenario k at period t (ppktm ) and the quantity of materials purchased from supplier s (bktsm ), the quantity of products ordered from distribution center w to factory m (qwktmw ), and the quantity of products ordered from wholesaler c to distribution center w (qcktwc ) as second-stage decision variables, variables that are determined for each scenario in the two-stage stochastic programming method. Decision variables at each of these stages are considered simultaneously to plan a resilient supply chain network, aiming to minimize costs in all possible future scenarios. The evaluation of resilience focuses on the total cost and one of them, the stockout loss cost. The proposed method is compared with a deterministic method that does not consider risk. The total cost under normal conditions and the total cost and stockout loss cost when the disruption risk occurs are evaluated. This will allow us to evaluate the feasibility of designing a resilient supply chain that can provide a stable supply of products while taking economic feasibility into consideration. The proposed method uses all possible scenarios to select material suppliers and determine appropriate inventory levels. This allows the optimal selection of material suppliers and determination of appropriate inventory levels for all scenarios, while optimizing the second-stage decision, including production quantity, for each scenario assumed. However, with this method, the number of scenarios that has to be assumed becomes enormous as the types of risks and the number of facilities increase, so it is not realistic to derive an exact solution in practical time. In order to solve this problem, we propose a method to reduce the computation time by narrowing down the number of scenarios while maintaining the solution accuracy.

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Fig. 2. Conceptual diagram of the proposed method

This paper proposes a computational method that uses Latin hypercube sampling, which allows samples to be extracted evenly from the entire sampling space, and allows the selection of multiple appropriate scenarios to improve the efficiency of the computation. Latin hypercube sampling is a technique that samples evenly from the entire sampling space and avoids forming clusters [9]. The Latin hypercube sampling requires one extract from each row and column. So, the samples are spread over the entire sampling space, which consists of multiple elements as shown in Fig. 3.

Fig. 3. Example of Latin hypercube sampling

By repeatedly sampling scenarios using Latin hypercube sampling and solving problems, all scenarios are considered. This creates a supply chain network that is resilient to all possible scenarios. 3.3 Algorithm of Proposed Method The algorithm for the proposed method is described below.

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STEP1. Select the scenarios with variations in the occurrence times and facilities using Latin hypercube sampling for each disruption scale. The existence of this process is a major difference between the proposed method and the exact solution method. This sampling reduces the number of scenarios and improves computational efficiency. First, for each disruption scale, a space is generated where the vertical axis is the period of occurrence of the disruption and the horizontal axis is the facility where the disruption occurs, and then the space is divided by the number of facilities. Next, one scenario from each facility (column) and each time range (row) is selected. In this way, it is possible to sample scenarios with variations in the period of occurrence and facilities for each disruption scale. STEP2. Solve the problems of selecting material suppliers and determining appropriate inventory levels using the scenarios selected in STEP1. STEP3. Evaluate the solution obtained at STEP2. To evaluate the solution, we fix the first stage decisions of the original problem and then solve the obtained problem for other scenarios [11]. The objective function value obtained is evaluated whether the solution obtained from STEP2 is optimal in the original problem. STEP4. If the evaluation value of the solution obtained in STEP3 is better than that of the tentative solution, the solution is accepted and then go to STEP5. If not, go to STEP6. STEP5. Update the tentative solution with the solution received in STEP4. STEP6. Repeat STEP1 through STEP5 until the number of iterations N for the entire flow is reached. 3.4 Formulation In this section, the formulation for making the resilient supply chain network by considering this two-stage decision process is explained as follows: min. COST = CA + CR where CA =

CR =

 k∈K

pk





t∈T

+



CSsm · xsm



CPm · ppktm +

m∈M

 



CBsm · bktsm

s∈S m∈M

(HMm · imktm + HPm · ipktm ) HWw · iwktw +

w∈W

+

(2)

s∈S m∈M

m∈M

+

(1)

 



CTSsm · bktsm

s∈S m∈M

CTMmw · qwktmw

m∈M w∈W

+

 w∈W c∈C

CTWwc · qcktwc +

 c∈C

 CLc · ldktc

(3)

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amktm =



bk(t−LSMsm )sm , ∀m, ∀k, ∀t

(4)

qwk(t−LMWmw )mw , ∀w, ∀k, ∀t

(5)

s∈S

awktw =



m∈M

acktc =



qcktwc , ∀c, ∀k, ∀t

(6)

Dktc = acktc + ldktc , ∀c, ∀k, ∀t

(7)

   sub. to 0 ≤ ppktm ≤ min imk(t−1)m , lpm − ipk(t−1)m · βktm , ∀m, ∀k, ∀t

(8)

imktm = imk(t−1)m + amktm − ppktm ≥ 0, ∀m, ∀k, ∀t

(9)

w∈W

ipktm = ipk(t−1)m + ppktm −



qwktmw ≥ 0, ∀m, ∀k, ∀t

(10)

w∈W

iwktw = iwk(t−1)w + awktw −



qcktwc ≥ 0, ∀w, ∀k, ∀t

(11)

c∈C

⎧⎛ ⎞ ⎨   qcktw c ⎠, iwk(t−1)w 0 ≤ qcktwc ≤ min ⎝Dktc − ⎩ w ∈W  ⎫ ⎞ ⎬  − qcktwc ⎠ · γktw , ∀w, ∀c, ∀k, ∀t ⎭  

(12)

c ∈C

⎧⎛ t−1 ⎨   0 ≤ qwktmw ≤ min ⎝lww − iwk(t−1)w − qwkτ mw ⎩ m∈M τ =t−LMWmw +1 ⎞   − qwktm w ⎠, ipk(t−1)m m ∈M 

−



qwktmw

w ∈W 

0 ≤ bktsm

(13)

⎞⎫ ⎬ · βktm ⎠ , ∀m, ∀w, ∀k, ∀t ⎭

⎧⎛ t−1 ⎨   ⎝ ≤ min lmm − imk(t−1)m − bkτ sm ⎩ s∈S τ =t−LSMsm +1 ⎞ ⎛ ⎞   − bkts m ⎠, ⎝Capss − bktsm ⎠ · αkts s ∈S

·xsm }, ∀s, ∀m, ∀k, ∀t

m ∈M 

(14)

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xsm ∈ {0, 1}, ∀s, ∀m, ∀k, ∀t

(15)

0 ≤ lmm ≤ Capmm , ∀m

(16)

0 ≤ lpm ≤ Cappm , ∀m

(17)

0 ≤ lww ≤ Capww , ∀w

(18)

ppkt  m = ppk  t  m , ∀m, ∀k

(20)

bkt  sm = bk  t  sm , ∀s, ∀m, ∀k

(21)

qwkt  mw = qwk  t  mw , ∀m, ∀w, ∀k

(22)

qckt  wc = qck  t  wc , ∀w, ∀c, ∀k

(23)

The objective function (1) minimizes the sum of the cost of the first stage (CA) and the expected cost of the second stage (CR). Equations (2)–(7) are relational expressions. First, the first stage cost shown in Eq. (2) is the contractual costs with suppliers at each factory. The second stage cost shown in Eq. (3) is the sum of the expected values of production costs (term 1), purchase costs (term 2), inventory storage costs (terms 3 and 4), transportation costs (terms 5, 6 and 7), and stockout loss costs (term 8). Equations (4)–(6) show the quantity of materials and products arrived at each facility in scenario k at period t. Equation (7) is a conservation of demand equation that states that the sum of the quantity arrived and the stockout quantity of wholesaler c in scenario k, period t equals the quantity demanded. Next, Eqs. (8)–(23) are constraints. Equation (8) is a constraint on the production capacity of factory m in scenario k, period t. The upper bound is the amount of material inventory or inventory level minus the starting inventory of the product. If a disruption occurs at factory m, production will not be possible. Equations (9)–(11) are constraints on the amount of inventory at the end of the period at each facility in scenario k and period t. Inventory replenishment follows fixed order period system to purchase materials and produce and ship products each period. The initial inventory quantity at each facility is set to the inventory level set for each facility. Equations (12)–(14) are constraints on the order quantity and purchase quantity, and downstream facilities order or purchase in order from the upstream facilities with the lowest transportation cost. The upper bound in Eqs. (12) is the upstream supply availability or demand minus the remaining to be ordered. The upper bound in Eqs. (13) and (14) is the upstream supply availability or inventory level minus the starting inventory and the remaining to be ordered. In other words, after one downstream facility places an order with a group of facilities (W  , M  , S ) whose transportation costs are lower than those from one upstream facility, it places an additional order with that upstream facility if the required quantity is not met. On the other hand, after one upstream location responds to orders from a group of facilities (C  ,

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W  , M  ) whose transportation costs are lower than those from one downstream facility, it responds to an order from that downstream facility with the remaining inventory. If a disruption occurs at one upstream facility, downstream facilities cannot place orders with that facility. Equation (15) indicates that xsm is a binary variable. Equations (16)–(18) show the range of possible inventory levels for each location. Equations (19)–(23) are constraints for nonanticipativity [12]. It is shown that the second-stage decision variable in period t (1, 2, . . . , t) is the same as in the no disruption scenario (k) when a certain disruption in scenario k arises from period t + 1.

4 Computational Experiments We evaluate the optimality and resilience of the supply chain network planned by computer experiments against the risk-aware methods for selecting material suppliers and determining appropriate inventory levels described in Sect. 3. Furthermore, we compare the solution accuracy and computation time of the proposed scenario sampling-based method with those of the conventional exact solution. In order to compare with the exact solution method, this paper focuses on problems of a scale that can obtain a solution with appropriate computational time. IBM ILOG CPLEX 12.10 [13] is used to solve each problem. 4.1 Experimental Conditions The experimental conditions are set as follows, after discussions with the collaborating company: • • • • • • •

Number of suppliers (S): 5 Number of factories (M): 2 Number of distribution centers (W): 10 Number of wholesalers (C): 10 Number of planning periods (T): 15 Contract costs between supplier s and factory m (CSsm ): 100 Stockout loss cost at wholesaler c (CLc ): 100 The conditions for the proposed method with scenario sampling are as follows:

• Number of sampling problems generated (N ): 30 • Number of trials of the proposed method: 5 In this experiment, it is assumed that the disruption occurs at the supplier or the factory, and the stoppage periods of the location where the disruption occurs are 1, 3, 5, and 7 periods. Table 1 shows the probability of disruption occurrence at each facility assumed in the proposed method. To ensure that the weights of economic efficiency and stability are equal, the experiment assumes that the probability of occurrence of the no disruption scenario is 0.50 and the sum of the probabilities of occurrence of the scenarios in which some disruption occurs is 0.50. In this experiment, demand is assumed to be given.

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Table 1. Probability of occurrence of each disruption Stoppage period

No

1

3

5

7

Probability

0.50

0.25

0.15

0.075

0.025

We evaluate the resilience of the supply chain network and the risk-aware strategic decisions made using the proposed method by conducting computer experiments. In addition, we compare the solution accuracy and computation time of the proposed scenario sampling-based method with those of the conventional exact solution. 4.2 Experimental Result Table 2 shows the objective function values (COST ) and computational time when the proposed method aims at cost minimization with consideration of disruption risk, and when it aims at cost minimization only. Note that the objective function values for the proposed computational method in Table 2 are the recalculated expected values for all scenarios, not just the sampled scenarios. The results of the proposed computational method show the average (Avg.) and the standard deviation (S.D.). Figure 4 compares the selection of material suppliers for each factory in both cases. Figure 5 compares the inventory levels at each facility. In Fig. 5 the inventory level at factories was higher than at distribution centers because demand from 10 distribution centers is met at two factories. The reason for the difference in inventory levels of materials and products at the factories is that the supply capacity of suppliers is set lower than the production capacity of factories. The reason for the difference in inventory levels among distribution centers is that the network structure is such that factory 1 has a longer transportation distance from distribution center 1 to 10, and factory 2, conversely, has a longer transportation distance from distribution center 10 to 1. As a result, the decision was made to increase inventory levels at distribution centers 4, 5, 6, and 7, which are located far from any of the factories, in consideration of transportation time and cost. As shown in Table 2, the obtained expected the objective function value by the exact solution method increases by 4.8% when disruption risk is taken into account. The computation time for only cost minimization is very short because the solution is obtained only in the no disruption scenario. This increase in the expected value is due to the increase in contract costs due to contracts with multiple material suppliers and the associated increase in transportation costs due to longer transportation distances. Other reasons for the increase are higher inventory costs from raising the steady-state inventory levels, as well as the expected stockout loss costs. As shown in Fig. 4, each factory contracted multiple material suppliers. The selection of suppliers was the same as the exact solution for each trial of the proposed computational method. As shown in Fig. 5, each factory has increased its inventory level of products. When a factory stops production, all downstream facilities are affected, so they increased their inventories of products. Inventory levels varied in each trial of proposed computational method, but were close to this exact solution.

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Objective function value

32580

Computational time (sec.) 0.96

S.D.

34142

34158.20 8.58

90564.32

33981.80 3875.50

Fig. 4. Results of selection of material suppliers

Fig. 5. Results of inventory levels

As for the proposed computational method, Table 2 shows that the average objective function value of the original problem when using the solution of the proposed method reached 99.9% of the exact solution. Therefore, the proposed computational method can extract appropriate scenarios from the sampling space and plan a resilient supply chain network. The reason why the objective function values of the proposed computational method were different for each trial is that the inventory levels differ slightly from one trial to another as described. The computational time of the proposed computational method was reduced by 62.5% from the exact solution method. In other words, the proposed computational method with Latin hypercube sampling contributes to a significant reduction in calculation time through scenario sampling. From the above, it can be

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said that the proposed method reduces computation time and improves computational efficiency while maintaining the optimality of the solution by appropriately reducing scenarios. Next, we evaluate the supply chain network resiliency with contracting multiple material suppliers and increasing inventory levels, taking into account of disruption risks. The proposed method is compared with the deterministic method, which only aims at cost minimization. Since the results of the proposed calculation method and the exact solution are very close, the exact solution was used for the resilience evaluation. We investigate the extent to which the proposed method reduces total expected cost and stockout loss cost compared to the deterministic method. A comparison of the average of total cost and stockout loss cost for each disruption magnitude is shown in Fig. 6 for the target model set at the obtained selection of material suppliers and inventory levels. The total cost for cost minimization with disruption risk under normal circumstances was higher by 1.0% than for cost minimization only. On the other hand, in the situation where disruption risk occurred, the total costs of cost minimization considering the disruption risk were 1.1% for 1-period, 4.5% for 3-period, 8.1% for 5-period and 11.6% for 7-period suspensions lower than the those of cost minimization only, respectively. In addition, by considering the disruption risks, it has been confirmed that the cost of stockout loss was greatly suppressed compared to the case where it is not considered. These results indicate that the proposed method can construct a resilient supply chain network to respond to and recover from disruption risk and maintain a positive steady state operation in an acceptable cost and time.

Fig. 6. Simulation results for each risk (evaluation of resilience)

5 Conclusion This paper proposed a method for selecting material suppliers and determining appropriate inventory levels using two-stage stochastic programming, with the aim of planning a resilient supply chain network. In order to reduce the number of scenarios and shorten the

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computation time with maintaining resilience to disruption risks, a computation method using scenario sampling was also proposed. Computational experiments suggested that the proposed method is capable of planning a supply chain network that is resilient to disruption risk. There are two contributions in this paper. One is to address the risk of disruptions, we considered not only the opening and closing of facilities, but also the holding of inventory, which plays an important role in the supply chain. The other is the use of two-stage stochastic programming and Latin hypercube sampling to achieve both optimal decisions at each stage of the supply chain and computational efficiency. That is because that the design and operational decisions of the supply chain must be considered simultaneously to consider the optimality of operations in a disruption risk environment. This paper focused on a supply chain in which a single object is processed from raw material to finished product and then passed on to the customer. In future studies, in order to bring the problem closer to reality and apply it to real manufacturers, it is necessary to incorporate into the formulation the assembly of multiple parts into a single product, for example. In the future, we plan to use the proposed computational method to further resiliency of the entire network by focusing on the downstream of the supply chain network, including the decision-making process regarding the placement and establishment of distribution centers and the multiple products to be distributed. Acknowledgement. We would like to thank Prof. Nobutada Fujii (Kobe University) and Dr. Ruriko Watanabe (Waseda University) for providing appropriate advices.

References 1. Report on the International Economic Survey Project for the Integrated Domestic and International Economic Growth Strategy (Survey for Strengthening Supply Chains in Asia at Large). Ministry of Economy. https://www.meti.go.jp/meti_lib/report/2020FY/000173.pdf. Last accessed 14 April 2023 2. Wicaksana, A., Ho, W., Talluri, S., Dolgui, A.: A decade of progress in supply chain risk management: risk typology, emerging topics, and research collaborators. Int. J. Prod. Res. 60(24), 7155–7177 (2022) 3. Ho, W., Zheng, T., Yildiz, H., Talluri, S.: Supply chain risk management: a literature review. Int. J. Prod. Res. 53(16), 5031–5069 (2015) 4. Golan, M.S., Jernegan, L.H., Linkov, I.: Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVD-19 pandemic. Environ. Syst. Decis. 40, 222–243 (2020) 5. Ribeiro, J.P., Barbosa-Povoa, A.: Supply chain resilience: definitions and quantitative modeling approaches - a literature review. Comput. Ind. Eng. 115, 109–122 (2018) 6. Kungwalsong, K., Cheng, C.Y., Yuangyai, C., Janjarassuk, U.: Two-stage stochastic program for supply chain network design under facility disruptions. Sustainability 13(5), 2596 (2021) 7. Suryawanshi, P., Dutta, P.: dOptimization models for supply chains under risk, uncertainty, and resilience: a state-of-the-art review and future research directions. Transport. Res. Part E 157, 102553 (2022) 8. Silva, A.C., Marques, C.M., de Sousa, J.P.: A simulation approach for the design of more sustainable and resilient supply chains in the pharmaceutical industry. Sustainability 15(9), 7254 (2023)

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Function-Based Approach for Disaster Relief Logistics in Germany Theresa-Franziska Hinrichsen1(B) , Eduardo Colangelo1 , Martina Schaffer1 Merlit Kirchhöfer2 , and Tobias Spanke2

,

1 Fraunhofer Institute for Manufacturing and Automation IPA, Nobelstr. 12, 70569 Stuttgart,

Germany [email protected] 2 Federal Agency for Technical Relief THW, Provinzialstraße 93, 53127 Bonn, Germany

Abstract. Several times in the last few years, natural disasters have demonstrated the vulnerability and susceptibility of complex societies and organizations to crisis situations caused by interdependences on a global scale. When crises are associated with the occurrence of disasters, disaster relief organizations with a focus on disaster relief management are deployed. These organizations often face different logistical requirements specific to each disaster scenario. Thus, every crisis requires the consideration of its unique variables and factors. Depending on the scenario, a situation may become too complex for the management of logistics to be made using traditional methods. In spite of the increasingly complex requirements, existing approaches for disaster relief logistics are based on empirical knowledge and often designed with a short-term view. Methods to plan and to take preventive measures or to learn from past operations are insufficient. A holistic approach is missing. To close this gap, this paper proposes a holistic function-based approach, complementing current procedures with a logistic concept adapted from the industry. First, the tasks and challenges in disaster relief logistics are presented, before the requirements for logistics are derived. This is followed by a comparison of the requirements in disaster management with the requirements in industrial supply chains. Subsequently, the supply chain functions necessary to manage scenarios in disaster relief logistics are described. Finally, an example is used to further explain the presented functions. Keywords: Supply Chain · Resilience · Logistics · Disaster relief

1 Introduction Most recently, natural disasters revealed that such events can cause major disruptions in sectors affecting social life, healthcare and the economy [1, 2], putting specific emphasis on measures mediating risks coming from severe disasters. An example for such a measure is creating resilient logistics and distribution networks to secure the availability of critical goods (e.g. food resources, medical supplies, industrial goods) in cases of emergency. In the event of a disaster, a rapid response time is crucial [3]. To help quickly © IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 730–743, 2023. https://doi.org/10.1007/978-3-031-43688-8_50

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and effectively in such an event, disaster relief organizations with a focus on disaster management are deployed. This is to minimize the impact on societies and organizations. While such organizations are represented worldwide, this paper focuses on the structures of disaster relief organizations based in Germany. Predominantly, the findings in this paper are based on the structures of the Federal Agency for Technical Relief (abbreviated THW in German), supplemented by findings in literature. From a logistic point of view, THW provides a comprehensive example due the scope and variety of its activities [4]. In order to be able to act efficiently and effectively, a certain degree of resilience is required on the part of the disaster relief organizations. Logistics plays a major role in disaster management, since resources must be provided and distributed to the affected regions. In the last decades the areas of logistics and supply chain management in other fields like the industry had already been strengthened, while further developments in disaster relief organizations lags behind [5, 6]. Reasons for this can be, for example, the organizational structures, but also the low financial scope of such organizations [7]. Today’s approach is mainly based on empirical knowledge, reactive and application-specific approaches and central organized methods. Based on this problem, the following questions are to be answered: “How can the complexity of the logistical tasks of disaster management be supported by a data-driven approach in order to increase resilience?”, “What are the requirements that need to be considered?”, “To what extend is disaster relief logistics comparable with industrial logistics?”, “Which approaches can be adopted from industrial logistics?”. To address the problem, the paper proposes a holistic function-based approach based on industrial logistic approaches. First, the tasks and logistical requirements in disaster management are examined and followed by a comparison with industrial logistics requirements. Subsequently, basic functions are derived and the current status is examined. For this, the THW serves as an example. This procedure is necessary in order to consider the required basic functions for the development of the approach. Thereafter, the approach is presented, before the application of this takes place in an example. The paper is concluded with a summary and an outlook, in which following steps are outlined. 1.1 Disaster Relief in Germany Disaster relief measurements avert dangers in cases of severe disasters [8, 9]. In Germany, the federal and state governments are responsible for protection against major disasters and catastrophes. Civil defense and disaster control form – despite different responsibilities – a so-called “integrated assistance system”. The responsibility depends on the location of the disaster. This means the resources provided by the federal government under civil defense can be used by the states in disaster management just as their own resources and vice versa [9]. In the case of disasters that affect multiple states, such as refugee operations [10], the federal government has additional options for action. Operational implementation occurs at the municipal level, through the support of fulltime and volunteer emergency forces from state and private disaster relief organizations. The emergency forces are assigned to units that are structured according to specialized services, e.g. technical assistance or water rescue [11]. In Germany, the disaster relief organizations that make the greatest contribution to the common good include the fire

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department, the THW and the German Red Cross [12]. In this system, operational planning is characterized by a pure requirement principle, which prohibits the organization from acting proactively, e.g. sending units on its own orders. Disaster relief organizations operate local, national and international. The goal is the protection of human lives, industrial goods and the environment during emergencies. Performing tasks are for example search and rescue missions, flood protection, electricity supply, technical support of infrastructure, drinking water provisioning and logistics support [13]. The broad range of operational areas related to disaster control make specific logistic processes necessary, which depend on the particular characteristics of the organization. 1.2 Phases of Disaster Relief Crises similar to the ones faced in the last years or even more complex, cannot be ruled out in the future. Global refugee movements, climate change and resource scarcity are exemplary developments and events that can lead to challenges for logistics in the context of disaster management. For both, the prevention and management of profound crises, it would be of central importance that signs of a crisis situation are identified at an early stage. The better a development can be recognized, the sooner preventive and reactive measures can be initiated and strategies subsequently developed to increase resilience. For this purpose, it is useful to divide a crisis into three stages: before, during and after a crisis [14, 15]. Figure 1 shows three temporal stages of a crisis situation that can be assigned to the five phases of resilience cycle: prepare, prevent, protect, respond and recover [16].

Fig. 1. Resilience cycle [16]

The two phases prepare and prevent describe the state before the crisis. The prepare phase involves making thorough preparations for a crisis with the aim of reducing underlying risk factors. The prevent phase utilizes these risk reduction measures to avoid some adversities from occurring in the first place. Protect and respond describe events during a crisis. In case of an adverse event, the protect phase includes all measures to ensure

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that physical and virtual protection systems operate flawlessly in order to minimize the negative impacts. The respond phase provides rapid, effective and well-organized disaster relief. The characteristic of this phase is the short reaction time. After a crisis, the recovery phase occurs and deals with recuperating and learning from what has happened [16]. This results in the requirement for a holistic approach that covers all phases and functions from prevention to recovery.

2 Corporate Logistics in Disaster Relief Organizations The term corporate logistics refers to cross-divisional systems thinking. The objects of logistics are e.g. goods, materials, work pieces and information. Technical, informational and business functions serve to fulfill the guiding principle of logistics [17]. The goal of logistics is: (1) to provide the right materials and goods, (2) in the right quantity and (3) with the right quality. This requires planning, design, management and control of the flow of materials, information and values [18, 19]. Corporate logistics can be divided into several areas and sub-processes. These interlock and work together to ensure an efficient and smooth supply chain. As shown in Fig. 2, corporate logistics consist of the following sub-areas: Procurement logistics, production logistics, distribution logistics, disposal logistics and reverse logistics [17, 20].

Fig. 2. Logistics Areas

Compared to profit-driven organizations, disaster relief organizations usually have a different approach to planning and managing material flows. Due to the requirement principle, the mode of operation is to a great extend ad-hoc with a very small timeframe to plan or allocate resources. Logistics-related tasks can range from allocating emergency provisions, over the maintenance of critical infrastructure, to setting up mobile logistics warehouses or emergency shelters [13]. Often, such tasks are time-critical and require goods, equipment and units specific to the crisis scenario. Operations must thus be planned while taking [1] time horizon [2], required resources (e.g. vehicles, technical devices, consumables, other operational resources) [21], tactical units and duration of operations into account [22]. Another challenge is the organization of units on site. This must be done according to the individual circumstances of a disaster. Units of

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THW can for example be coordinated by local sections, national associations or the THW headquarter. At the same time, units consist of volunteers, meaning that personnel capacity is prone to fluctuation. Procurement and distribution logistics with a minimum response time are essential for ensuring security in a disaster situation. The decision to select storage and distribution locations has an impact on the level of service and efficiency. A balance must be found between centralization and decentralization. Centralization can save costs, while decentralization ensures proximity to the customer [23]. The challenge here is the unpredictability of disaster locations. The Covid-19 pandemic showed disaster relief organizations struggled to adequately meet this requirement due to a lack of warehouses. This led to the establishment of additional logistics centers from which resources are brought to the areas of operation [24]. Additionally, mobile warehouses were set up for distribution in the areas of operation. The warehouse structure can thus be compared to that of a distributor. The THW can be used to illustrate this structure. A distinction is made between two main types of warehouses: Logistics centers and mobile warehouses. Figure 3 shows the interactions between authority, THW, supplier and warehouse types:

Fig. 3. THW Warehouse system

Currently, the THW has three logistics centers all over Germany and is responsible for their management. Three more warehouses are in the planning stage. In these centers, decentralized reserves of protective goods are stored, which are promptly available in the event of a disaster or emergency. These include equipment such as devices, machines, work clothes and vehicles. The central location and good access to highways and airports of the logistics centers are decisive for the choice of a location [25]. Mobile warehouses are set up on an as-needed basis in the event of a crisis and are used to distribute relief supplies. The administration of these warehouses is taken over by the federal state or local authorities. The THW only takes over the operational implementation and distribution. The THW does not have any influence on the stocks of goods either. This, of course, complicates the work on site.

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In addition to warehousing and handling facilities, Kaput identified four other supply chain performance drivers from corporate logistics in the context of responsiveness and efficiency: procurement strategy, transportation systems, information and communication systems and stock strategy [23]. In the context of resilience, this consideration is not sufficient. In addition to material flow and information flow, value flow and processes should also be considered. Due to the tasks of disaster relief organizations, the consideration of production aspects is not necessary. Moreover, the requirements for controlling were considered to be more similar to those of a retail company than of a manufacturing one. Based on the structured framework given by corporate logistics, seven relevant main objects can be identified. For these main objects, an analysis of the current state of the relevant functions in disaster relief organizations was carried out (aided by the insight of interviewed THW experts and a literature research). Table 1 shows the result of the analysis. Table 1. State of corporate logistics in disaster relief organizations

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In the area of material flow, the aspects of stocking strategy and trigger reason were examined. Stockpiles have a high relevance, because the stockpiling of suitable relief goods in sufficient quantities enables a fast and demand-oriented supply [23]. • Stockpiling strategy In disaster protection, the stockpiling strategies Assemble-to-Order and Purchaseto-Order are relevant and fully supported. An example of Assemble-to-Order is the “equipment for foreign deployments”. This includes e.g. hydration bladders, various pumps, power generators and hose materials. • Trigger reason Material planning can be oriented based on demand, forecast or consumption. Given the geographical distribution of deployments and the short time frame, disaster relief organizations tend to work in a decentralized manner, with material consumption and demand being documented in several different files. The experience of the planners plays therefore a big role in maintaining the material flow and there is a big potential for profiting of digitalization. The information flow, as part of the information logistics, concentrates on the common IT systems for order processing. Production-related software, such as MES, was neglected due to the scope of this analysis. Information systems are required to make data available as a basis for decision-making and to enable the exchange of information at all levels of the supply chain. Implementation can improve inventory and demand analysis, ensure transparency of goods, information and financial flows and reduce costs [23]. Looking at the supply and availability of tools in disaster relief organizations, it can be shown that such solutions are not widely available. Solutions are mainly developed for specific use cases [26, 27]. • Enterprise Resourcing Planning System (ERP) ERP systems specially designed for disaster relief organizations currently exist in the market [28]. The need for such systems has been recognized by the organizations, having implemented them or planning to do so. • Business Intelligence (BI) Disaster relief organizations will usually employ Key Performance Indicators in an ad hoc manner. This is only due to the already mentioned decentralization of some data but also because of the specific requirements of each deployment. Although not widespread at the moment, the utilization of a proper Business Intelligence strategy (with the corresponding software tool) could help reduce effort in monitoring a situation and analyzing past data (especially integrated with a future ERP system). • Tracking App The “Tracking App” mentioned in the table is an example of proprietary software developments coming from disaster relief organizations (in this case, by the THW) in order to fulfill their specific requirements. This app was created with the intention to improve the traceability of equipment during the management of a crisis. This software component is currently in the prototype phase, hence the current low rating. At the same time, this requires the appropriate hardware as a prerequisite, e.g. smartphones. The distribution of this is already being introduced step by step in disaster relief organizations [29].

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In addition to the material and information flows, the value flow is also relevant. A distinction is made between asset management and controlling. • Asset Management In disaster relief organizations, asset management includes buildings, machines and the vehicles fleet. In this area, the decentralization as well as the great number of assets represent challenges for their management. Activities such as planning and maintenance could greatly profit from a structured and updated information source (such as the mentioned “tracking app”). • Controlling The requirements for controlling differ from those of industrial companies. Disaster relief organizations are usually federal agencies and work therefore as contractors. The requirements for bookkeeping, payment transactions and accounting tend to be fully supported. In addition to the areas of material, information and value flow, the logistic processes were also examined. A distinction was made between logistic core processes and quality. • Core processes Maintenance is a very important process for disaster relief organizations, e.g. the operational readiness of vehicles in the event of a disaster [30]. As mentioned before, this could profit from digitalization and automation (e.g. notifications). Given the current focus in establishing centralized warehouse as well as stocking them preventively, it would be important to adapt warehouse management techniques from the industry (including software tools, usually integrated with ERP systems). This also extends to the related areas of picking and shipping. Procurement is a challenging area for disaster relief organizations, due to the dependence on the federal and state governments [9]. As with the other processes, this area will also profit from the increasing digitalization. Not to be neglected is the reverse logistics process. Keeping track of the whereabouts of materials during deployments, not to mention their disposal or return, is difficult. There is a potential for employing digitalization to facilitate this process, even if precise data collection (a usual problem in the field) is not possible. • Quality The requirements of the functionalities of goods receipt and goods issue inspection are usually fulfilled. Within the logistics centers, mainly quantity checks are carried out on incoming and outgoing goods. Quality checks are performed by the suppliers.

3 Function-Based Model To increase the ability to be prepared for unforeseen events and allocate resources effective, efficiently and fairly during crises, organizations such as THW need to be able to perform their tasks in an organized and synchronized manner. To become resilient, disaster relief organizations need to be committed and continuously plan ahead (as described in the resilience cycle) and consider the possible scenarios. Instead of a structure that encourages ad hoc and centralized decision making, it is necessary to learn from how

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corporate logistics deal with complex supply situation: through specialized areas and processes acting in unison. A constellation of coordinated functions is therefore required, each taking action at the appropriate time. The function-based approach proposed in this work intends to: • identify the functions necessary not only to react during a disaster but also to plan beforehand and early detect the upcoming crisis and the related requirements, • depict how these functions are related based on the time and crisis phase and • identify areas where THW and similar organizations may require software-supported functions (either self-developed or purchased). The designed function-based model describes not only the required functions, but also their assignment to the phases of the resilience cycle and the relationship between them. The presented structure is necessary to ensure that the functions act not only at the required time, but also in a synchronized manner. In addition, the proposed model can serve as a basis for the development of software components capable of handling complexity, e.g. through the use of simulation or machine learning. In Fig. 4, an overview of the proposed functions is provided. From a digitalization point of view, these functions are related on a data level, with the output of some functions being the input for others further in the cycle. From the point of view of corporate logistics, the identified functions should assist in the areas described in Table 1, being in many cases implemented either as an integral part of the mentioned software tools or an additional component (integrated in the IT landscape).

Fig. 4. Functionalities for systematic disaster relief logistics

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In the time before a crisis, it is necessary to generate possible deployment scenarios and to pre-plan the deployment. To achieve this, three functions are required which are to be executed recurrently until the respond phase. • Scenario generation The diversity of operations in disaster protection requires a function to help assess the situation. In the prepare phase, scenario generation is necessary to derive requirements for the ideal pre-planning of possible deployments. • Forecast The forecast function comprises two functionalities: on one side the demand and on the other the availability of the relief supplies, equipment and human resources. Forecasting the demand – taking into account the planned scenarios – includes considering the type of goods as well as the corresponding quantities and dates. On the supply side, the availability forecast needs to take into account a variety of factors, such as replenishment times, stocks and competition on the procurement market. • Deployment planning The function represents the ideal pre-planning for the specific scenarios generated. The resulting plans are different in each scenario and can change during the course of a crisis. In the time during a crisis, four functions are required: Scenario assessment, early warning system, monitoring of crisis development and monitoring of deployment. • Scenario assessment The generated scenarios have to be evaluated in order to make decisions. This is to be done based on the current crisis data and the results obtained from other functions. The assessment can become rather complex depending on the number of factors considered and their interdependencies. • Early warning systems To be prepared for a disaster and to be able to initiate measures, a function is required that can detect deviations from the normal state at an early stage. To identify such deviations, a focus should be placed on monitoring relevant data and their thresholds (e.g. weather data). • Monitoring of crisis development Monitoring of the crisis is required to adapt the measures and the operational plan to the development of the situation. The function must include the collection and processing of data to monitor the crisis considering relevant aspects (e.g. place and duration). • Monitoring of deployment The success of the measures associated with the deployment depends on how well they respond to the disaster. A successful implementation, therefore, needs continuous monitoring. This allows to promptly change the operational plan in order to adjust or drop measures that are not working as desired or to adapt to changes over the development of the crisis. To be prepared for subsequent disasters, after crisis functionalities are required which analyze and learn from what happened.

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• Evaluation This function covers the subsequent evaluation of the results of the functions utilized during the crisis. The precision and reliability of the functions can be assessed, for example, based on the accuracy of the pre-planning as well as the effectiveness and efficiency of the measures taken. As a result, new functions can be created and parameters can be changed.

4 Example To better understand the proposed functions, an example can be utilized. As described in the sections above, there are three main phases to distinguish in a crisis: before, during and after the crisis. Let us consider the scenario of incoming refugees, which has been present over eight years and continues to cause action requirements for THW. Although the increase in the number of refugees is a continuous situation, the flow can be considered as occurring in waves characterized by considerable increases in the influx for a period of time. Before a wave, the scenario generation would serve to identify influencing factors as well as their probability of occurrence. The function would determine that refugee flows in the past eight years have been influenced by an interdependent network of multicausal factors. It would then help to assess, given the probability of certain factors, the scenarios that could occur (e.g. displacement because of drought). The forecast function would then complement the derived scenarios by quantifying the possible refugee movements towards the European Union (EU) and calculating the corresponding needs for resources (supplies, equipment and personnel). Based on these calculations, the deployment planning would proceed to answer operational questions regarding the preparation for the possible scenarios such as “Where will border crossings be taking place?” and “What amounts of refugees could be received by which EU state?” The basis for this assessment is historical deployment data. Additionally, the requirements on the supply side must be determined. An important consideration is how market supply and delivery times might change, especially for highly competitive and essential relief supplies and equipment, e.g. tents. Preventive measures can be taken due to the structure of the disaster response in terms of equipment, such as diversifying suppliers or increasing stock levels. In terms of personnel planning, it is necessary to consider which kind of personnel can be deployed, e.g. volunteers with different language skills. Furthermore, this function could also establish and parametrize early warning systems based on relevant factors to monitor. The occurrence of a wave may be informed by an early warning system. This could monitor factors directly, e.g. border crossings on EU countries or indirectly, e.g. weather conditions, influencing the relevant refugee movements. The scenario assessment would evaluate the occurred scenario, comparing to the results of the scenarios generation to the deployment planning (both functions continue to act in this phase). Subsequently, the deployment planning would act once again, guiding the deployment according to the plans made (which were also continuously adapted as the situation became clearer). The activities include executing procurements and managing the distribution of relief supplies, equipment and personnel. Parallel to the deployment dealing with the refugee wave, the monitoring of the crisis development would observe how

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the characteristics of the situation develop, e.g. fluctuation in the refugee flows. This would be complemented by the monitoring of the deployment, controlling how effective and efficient the deployment takes place. In case of deviations in terms of time, quantity, duration or the location and availability of resources, the measures should be adjusted on a continuous basis. After a wave, the evaluation function would determine how well the functions worked. For example, “How accurate were the forecasts?”, “How helpful were the measures taken?”, “How useful was each functionality?”.

5 Conclusion Recent years have shown that certain crises affect societies and organizations as a whole. If the crises are disasters, disaster relief organizations are deployed to minimize the impact. The tasks of such organizations are manifold and present them with logistical challenges. In order to be able to pursue their tasks and achieve their goals, a quick response time for disaster relief teams plays a significant role, which requires a high level of organization. An analysis of the logistical requirements has shown that they have parallels to industrial logistics, especially to those of contractors. While concepts for industrial logistics have been strongly promoted in recent years, there is a need for action in disaster relief logistics. Existing approaches are based on empirical knowledge and central approaches for individual problems. Due to the complexity, it is necessary to organize the work into functions. This is to achieve an efficient management of operations and also to recognize the requirements of upcoming crises in order to plan preventively. A holistic function-based approach was developed for this purpose. The presented function approach comprises eight functions, organized according to the phases of the resilience cycle: scenario generation, deployment planning, forecast, scenario assessment, early warning system, monitoring of crisis development, monitoring of deployment and evaluation. Examples illustrate the described concepts and functions further.

6 Outlook The content of this paper was collected and conceived during the work in the PAIRS project, founded by the German government. Future research in the field of disaster relief logistics should aim at the implementation of the individual functions. The initial focus might include the individual functions, which are to be designed in such a way that they can handle the complexity of the circumstances in disaster relief operations. Further work address questions related to the organization of disaster relief organizations, e.g. “How should scarce resources be allocated?”, “Which forecasting method is suitable for which resources?”, “What are the specific tasks in individual disaster situations?” At the same time, technical questions arise, e.g. “Which approaches can fulfill the function’s requirements, e.g. machine learning?”, “How can the functions be automated?” The goal should be to design the functions in such a way that they can be applied individually but also work together in an automated manner. Lastly, the aim is to integrate future solutions into the platform for crisis management envisaged in the PAIRS project. The

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challenge here will be data availability, due to insufficient system deployment. To deal with this, scenarios will be formed to reduce complexity and analyze data requirements for this specific use case, e.g. refugee deployment. Subsequently, short-term solutions should be created to capture the data, e.g. Excel-based. Acknowledgement. The research presented in this paper was supported by the German Federal Ministry for Economic Affairs and Climate Action (BMWK) through the “PAIRS – PrivacyAware, Intelligent and Resilient CrisiS Management” project under the grant agreement number 01MK21008C. The authors are responsible for the content of this publication.

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Author Index

A Acerbi, Federica 129, 398 Adelfio, Luca 593 Adlon, Tobias 489 Aghezzaf, El-Houssaine 460 Agrawal, Tarun Kumar 305 Akram, Md Ali 241, 549 Alexopoulos, Kosmas 650 Andersson, Dennis 3 Annunen, Petteri 100 Arana-Landín, German 666 Arena, Simone 426 Aslanidou, Ioanna 159 Assuad, Carla Susana Agudelo B Bar, Eirin Skjøndal 473 Bellgran, Monica 174, 367 Ben Ahmed, Mohamed 620 Birkie, Seyoum Eshetu 174 Bititci, Umit Sezer 115 Boffelli, Albachiara 72 Bohlin, Lotta 174 Bohman, Mikael 174, 367 Bokrantz, Jon 521 Boos, Wolfgang 685 Botta-Genoulaz, Valérie 536 Braun, Greta 521 Bressanelli, Gianmarco 319 Bruch, Jessica 159, 174 Burggräf, Peter 489 Burgio, Alessandro 634 Burow, Kay 335 C Caccamo, Chiara 634 Cannas, Violetta Giada 273 Chari, Arpita 521 Chavez, Zuhara Zemke 174 Chen, Xiaoxia 521 Colangelo, Eduardo 730

Colombo, Beatrice 72 Colombo, Jacopo 72 Costa, Federica 229

D Deike, Lennart 549 Dejene, W. Tirufat 43 Despeisse, Mélanie 3, 129, 521 Drobnjakovic, Milos 504 Ducloux, Malin 305

549

E Egilsson, Nils Ólafur 3 Elvin, Malin 159, 174 Esmaeilian, Sara 473

F Fabris, Riccardo 273 Fang, Qi 3, 521 Ferrati, Francesco 43 Fragapane, Giuseppe 634 Fruggiero, Fabio 215

G García-Alonso, Rakel 666 Ghorbani Saber, Reza 460 González Chávez, Clarissa A.

521

H Haapasalo, Harri 100 Halse, Lise Lillebrygfjeld 256 Hara, Masashi 714 Hauge, Jannicke Baalsrud 305 Hinrichsen, Theresa-Franziska 730 Holthe, Ragnar 241 Hörnlund, Jenny 3 Hribernik, Karl 335

© IFIP International Federation for Information Processing 2023 Published by Springer Nature Switzerland AG 2023 E. Alfnes et al. (Eds.): APMS 2023, IFIP AICT 692, pp. 745–747, 2023. https://doi.org/10.1007/978-3-031-43688-8

746

I Ivanov, Dmitry

Author Index

563, 607

J Jæger, Bjørn 350 Janßen, Jokim 685 Jayalath, Madushan Madhava Jeon, Hyun Woo 29 Jiménez-Redal, Rubén 666 Johansen, Kerstin 144 Johansson, Björn 521 Jonsson, Marie 144

288

K Kaihara, Toshiya 714 Kalaiarasan, Ravi 305 Kazmierczak, Karolina 3 Kim, Alex Yoosuk 16 Kim, Juyoung 16 Kirchhöfer, Merlit 730 Klein, Patrick 335 Klingebiel, Katja 441 Klymenko, Olena 256 Kobayashi, Hibiki 714 Kokuryo, Daisuke 714 Köpman, Juhoantti 100 Kulvatunyou, Boonserm 504 Kurdve, Martin 174 L La Cava, Giuseppe 215 La Scalia, Giada 593 Landeta-Manzano, Beñat 666 Lee, Ga Hyun 29 Leiviskä, Vesa-Matti 100 Leone, Rosanna 593 Lerfall, Jørgen 473 Leyman, Pieter 460 Liao, Zhicheng 549 Lindahl, Emma 174 M Madonna, Alice 72 Magnusson, Filip 367 Majava, Jukka 100 Mancusi, Francesco 215 Mangano, Giulio 536 Marques-McEwan, Melissa 115 Mattsson, Sandra 144

Mavrothalassitis, Panagiotis 650 Miragliotta, Giovanni 319 Miyachi, Yuto 714 Moestam, Lena 3 Molin, Björn 3 Molland, Even 620 Moon, Ilkyeong 16 Mostafayi Darmian, Sobhan 577 Muffatto, Moreno 43 Myrold, Sivert 350 N Nettesheim, Philipp 489 Nguyen, Phu 563 Nikolakis, Nikolaos 650 Nikolov, Ana 504 O Oh, Sewon 16 Olsen, Anna 473 Örtengren, Roland 521 P Panagou, Sotirios 215 Park, Junseok 16 Pasanisi, Davide 398 Perau, Martin 58 Perera, H. Niles 288 Pesenti, Valerio 398 Pinzone, Marta 189 Pirola, Fabiana 412 Portioli-Staudacher, Alberto Pozzi, Rossella 273 Prathapage, Hiran 607

229

R Ranaboldo, Matteo 634 Ratnayake, R. M. Chandima 288 Reh, Daniel 441 Ridella, Matteo 273 Ringen, Geir 241 Romsdal, Anita 473 Roskladka, Nataliia 319 Rotabakk, Bjørn Tore 473 S Saccani, Nicola 319 Sala, Roberto 412 Salzwedel, Jan 489

Author Index

Saporiti, Nicolò 273 Saraceni, Adriana 85 Sariddichainunta, Puchit 714 Sassanelli, Claudio 129 Schaffer, Martina 730 Schjølberg, Per 426 Schröer, Tobias 58, 685, 699 Schuh, Günther 58, 685, 699 Schulz, Bennet 441 Seker, Dogukan 58 Sgarbossa, Fabio 426, 563, 577, 593, 607 Skoogh, Anders 521 Söderberg, Helena 3 Solibakke, Per 200 Sølvsberg, Endre 426 Spanke, Tobias 730 Spiß, Maria 699 Stahre, Johan 521 Steinberg, Fabian 489 Stoll, Oliver 412 T Taisch, Marco 129, 189, 398 Tanaka, Rina 714

747

Theradapuzha Mathew, Ninan 521 Thibbotuwawa, Amila 288 Thoben, Klaus-Dieter 335 Tomasgard, Tore 620 Tschauder, Henning 489 Turanoglu Bekar, Ebru 3, 521 U Urbinati, Andrea

273

V Verpelli, Luca 398 Villa, Simone 72 W Wan, Paul Kengfai 634 Wang, Hao 521 Warmbier, Piotr 382 Welo, Torgeir 577 West, Shaun 412 Wiktorsson, Magnus 305 Z Zielke, Verena

85